esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 5 Conclusions  
Aiming at the problems that the incipient fault characteristics are weak, and the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, a nonlinear dynamic incipient fault detection method based on Deep ADKPCA was proposed. This method combines the advantages of Deep PCA and moving window algorithm, effectively captures the noise information by capturing the timing relationship between variables, fully mines the fault information in the signal, and improves the incipient detection capability of the fault. The proposed method uses the concept of kernel function, constructs a dynamic non-linear principal component model, and has the ability to handle non-linear and non-stationary signals. Compared with the fault detection results of the ADKPCA, Deep KPCA, and Deep DKPCA methods, the fault detection rate of the Deep ADKPCA method proposed in this paper was more than 85%, the detection delay was almost zero, and the ability to detect incipient faults was significantly improved. It was proved that Deep ADKPCA incipient fault detection method had a certain feasibility. The application of this method in other fields such as motors and rotors needs further research.  
**Author Contributions:** Conceptualization, H.S. and X.B.; software, J.G.; validation, J.G., X.B., and L.G.; formal analysis, H.S., X.B., and Z.L.; resources, H.S., X.B., and J.S.; data curation, L.G.; writing--original draft preparation, J.G.; writing--review and editing, H.S. and X.B.; supervision, H.S. and X.B. All authors have read and agreed to the published version of the manuscript.  
Figure 12: (a) The fault detection rate of the rolling bearing rolling body fault and (b) the detection delay of the rolling bearing outer rolling body fault.  
**Acknowledgments:** This research was funded by National Science Foundation of China (Nos: 51705341, 51905357, 51675353), Natural Science Foundation of Liaoning Province (No: 2019-ZD-0654).  
**Conflicts of Interest:** The authors declare no conflict of interest
## 2 Results
### The Self-Assembly Approach
#### 2.1.2 Selection of Crosslinker  
XL makes up about 80% of a MIP formulation and potentially has the greatest influence on non-specific binding in MIPs. Thus, to further our molecular modelling and NMR studies, the affinity of **1** to EGDMA, TRIM and DVB polymers was measured. These crosslinked polymers were prepared in the absence of **1** and any FM in acetonitrile (AN) and CHCl\({}_{3}\), the chosen porogens for **1**-MIPs. Although **14** has been successfully used both as a functional monomer and crosslinker in OmniMIPs [49] and our MM and NMR data suggest that it could potentially work as a crosslinker (though not as a functional monomer) for **1**-MIP, we excluded **14** in this study. High levels of non-specific binding of **1** were observed in DVB polymers, two to four times higher than in EDGMA and TRIM polymers. Of the three crosslinkers, only DVB is capable of \(\pi\)-\(\pi\) stacking interaction with **1**, and this is most likely the reason for the observed high level of superficial binding of **1**.
## 3 Experimental Results and Analysis
### Strength and Deformation Properties
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **Length** & **Thickness** & **Width** & **Density** & **\(\sigma_{\text{P}}\) (MPa)** & \(\varepsilon_{\text{P}}\) (\%\%)** & **E (GPa)** \\
**No.** & **(mm)** & **(mm)** & **(mm)** & **(kg-m\({}^{3}\))** & **\(\sigma_{\text{P}}\) (MPa)** & \(\varepsilon_{\text{P}}\) (\%\%)** & **E (GPa)** \\ \hline C-1 & 150.1 & 25.0 & 100.1 & 2391.4 & 100.9 & 5.57 & 21.63 \\ C-2 & 150.5 & 24.9 & 99.6 & 2427.5 & 105.6 & 5.96 & 21.63 \\ C-3 & 150.7 & 24.8 & 100.7 & 2385.5 & 101.3 & 6.97 & 19.07 \\ S-1 & 150.3 & 25.5 & 100.4 & 2462.2 & 89.22 & 6.08 & 16.90 \\ S-2 & 151.0 & 24.7 & 100.8 & 2415.4 & 78.50 & 6.01 & 14.95 \\ S-3 & 151.1 & 24.9 & 100.8 & 2404.6 & 77.95 & 6.23 & 14.95 \\ H-1 & 150.3 & 25.1 & 100.1 & 2407.1 & 82.44 & 5.17 & 18.36 \\ H-2 & 150.3 & 25.1 & 99.9 & 2390.3 & 74.73 & 5.22 & 17.46 \\ H-3 & 150.4 & 25.1 & 99.9 & 2362.7 & 77.28 & 5.22 & 17.32 \\ D-1 & 150.6 & 25.2 & 99.8 & 2370.2 & 68.74 & 5.06 & 15.63 \\ D-2 & 150.8 & 25.2 & 99.8 & 2380.2 & 70.44 & 4.93 & 16.76 \\ D-3 & 150.4 & 25.1 & 99.9 & 2362.7 & 77.28 & 5.22 & 17.32 \\ V-1 & 149.9 & 25.0 & 99.3 & 2360.7 & 87.01 & 5.88 & 18.06 \\ V-2 & 150.4 & 25.1 & 99.5 & 2358.2 & 83.35 & 5.43 & 17.37 \\ V-3 & 150.5 & 24.9 & 99.7 & 2401.5 & 88.82 & 5.77 & 17.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specific sizes, strength and deformation parameters of rock samples. Figure 4: Mechanical behavior of different groups of sandstone samples under uniaxial loading. Figure 4b illustrates the average mechanical parameter values of different groups of samples. It was found that the mechanical properties of the samples with openings, including the \(\sigma_{\rm p}\), \(\varepsilon_{\rm p}\) and \(E\), were much lower than those of the intact samples, and the degree of weakening was closely associated with the configuration of openings. For the uniaxial compressive strength, the reduction rate ranged from 15.80% to 29.94%, and the values of these groups could be ordered from large to small as: C (102.60 MPa) \(>\) V (86.39 MPa) \(>\) S (81.89 MPa) \(>\) H (78.15 MPa) \(>\) D (71.88 MPa). Interestingly, observations demonstrate that the average strength of Group V was even higher than that of Group S. This behavior was attributed to different stress distributions round the openings, which would be detailedly illustrated in the numerical study section. In terms of the Young's modulus, the average value of Group C was the largest, followed by Groups H, V, D and S, with a decrease extent of 14.76%-24.93%. Besides, compared with the intact rock samples, the peak strains of the holed samples were also reduced to varying degrees. Among them, Group S had the largest peak strain (6.11%), while the strain of Group D was the smallest. In conclusion, both the quantity and configuration of the openings exerted a significant influence on the strength and deformation properties.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### Comparative Analysis
#### 4.2.1 Finance  
In the finance sector, where accurate risk assessment and regulatory compliance are critical for decision-making, the choice of suitable XAI frameworks holds immense importance. Among the notable frameworks, SHAPs, Explain-IT, and Integrated Gradients stand out for their tailored solutions to the unique challenges encountered in financial analysis. SHAPs' capability to elucidate the contribution of individual features to predictions aligns perfectly with the needs of risk assessment, offering clarity on the factors influencing portfolio performance and financial risk. The DLIME's speed and scalability are particularly advantageous for managing extensive and dynamic financial datasets, ensuring efficient interpretation without compromising accuracy. Meanwhile, Integrated Gradients' visualization of feature attributions aids in understanding the underlying logic behind model predictions, facilitating transparent decision-making processes.  
Extensive research on AI applications in finance [72; 73; 74] underscores the growing significance of technological advancements in financial technology (FinTech). While [75] provides a comprehensive overview of the AI solutions in finance, the analysis in [73] identifies thematic clusters, and [74] explores financial intelligence, highlighting its role as the third stage of technological advancement in finance. These studies collectively emphasize the importance of XAI in addressing the challenges related to risk management, stock market analysis, portfolio optimization, anti-money laundering, and electronic financial transaction classification within the finance sector. Each of these frameworks, with its distinct methodologies and strengths, empowers financial analysts and institutions with actionable insights, facilitating informed decision-making and regulatory compliance [76].
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 4 Emulation Results and Discussion  
For convenience we list all simulation parameters in Table 1. We assume a moderate number of satellites in the constellation and a relatively lower constellation altitude to emphasize the impact of satellite constellation. LoRa data rate 3 (DR3) parameter is utilized to provide a realistic emulation, in which the frequency bandwidth is 125 kHz. The transmit power level and the duty cycle follows the specification of LoRa Alliance. Moreover, we adopt the excess path-loss parameters provided in [47].
# Ray Tracing Method of Gradient Refractive Index Medium Based on Refractive Index Step
###### Abstract  
For gradient refractive index media with large refractive index gradients, traditional ray tracing methods based on refined elements or spatial geometric steps have problems such as low tracing accuracy and efficiency. The ray tracing method based on refractive index steps proposed in this paper can effectively solve this problem. This method uses the refractive index step to replace the spatial geometric step. The starting point and the end point of each ray tracing step are on the constant refractive-index surfaces. It avoids the problem that the traditional tracing method cannot adapt to the area of sudden change in the refractive index and the area where the refractive index changes sharply. Therefore, a suitable distance can be performed in the iterative process. It can achieve high-efficiency and precise ray tracing in areas whether the refractive index changes slowly or sharply. According to the comparison of calculation examples, this method can achieve a tracing accuracy of \(10^{-5}\) mm. The speed and precision of ray tracing are better than traditional methods.  
gradient index media 1; ray tracing 2; refractive index step 3; constant-index surface 4; law of refraction 5
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects  
A 5G-satellite integrated network requires accurate modelling of signal propagation effects across large distances, incorporating environmental variability between satellites and terrestrial receivers. Traditional models like free-space path loss (FSPL), the two-ray model, and Rician fading provide a foundation but lack the complexity needed to address factors such as atmospheric and rain attenuation, which are critical in high-frequency 5G and satellite communications. In this enhanced model, we integrate these environmental elements to develop a comprehensive framework for 5G-satellite IoT applications, enabling a robust analysis of real-world signal dynamics.
# Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
## 5 Conclusions  
This study focuses on optimizing beam tracking for Reconfigurable Intelligent Surfaces. To address the system latency issues caused by multiple TCP feedback exchanges and exhaustive codebook traversal in traditional RIS systems, two optimization mechanisms were proposed and implemented. The proposed EIR-CM and VSR-CM architectures provide two  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Tracking Scheme**} & \multicolumn{2}{l}{**Traditional Multi-Host**} & \multicolumn{2}{l}{**Edge-Integrated RIS Control**} & \multicolumn{1}{l}{**Visually Steered RIS**} \\  & **Scheme**[28] & **Mechanism** & **Control Mechanism** \\ \hline Tracking Latency & 3–4 s & 1–2 s & \(<\)0.5 s \\ Synchronization & Required & No Required & No Required \\ Feedback & Required & Required & No Required \\ Codebook Traversal & Traverse All & Traverse All & Directly Select Target \\ System Complexity & Multi-Host & Integrated Host & Codebook \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of tracking performance across different RIS architectures.  
Figure 8: Experimental results with the VSR-CM system. (**a**) RIS beam adjustment for target user on the left side of camera. (**b**) Signal distortion due to user absence or blocked by wave-absorbing materials. (**c**) Continuous RIS beam tracking for user movement to the right side of the camera.  
complementary approaches for future 6G wireless networks with an integrated RIS: The EIR-CM provides a practical framework for real-world RIS deployment by reducing control latency and enabling decentralized RIS management. The VSR-CM eliminates the need for traditional CSI feedback, providing a fast, AI-driven approach to dynamic beam tracking and user-centric RIS optimization. These architectures reduce network signaling overhead and improve spectral and energy efficiency in dense urban environments. They enable multi-RIS co-optimization, leveraging edge computing and AI-based visual sensing to dynamically tune multiple RIS elements, and facilitate seamless integration of the RIS with next-generation wireless infrastructures for V2X, IoT, drone networks, and 6G ultra-reliable low-latency communications applications.  
Conceptualization, Z.W. and Y.N.; methodology, Z.W. and Y.N.; software, Z.W. and Y.N.; validation, Z.W. and Y.N.; writing--original draft preparation, Z.W.; writing--review and editing, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors are grateful to [PERSON] and [PERSON] from Nanyang Technological University for their valuable discussions and support throughout this work.  
The authors declare that there are no conflicts of interest.
# Review of Web Mapping: Eras, Trends and Directions
## 4 Trends and Directions in Web Mapping
### Directions in Web Mapping: Data, Users and Functionality
#### 4.2.1 Directions for Web Mapping Data  
The amount of data and information on the web is continuing to proliferate with sources including aerial and satellite imagery, crowdsourced data, online databases, sensors, data feeds from Internet-enabled objects in the Internet of Things (IoT) and online social interactions [138; 139; 140]. By 2025, there is expected to be 27 billion connected devices on the Internet with one-third critically dependent on geolocation data and 60% potentially including geospatial data in applications [141].  
Once collected, all that data have to be made available and contextualized for extracting information and then knowledge. If the Internet and the cloud could be the glue for sharing, many steps have to be considered to make this availability efficient and effective. The heterogeneous data has to be modeled, structured, and indexed. Mechanisms for discovering and mining them have to be implemented or enriched. The additional benefits of the availability of such diverse and different sources of data, as well as understanding their quality and limitations, is crucial in promoting their integration including the capacity of dealing with both syntactic and semantic procedures, based on ontologies and workflows (refer to Section 4.2.3 for more on this).  
Data will be so important in the next years that \"Organizations that do not use data intelligently, will not survive\" [90]. That is the reason why a relatively new professional, the data scientist, is a key player in making mashups of multiple analytical processes to make sense of petabytes of big data [142]. Web mapping will continue to be a strong component in data science, since location (space/time) is a fundamental key for contextualizing events and visualization will remain one of the strongest tools for interpreting and linking data, information, knowledge and intelligence.
## 5 Conclusions  
In this paper, simulations and experimental studies are reported to verify the influence of the active air intake system on the beam quality in a windowless beam expanding system. Through simulation, we found that the distribution of the inlet holes in the intake system has a great influence on the system's wavefront aberration. We observed that by increasing the number of rows and the number of holes per row for inlet holes, the internal flow field of the beam expanding system can be simplified. Finally, it was found that an air intake layout with 16 rows and 360 holes per row had the optimal effect for the beam expanding system of the 850 mm optical telescope when the mass flow rate was 100 g per second. The RMS of wavefront aberration in this state is only 0.08 \(\upmu\)m, which is acceptable in engineering practice.  
In this paper, the method of flow field simulation combined with ray tracing is well applied to the simulation of aero-optical effects. The concept of differentiation is applied to divide the ray transmission path after the flow field simulation to avoid fitting the refractive index field with complex formulas. To obtain more accurate results, the Barron gradient operator instead of the surface normal of each element is used to calculate the refractive index gradient. After calculating the direction of the refractive index gradient, [PERSON]'s law is directly used to calculate the light deflection to determine the direction of the light emitted from the surface of each element. Compared with other traditional methods, this algorithm is simpler, more accurate, and easier to implement. By comparing the simulation and experimental results, it is obvious that the simulation process can reflect the real situation precisely. Thus, the simulation can be used in future design work for a beam expanding system.  
Figure 12: Wind speed profiles of experiment.  
Figure 13: Wind speed profiles of simulation.  
**Author Contributions**: Conceptualization G.R. and Y.T.; methodology, Q.Z.; software, Q.Z.; validation, Y.T., T.T. and G.R.; formal analysis, Q.Z.; investigation, Q.Z.; resources, T.T.; data curation, Q.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, Y.T.; visualization, Q.Z.; supervision, Y.T.; project administration, Y.T.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by Open Research Fund of State Key Laboratory of Pulsed Power Laser Technology (SKL2018 KF05), Excellent Youth Foundation of Sichuan Scientific Committee (2019 JDJQ0012), Youth Innovation Promotion Association, CAS (2018411), CAS \"Light of West China\" Program and Young Talents of Sichuan Thousand People Program.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
###### Abstract  
This research addresses the critical need for an efficient and precise identification of _Capsicum_ spp. fruit varieties within the post-harvest contexts to enhance quality control and ensure consumer satisfaction. Employing the YOLOv8m convolutional neural network, the study identified eight distinct pepper varieties: Pimento, Bode, Cambuci, Chilli, Fidalga, Habanero, Jalapeno, and Scotch Bonnet. A dataset comprising 1476 annotated images was utilized and significantly expanded through data augmentation techniques, including rotation, flipping, and contrast adjustments. Comparative analysis reveals that training with the augmented dataset yielded significant improvements across key performance indicators, particularly in box precision, recall, and mean average precision (mAP50 and mAP95), underscoring the effectiveness of data augmentation. These findings underscore the considerable potential of CNNs to advance the AgriFood sector through increased automation and efficiency. While acknowledging the constraints of a controlled image dataset, subsequent research should prioritize expanding the dataset and conducting real-world testing to confirm the model's robustness across various environmental factors. This study contributes to the field by illustrating the application of deep learning methodologies to enhance agricultural productivity and inform decision-making.  
Graduate Program in Production Engineering, Universidade Paulista, Rus Dr Bacelar 1212,  
Sio Paulo 04026-002, SP, Brazil; [EMAIL_ADDRESS] (M.d.O.B.);  
[EMAIL_ADDRESS] (F.P.L.A.); [EMAIL_ADDRESS] (S.d.S.S.);  
[EMAIL_ADDRESS] (L.d.S.C.); [EMAIL_ADDRESS]  
repper classification; YOLOv8m; computer vision in food supply; deep learning; object detection
## 6 Conclusions  
The simulation results show that the type of distribution and the variability of demand, measured by standard deviation, have a significant influence. Thus, the authors' conclusion is that the models developed may give more accurate results and that the \"Square Root Formula\" should be modified taking into account the characteristics of the demand. However, when the excess inventories resulting from inaccurate demand forecasts were included (100% for nine warehouses and 15 for one central warehouse), the results obtained with both methods (simulation and optimization) were similar for all variants. It can therefore be concluded that the classical formula of the \"Square Root\" was probably developed for such a (specific) situation. Actually, a negative consequence of decentralization of storage may be an excessive level of inventories. It is easier to forecast demand at the level of a central warehouse serving a larger market than for local markets, as evidenced by case studies of companies that have implemented such a strategy.  
Finally, after completing the regression models with cost components, the total costs were calculated on selected cases of product groups (food, electronics, clothes) and the number of warehouses with the lowest total costs was found.  
The models developed by the authors (simulation and regression) and simulations and calculations carried out with their use allow the identification of several interesting relationships. Optimizing the size of a distribution network can actually have a significant impact on a company's profitability. Cost savings ranged from \(-24.3\%\) to \(-37.5\%\) for Gaussian distributions and were even greater for Gamma, from \(-33.7\%\) to \(-59.3\%\). However, as it turned out, centralization may not necessarily be the optimal strategy for the most expensive goods (high inventory maintenance costs and lost sales). Loading parameters are also important, as they have a significant impact on the costs of storage and, above all, transport.  
The results of the simulations proved that it is profitable to conduct research on methods of optimization of the distribution network. However, instead of creating a complex mathematical model that would reflect in great detail the complex interconnecting relationships, parameters of logistics processes, and the efficiency of these processes, regression models can be used.