esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Gradient-based strategies, such as back-propagation (BP) algorithm [21], were applied to adjust the RBFNN parameters for better network performance. However, such approaches may have limited searching capability to find the global minimum [22]. Unlike the gradient-based strategies, evolutionary algorithms (EAs), such as PSO [23; 24], performed robustly for training RBFNNs due to their global optimization ability [25]. In Reference [23; 24], the basic PSO was used to automatically tune RBFNN parameters for high approximation accuracy. In Reference [26], a nonlinear time-varying evolution particle swarm optimization (NTVE-PSO) was proposed for training RBFNNs by adjusting inertia and learning factors in basic PSO. However, only parameter adjustment of PSO was performed without considering the update mechanism, which may have a direct influence on finding the optimal solution throughout the whole optimization process. To overcome the above drawbacks, we propose an improved PSO to optimize RBFNN parameters for improving network convergence and generalization capability. The obvious novelty of this improved PSO lies in the modifications of both parameter adjustment and update mechanism. As for parameter adjustment, the time-varying learning factors are proposed to balance the exploration and exploitation capability during the optimization process. As for update mechanism, the historical local best information is added to the updating process for better population diversity as the iteration increases. The advantages of the proposed PSO-RBFNN over other RBFNNs exist in better network convergence and prediction accuracy, which are verified by a design case of a planar miniaturized triple-band antenna. This rest of the paper is organized as follows. Section 2 formulates the multi-objective antenna design problem. Section 3 presents the antenna surrogate model based on the improved PSO-RBFNN and Section 4 establishes the fast multi-objective antenna optimization framework by combining the improved PSO-RBFNN with MOEAs. In Section 5, the multi-objective optimization of a compact planar triple-band antenna is presented to demonstrate the effectiveness of the proposed model. Finally, Section 6 concludes the paper.
## 4 Results and Discussion
### Case Study 2
#### 4.2.3 Comparison of RTO with Other Algorithms  
To evaluate the execution of the proposed RTO algorithm, this technique was compared with the other mathematical and evolutionary techniques such as the CGA, FA, CFA, and CPSO technique accessible in the literature, as shown in Table 8. The RTO outmatched the CGA, FA, and CFA given the total net gain of times and gives advantages of 3.95, 4.32, and 2.46 s over these methods, respectively. However, in the case of CPSO, this technique almost converges to the same optimum solution as obtained by using the RTO with more computational effort. Furthermore, the RTO algorithm is better than the other techniques mentioned in the literature in terms of the nature of the results. Convergence representation and less number of iterations is required to get the ideal and best
## 3 Model Construction and Prediction Results  
### Creation of the Experimental Results Database  
As a crucial method for obtaining the mechanical properties of concrete under compressive stress, the uniaxial compression test plays an important role in understanding the mechanical behavior of concrete. At present, the uniaxial compression test of steel fiber reinforced concrete is carried out on a certain scale, but there are differences between the tests in various studies in the literature. In this study, a comprehensive database is compiled based on available uniaxial compression tests of SFRC, incorporating a sufficient amount of data extracted from the literature [8,16-21]. Following general data collection principles, the database is established with full consideration of various experimental influencing factors, including specimen composition, loading devices, and testing environments. A summary of the collected experimental data is presented as follows:  
(1) Material aspect: All specimens use either ordinary Portland cement or Portland cement as the cement material. The aggregate is natural aggregate with good gradation and only one admixture of water reducing agent.  
(2) Loading condition: The compressive strength values are either directly obtained from cube tests or converted from other specimen shapes to equivalent cube strength.  
(3) Environment aspect: The specimens are standard maintenance to minimize the environmental impact as much as possible.  
Each group of data contains the geometric size information of the specimen, the composition information of the concrete, the curing age, and the compressive strength. Based on these parameters, this study selects and combines 14 input features to predict the compressive strength of SFRC. These features include cement content, coarse aggregate content, fly ash content, slag content, silicon powder content, nano-silica content, limestone powder content, sand content, quartz powder content, water content, superplasticizer  
Figure 2: Schematic representation of the SHAP additive interpretation.  
content, steel fiber content, steel fiber diameter, and steel fiber length. The statistical properties of the input features are shown in Table 1.
## 4 Collision Avoidance Algorithm  
The collision avoidance algorithm developed in this study enables the drone to detect and respond to nearby obstacles in real time by modifying its control signals. The approach combines distance data from multiple Time-of-Flight (ToF) distance sensors and computes virtual repulsive forces to guide the drone safely through cluttered indoor environments. The core concept is to maintain smooth and responsive manual flight while automatically overriding user inputs when potential collisions are detected.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 4 Formation Damage Diagnosis and Technology Development Direction Prediction
### Investing in Oil and Gas Formation Damage Prediction and Diagnosis Expert System  
Protection of oil and gas formations should be based on prevention and a combination of prevention and treatment. Both the formation damage mechanism and protection technology are highly targeted. However, to establish a reliable formation protection technology, a good understanding of the formation damage mechanism is required. Unfortunately, the formation damage mechanism and best protection measures vary between different regions and formation types. In the future, it is necessary to make full use of geological, engineering, and production information to establish intelligent prediction and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
diagnosis methods for oil and gas formation damage based on big data analysis methods. Those could form an expert system for formation damage prediction and diagnosis and use existing information and data on the formation to quickly, accurately, efficiently, and in real time, evaluate and diagnose the effectiveness of formation protection technology. This would allow determination of a formation protection plan and adjust the formation protection measures over time, in order to improve the formation protection effect and lay the foundation for the efficient implementation of formation protection technology.
## 3 Results
### Experimental Results of FK-SVM, HIST-SVM and PLSA-SVM
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Round** & **HIST-SVM** & **PLSA-SVM** & **FK-SVM** \\ \hline
30\% & 68.93\% & 67.37\% & 68.71\% \\
40\% & 71.46\% & 71.43\% & 77.37\% \\
50\% & 72.77\% & 75.86\% & 84.42\% \\
60\% & 74.92\% & 79.75\% & 86.25\% \\
70\% & 75.56\% & 81.24\% & 88.04\% \\ Average & 72.73\% & 75.13\% & 80.96\% \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline
**(a)** & & & \\ \hline
30\% & 68.72\% & 63.38\% & 70.47\% \\
40\% & 71.33\% & 71.61\% & 74.63\% \\
50\% & 70.86\% & 76.83\% & 81.25\% \\
60\% & 73.39\% & 81.22\% & 84.59\% \\
70\% & 74.03\% & 83.63\% & 87.45\% \\ Average & 71.67\% & 75.33\% & 79.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample comparison of different training percentages of FK-SVM, HIST-SVM and PLSA-SVM. (a) Precision. (b) Recall rate. Figure 2: The multi round compared experimental results of FK-SVM, HIST-SVM and PLSA-SVM. (**a**) Precision. (**b**) Recall rate. FK-SVM method can map low-dimensional linear non-separable data to high-dimensional space and make it linear separable; Even more, with more advantages in mining latent topics of text, especially latent sentiment topics, it is less disturbed by specific emotional words and irregular vocabulary usage. Therefore, it performs better in text emotional classification than HIST-SVM method. Comparison between FK-SVM and PLSA-SVM:  
The experiment demonstrates that in the 5 rounds of experiments, the accuracy and recall rate fluctuate within a small range, with a relatively stable overall result, indicating that both methods have a good effect on text sentiment classification. In contrast, a slightly higher accuracy can be achieved by using the FK-SVM proposed in this paper than the PLSA-SVM method, whether in the average or the comparison test each time, because the main difference between the two methods lies in Fisher kernel function in the support vector machine. Therefore, it can be concluded that the Fisher kernel function based on PLSA plays a decisive role in these experiments. Besides, this method can map non-linear data from low-dimensional space to high-dimensional space through the kernel function. When deriving the Fisher kernel function based on the PLSA model, the probability distribution information of the data, and the high-level semantic information of the PLSA, especially, the statistical information of the text itself, are taken into account. This allows the support vector machine based on Fisher kernel function to take the probabilistic information of text units and the latent emotional topics of text as classification features in text classification, thus improving the effect of classification.
## 5 Results and Discussion
### Incidence Angle Normalization  
Looking at the average backscattering signals for each orbit exemplarily over the Hur catchment, there is a clear trend visible of decreasing backscattering signals due to increasing incidence angles (Table 3, Figure 6). The normalized signals do not show such a trend for the respective orbits. The incidence angle normalization reduces the range of observed median backscattering intensities related to changing incidence angles by approximately 70% for the co-polarized signal. Nevertheless, the probability distribution function shows that the different distributions of backscattering values, depending on the incidence angle, are still present. While the orbits 88, 37 and 139 show a stronger bimodal distribution in co-polarization, orbit 15 has a more down-skewed distribution. In this regard, the influence of both incidence angle as well as ascending and descending orbit is still present and is similar to previous studies [71,86]. As the influence depends on surface and vegetation parameters, e.g., surface roughness, vegetation biomass, canopy height, plant geometry and phenology, a crop specific normalization could improve the results. Nevertheless, the results imply further processing steps to level out the different backscattering signals for a short-term change detection approach.
## 4 Discussion
### Study on Red Fruits and the Implementation of Cutting-Edge Edible Coating
#### 4.3.1 Edible Coatings and Their Operational Principles  
An edible coating is a thin layer made from edible substances and applied to food items to extend their shelf life [113; 114]. Currently, there is a growing interest in edible coatings and films compared to other synthetic plastic packaging materials, largely since they are biodegradable and can be consumed. They are also compatible with food [115; 116; 117]. Edible coatings are often derived from biodegradable polymers as a substitute for nondegradable packaging materials that generate significant waste [107; 118]. Edible films and coatings are a viable technological solution for preserving food quality, ensuring safety, and enhancing usefulness [10]. To be considered suitable, the materials must fulfil certain criteria, such as exhibiting favorable sensory characteristics, possessing effective barrier qualities, demonstrating sufficient mechanical resilience, maintaining biochemical, physicochemical, and microbiological stability, ensuring safety for human consumption, being environmentally friendly, and being reasonably priced [10]. Edible coatings are produced using natural raw ingredients, including polysaccharides, proteins, lipids, and their mixtures. In addition to their ability to preserve food, edible coatings are designed with chemicals, such as antioxidants, flavorings, and sweeteners, to achieve active packaging and improve the nutritional and sensory characteristics of the food [119; 120].  
Edible coatings and films can maintain the visual appeal, firmness, moisture content, and can extend the storage duration of fruits. This is due to their ability to act as barriers against moisture and gas transmission, prevent lipid oxidation, regulate enzymatic activities, and inhibit microbial spoilage [10; 112; 121]. Edible coatings alter the surrounding environment of fruits by modifying the gas composition inside the fruit, thus slowing down the respiration rate and ethylene generation. This effectively reduces the physiological degradation of fruits [4]. Moreover, it has been shown that edible coatings including antimicrobial substances, such as organic acids, plant essential oils, and polypeptides, effectively hinder the proliferation of microbes [122]. Edible coatings can decrease enzyme activity, minimize the occurrence of browning reactions, and prevent texture softening [10]. Furthermore, it has the capacity to preserve the inherent volatile flavor and color components [123]. The edible coatings shield food products from microbial contaminants, diminish the effects of deterioration [124], extend the storage duration [125; 126], and minimize lipid oxidation and moisture loss in food products [10; 127; 128].
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 6 Conclusions  
In this paper, the idea of drive-by railway track monitoring is investigated while using field measurements. An operational train from the Irish Rail fleet is instrumented using accelerometers installed on the train bogie. The train location and forward velocity are recorded while using a GPS system. A data set including acceleration and train velocity measurements from 60 passes on the Dublin-Belfast line is studied. The energy of the signals is extracted from the amplitudes of the accelerations using a Hilbert transform and considered as a track condition indicator. It is shown that, when the train has a forward speed less than 50 km/h, the energy indicator is no longer reliable. The data set is cleaned by removing these low speed passes for each 1 km data segment and the remaining passes are scaled using the train forward speed. It is demonstrated that the scaled amplitudes provide good repeatability for multiple measurements and they show considerable potential for drive-by track monitoring. The scaled amplitudes for a 6 km dataset are compared with TRV measurements. This confirms that the locations with high energy levels correspond to parts of the track that exhibit faults in the TRV data.  
Conceptualization, A.M. and P.Q.; methodology, A.M.; formal analysis and investigation, A.M and P.Q.; writing--original draft preparation, A.M. and E.OB.; writing--review and editing, A.M., E.OB. and P.Q.; supervision, E.OB. and C.B.  
The project has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement number 607524. The authors are thankful for this support. The authors wish to acknowledge the financial support received from Science Foundation Ireland under the US-Ireland Research Partnership Scheme. This research also was funded by the 'Irish Research Council New Foundations'.  
The authors declare no conflict of interest.
## 4 Simulation and Analysis
### Simulation on Network-Wide Neighbor Discovery  
In order to further validate the neighbor discovery in the FANET, we conducted simulations in a network consisting of 100 randomly deployed UAVs within a 200 m \(\times\) 200 m square area. The transmission range of UAVs varied from 25 m to 125 m, covering a wide range of practical scenarios. The transmission range and the number of sectors for each UAV in the FANET are the same.  
We traced the worst-case and the average discovery delay of the BR-DA-FANET protocol and the ODND protocol. The worst-case discovery delay represents the maximum delay observed among 10,000 runs, while the average discovery delay is the average value over the same number of runs.  
Figure 27 presents the worst-case discovery delay for different transmission ranges and different numbers of sectors. It clearly demonstrates that the worst-case discovery delay of the BR-DA-FANET protocol outperforms that of the ODND protocol. As the transmission range increases, the number of neighbors for each UAV also increases, resulting in an increase in the probability of worst-case delay.  
The average discovery delay under different transmission ranges as well as different numbers of sectors is shown in Figure 28. Figure 28 shows that the average discovery delay of the BR-DA-FANET protocol is less than that of the ODND protocol. With increasing transmission range, the average delay is increasing, and the explanation is the same as the worst-case delay.  
Figure 28: The average discovery delay under different transmission ranges and different numbers of sectors.  
Figure 27: The worst-case discovery delay under different transmission ranges and different numbers of sectors.
## 4 Results and Discussion
According to 2025 data, the price of fuel oil is 740 Euros per ton, resulting in a fuel cost of 5920 Euros for each start-up. When an additional 10% is considered for operational expenses, the total cost of a boiler start-up reaches 6512 Euros. For this boiler, which is started up approximately 20 times per year, the annual start-up cost amounts to around 130,000 Euros. In addition, based on the last 10 years of operational data, the average annual fuel-oil cost attributed to burners used for flame stabilization during operation, due to equipment failures and variations in coal properties, is estimated at 200,000 Euros. Consequently, the total annual fuel-oil expenditure reaches approximately 330,000 Euros. It is estimated that four of the newly developed microwave-assisted auxiliary burners would be sufficient to start up the boiler. The total cost of this system is calculated to be 720,000 Euros, with a projected payback period of approximately 26 months. In comparison, when the same application is implemented using arc plasma technology, the payback period is found to range between 16 and 24 months [17; 26]. Moreover, the magnetron head within the microwave generator has an operational lifespan of approximately 10,000 h [25], which is significantly longer than the electrodes used in arc plasma burners, typically requiring replacement after only 400-500 h of operation [18; 27]. This indicatesa considerable advantage of the microwave-based technology in terms of durability and maintenance requirements. Finally, during the start-up of coal-fired thermal power plant boilers, auxiliary equipment such as flue gas treatment systems and electrostatic precipitators are by-passed until coal ignition begins in the boiler, to prevent any damage from the smoke generated by fuels like fuel oil. With the newly developed microwave-assisted auxiliary burner, coal will burn efficiently within a stable plasma flame, ensuring that the flue gas treatment systems and electrostatic precipitators will be operational from the start-up of the boiler. This will minimize environmental impact and allow the electricity generation process to begin smoothly.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## Glossary  
\begin{tabular}{p{42.7 pt} p{142.3 pt} p{142.3 pt}}
**Abbreviation** & **Full Name** & **Description** \\ BM & Birnbaum measure & Quantifies the importance of a component in preventing system failure. \\ BN & Bayesian network & A graphical representation of variable dependencies for probabilistic reasoning. \\ BT & Bow-tie & A visual risk-management map linking top events to their causes and consequences. \\ BTA & Bow-tie analysis & An analysis technique that identifies, assesses, and mitigates risks through a bow-tie diagram. \\ CE & Critical event & An event with a major impact on the safety, performance, or reliability of a system. \\ CPT & Conditional probability table & Specifies conditional distribution of a variable based on other variables' values in a Bayesian network. \\ ETA & Event tree analysis & Identifies and evaluates event sequences leading to specific outcomes in risk assessment. \\ FST & Fuzzy set theory & A mathematical framework for representing partial set membership, capturing degrees of truth. \\ FTA & Fault tree analysis & A top-down analysis of component failures that can lead to system failure. \\ GDS & Gas distribution station & A facility for the safe and stable distribution of natural gas to long-distance pipeline users. \\ ID & Importance degree & A measure of the significance of a component or event to overall performance or safety. \\ IE & Intermediate event & An event that occurs between a root event and an outcome event in a Bayesian network analysis. \\ OE & Outcome event & A node in a Bayesian network signifying the system outcome based on the event sequence. \\ PRA & Probabilistic risk assessment & A risk assessment method that quantifies likelihood and consequences to aid decision-making. \\ QRA & Quantitative risk assessment & Quantifies risk by estimating the probabilities and impacts of potential hazards. \\ RAD & Relative agreement degree & A measure of consensus among individuals or groups on a specific issue, decision, or assessment. \\ RE & Root event & The initiating or fundamental event in a Bayesian network, from which other events are derived. \\ RoV & Ratio of variation & A metric assessing REs' significance in GDS by comparing pre- and post-RE probability shifts. \\ RRW & Risk reduction worth & A measure ranking REs by criticality and risk impact significance. \\ SB & Safety barrier & A measure mitigating or preventing undesired events and hazards. \\ SBFE & Safety barrier failure event & A safety barrier malfunction that may elevate risk or cause unintended consequences. \\ SCE & Spatial confinement event & Gas accumulation in confined spaces due to environmental constraints. \\ UE & Undesired event & An event that is not intended or desired, often leading to harm, loss, or negative consequences. \\ \end{tabular}