esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
## 10 Conclusions  
This paper presents a novel unprecedented DPD-based NN architecture and training method for RoF link efficiency enhancement. A comparative evaluation study was carried out experimentally between NN- and Volterra (MP/GMP)-based DPD methods, and system potential was measured in terms of MSE, ACPR and EVM. The results validate that NN-based DPD reduces spectral regrowth by 22 dBs at 0 dBm of input power, and EVM is reduced from 8.1% to 1.5%, while the GMP-based method reduces ACPR by 14 dB and EVM to 2.3%. This asserts that an NN-based DPD method enhances the RoF link performance as compared to a GMP-based method already at a very low complexity. The extension of NN to include memory effects and tuning of hyper parameters for higher linearization link lengths and utilization of 5G NR multiband signals is envisaged in future work.  
Conceptualization, M.U.H. and K.K.; Data curation, H.J.; Formal analysis, K.K.; Funding acquisition, M.U.H. and M.R.; Investigation, M.U.H.; Methodology, M.U.H.; Software, M.A.; Supervision, M.U.H., M.R. and H.J.; Writing--original draft, M.U.H.; Writing--review and editing, H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
Authors would like to thank [PERSON] for their fruitful comments and suggestions.  
The authors declare no conflict of interest.
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 1 Introduction
Interruption is an indefinite disturbance signal [27], which will destroy the physical meaning of IMF and affect the fault detection results. Therefore, this paper proposes a method based on Improved Deep Principal Component Analysis (ID-PCA). The method first extracts the time-domain feature information in the vibration signal of the rolling bearing to obtain a new data matrix with fault features and then decomposes the data into multiple data processing layers through the depth decomposition theorem to extract weak early fault features, effectively improving the ability of early fault detection of rolling bearings. This method solves the following difficulties in the early fault detection of rolling bearings:  
1. Due to the weak early fault features of rolling bearings, it is difficult to extract weak fault features;
2. It is easy to suffer a certain loss in the process of collecting the vibration signal of the rolling bearing, and it is also affected by the noise, which causes certain difficulties in the extraction of the signal characteristics of the rolling bearing;
3. Based on the improved deep principal element analysis method, the process of establishing the model for detecting the early fault of rolling bearing is simple, and the calculation efficiency is high. The rest of the paper is structured as follows: The first part introduces the basic theory. In the second part, an early fault detection method of rolling bearing based on improved deep principal element analysis is proposed. The third part proves the feasibility of the method through simulation experiments. The fourth part summarizes the proposed method.
## 4 Results  
The use of the BG power plant with the grid (without PVs) produces savings that reach a maximum value for \(P_{m}=140\;\mathrm{kW}\) (Figure 6), while if only the PVs are installed, the benefit increases substantially with the installed power in the lower range, but then the increase becomes slower, and from \(P_{pr}=150\;\mathrm{kW}\), this benefit is produced exclusively by the sale of excess energy (Figure 7), therefore the economic margin decreases.  
When both installations are combined, it is observed that, after a certain size, the incremental benefit (\(B_{I}=\Delta B/\Delta I\)) decreases, making the necessary investment less attractive.  
The study carried out allows a verification of the benefit obtained with different compositions of the hybrid installation with respect to the cost corresponding to the supply exclusively from the grid. This benefit has been calculated by weighing the results of a cloudy scenario and a sunny one, considering a percentage of 23.8% of cloudy days according to the meteorological data history. As shown in Figure 8, increasing the power of the PV installation produces a growing benefit because there is a greater amount of surplus energy that is sold. However, it is also clear from the results that the improvement in this benefit is becoming increasingly small.  
Figure 6: Average daily benefit with a BG plant (the green area represents the most profitable powers).  
From a financial perspective, it is much clearer to analyse the IRR of the whole. With this analysis, it is observed that the optimal configuration obtains a maximum value of IRR = 6.13% for \(P_{m}=140\) kW and \(P_{pr}=80\) kW, while achieving a 20% discount on the price of energy from the grid for cooperative members, compared to the cost of energy without the use of renewable resources. This can be seen in Figure 9.  
For the optimal hybrid system obtained, Appendix B shows some numeric data that specifies the characteristics of the systems. Additionally, Figure 10 shows the operation of both plants of the optimal system on the typical sunny and cloudy days with the constraints described in the proposed method.  
Figure 8: Average daily benefit (in EUR/day) with both plants.  
Figure 7: Average daily benefit with a PV plant (the green area represents the powers with the highest slope).  
Figure 10: Operation of the hybrid system in the typical sunny and cloudy days.  
Figure 9: IRR (in %) with both plants.
## 5 Comparison with Traditional Methods  
The performance of the proposed model is compared to that of a traditional neural-network-based damage assessment method that integrates the general linear PCA and back-propagation neural networks (BPNNs) to identify structural damage [58]. By the way of illustration, this method is used to re-perform the procedure of identifying damage in the bridge in noisy condition. As an alternative to the NLPCA, the PCA is employed to extract damage features from the WPNEs, and then the BPNNs are utilized to recognize damage patterns. Fed with the extracted damage features using PCA, the BPNNs predict the location and severity of the damage, giving the results of the damage identification shown in Figure 11. The results of damage prediction measured by _MSE_ is 5.365 and by maximum error is 12.763. Conspicuously, this traditional model does not identify the severity of the damage with great  
Figure 10: Identification of damage in the bridge in severely noisy conditions. (**a**) signal-to-noise ratio (\(SNR\)) = 10 dB; (**b**) \(SNR\) = 20 dB; (**c**) \(SNR\) = 30 dB.  
accuracy. In particular, a relatively significant error of damage identification occurs in the cases whose damage is located in Ele 2 and Ele 5. By comparing Figure 11 with Figure 9, it can be easily seen that the proposed hierarchical identification model assesses the damage with greater accuracy, the performance of which is superior to that of the traditional method. This conclusion is further verified quantitatively by much lower magnitude of _MSE_ and smaller value of maximum error for the proposed model than the traditional one for other cases. The comparison between the two methods demonstrates the great significance and effectiveness of the proposed hierarchical neural network based model.
## 4 Model (SIRAI)
### Characteristics and Benefits of the Model (SIRAI)
* RPA Financing Return Assessment Model: This theme was not specifically addressed in the proposed model, so it remains blank. * RPA monitoring assessment model: The model includes evaluating operational efficiency and classifying impacts, making it relevant for continuous monitoring (Table 10). The proposed model (SIRAI) for integrating RPA and AI encompasses characteristics and benefits that were not sufficiently addressed in the previously analyzed articles. Here are the key additional benefits identified:  
* Identification and Classification of Impacts: The model emphasizes the need to recognize and categorize social and environmental impacts, which is not a central focus in the previously analyzed articles. This classification allows organizations to understand the broader repercussions of their RPA and AI implementations, promoting sustainable business practices. * Ethical Considerations: The model includes an assessment of privacy and cybersecurity standards, ensuring that organizations comply with ethical and regulatory norms. This emphasis on ethics and compliance was not widely discussed in earlier articles. * Organizational Readiness Assessment: The model provides a practical guide for evaluating an organization's readiness to integrate RPA and AI, which was not addressed in depth in existing articles. This assessment helps companies adequately prepare for technological changes. * Customization of Metrics: Organizations can tailor evaluation metrics based on their specific needs, allowing for a more focused and efficient approach to RPA implementation. * Actionable Recommendations: The model not only evaluates but also offers practical recommendations that can be applied to optimize technology use, thereby improving organizational readiness and effectiveness. The proposed model for the integration of Artificial Intelligence (AI) and Robotic Process Automation (RPA) stands out for its comprehensive approach, which considers not only operational efficiency, but also the social and environmental impacts of technologies. Unlike existing models, which often focus exclusively on economic metrics, this model offers a holistic assessment that includes organizational readiness, identification and ranking of impacts, and practical recommendations for responsible implementation. This emphasis on ethical considerations and customization of assessment indicators makes the model a valuable tool for companies seeking not only to optimize processes, but also to promote sustainable and responsible practices in a constantly evolving digital environment.
## 4 Proposed Epileptic Seizures Detection Model
### Feature Selection Techniques
#### 4.3.1 Principal Component Analysis (PCA)  
PCA is a statistical procedure that transforms a set of correlated variables into a set of uncorrelated components ranked by the amount of variance they explain in the data. This technique is widely used for dimensionality reduction because it simplifies the complexity of the dataset while retaining the most critical information. We applied PCA for dimensionality reduction, selecting 50 components based on cumulative explained variance analysis. This approach ensures that the majority of the dataset's variance is captured with fewer features, thus simplifying the model and enhancing its computational efficiency. The decision to use PCA is rooted in its ability to mitigate the curse of dimensionality and improve model performance by reducing overfitting and enhancing generalization.  
The selection of 50 principal components in PCA was guided by a cumulative explained variance analysis, which revealed that these components captured approximately 95% of the total variance present in the dataset. This threshold was chosen to ensure that most of the meaningful information in the original 178 EEG features was retained while reducing dimensionality to improve computational efficiency and model performance. This 95% cut-off is widely accepted in EEG-based machine learning studies, as it balances the trade-off between retaining discriminative signal features and eliminating redundant or noisy dimensions.
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
###### Abstract  
Robots, or in general, intelligent vehicles, require large amounts of data to adapt their behavior to the environment and achieve their goals. When their missions take place in large areas, using additional information to that gathered by the onboard sensors frequently offers a more efficient solution of the problem. The emergence of Cyber-Physical Systems and Cloud computing allows this approach, but integration of sensory information, and its effective availability for the robots or vehicles is challenging. This paper addresses the development and implementation of a modular mobile node of a Wireless Sensor Network (WSN), designed to be mounted onboard vehicles, and capable of using different sensors according to mission needs. The mobile node is integrated with an existing static network, transforming it into a Hybrid Wireless Sensor Network (H-WSN), and adding flexibility and range to it. The integration is achieved without the need for multi-hop routing. A database holds the data acquired by both mobile and static nodes, allowing access in real-time to the gathered information. A Human-Machine Interface (HMI) presents this information to users. Finally, the system is tested in real urban scenarios in a use-case of measurement of gas levels.  
wireless sensor network; mobile sensors; hybrid wireless sensor networks; Cyber-Physical Systems; urban environment monitoring +
Footnote †: This paper is an extended version of our conference paper published in [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] “Wireless Sensor Networks for Urban Information Systems: Preliminary Results of Integration of an Electric Vehicle as a Mobile Node.” In Proceedings of the ROBOT 2017: Third Iberian Robotics Conference, Sevilla, Spain, 22-24 November 2017.
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 4 Implementation and Experiments
### Baseline  
For our baseline, we tested the corpus sentences using a commercially available rule-based Arabic grammar checker (Microsoft Word 2007). We compared the errors detected by the grammar checker with the golden labels of the corpus to obtain the results. Consequently, precision for the baseline was 75.6%, while recall was 91.96% and F\({}_{0.5}\) was equal to 78.39%.
## Materials and Methods
### Chemicals and Reagents  
Gallic acid (3,4,5-trihydrobenzoic acid) anhydrous for synthesis was purchased from Merck (Darmstadt, Germany). Ethanol absolute for analysis, acetate buffer (CH\({}_{3}\)COONa 3H\({}_{2}\)O), Folin-Ciocalteu phenol reagent, sodium chloride (NaCl), sodium hydroxide (NaOH), sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)), potassium iodide (KI) and iodine (I\({}_{2}\)) were purchased from Merck. Starch (from rice) was purchased from BIOTREK S.A.C.I. (Athens, Greece). 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and acetic acid (CH\({}_{3}\)COOH) were purchased from Sigma-Aldrich (Darmstadt, Germany). Potassium chloride (0.1 M, 1413 \(\upmu\)S/cm) used for the calibration of conductivity meter was purchased from Hanna (HI 7031, Hanna Instruments, Inc., Woonsocket, RI, USA).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Code** & **Harvesting Year** & **Predominant Pollen** & **Secondary Pollen** & **Minor Pollen (\$-15\%)** & **Identified Pollen (\(\mathbf{<3\%}\))** \\ \hline G1 & 2018 & _Eucalyptus_ spp : 73\% & _Raphanphanphanphan_: 17\% & _Acacia_ spp : 3\%, _Salix_ spp : 3\% & _Sinuspis arensis_: 2\%, _Trifolium_ spp : 1\% \\ \hline G2 & 2018 & _Eucalyptus_ spp : 82\% & - & _Raphanphanphanphan_: 11\%, _Edilian_ plunglemization_: 3\% & _Salix_ spp : 1\%, _Rubus_ spp : 1\% \\ \hline G4 & 2018 & _Eucalyptus_ spp : 78\% & - & _Raphanphanphanphan_: 10\%, _Salis_ spp : 6\%, _Acacia_ spp : 3\% & _Castanan sativa_ Mill: 1\%, _Echium_ plattagheimer_: 1\% \\ \hline G5 & 2017 & _Castanan sativa_ Mill: 85\% & - & _Rabus_ spp : 11\% & _Laundula stochas_: 1\%, _Cratagus_ monogra_: 1\% \\ \hline G6 & 2017 & _Castanan sativa_ Mill: 82\% & - & _Eucalyptus_ spp : 6\%, _Rubus_ spp : 6\%, _Elchium plunglemization_: 3\% & _Raphanphanphan_: 1\%, _Erica_ spp :6\%, _Elchium plunglemization_: 3\% & _Raphanphanphan_: 1\%, _Cytisas_ spp : 1\%, _Cytisas_ spp : 1\% \\ \hline G7 & 2017 & _Eucalyptus_ spp : 79\% & - & _Erica_ spp :7\%, _Rubus_ spp : 5\%, _Kalman_ spp : 2\%, _Solanum_ spp : 1\% \\ \hline G8 & 2018 & _Erica_ spp: 46\% & _Castanan sativa_ Mill: 30\% & _Eucalyptus_ spp : 10\%, _Latus_ & _Trifolium_ spp : 2\%, _Cytisas_ spp : 1\%, _Certicus_: 3\%, _Rubus_ spp : 3\%, _Acacia_ spp : 3\%, _Almes_: 1\%, _Salix_ spp : 1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample coding, harvesting year and millisecond data of Portuguese honey samples analysed in the present study.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 3 Methodology  
The SiamFP algorithm proposed in this article draws on the network structure of the SiamFC algorithm. The complete course of SiamFP algorithm is displayed in Figure 1. The main process is divided into the following four steps:  
* Feature pyramid extractor [25]. Given two input images I1 and I2, we generate L-level pyramids of feature representations, with the bottom (zeroth) level being the input images, i.e., \(C_{t}^{0}=I_{t}\). To generate feature representation at the lth layer, \(c_{t}^{l-1}\), we use layers of convolutional filters to downsample the features at the (\(l\)\(-\)1)th pyramid level, \(c_{t}^{l-1}\), by a factor of 2.
* Optical flow network. The first and second frame images are used as the existing tracking frame and the prior frame to enter the optical flow estimation network to assess the approximate position of the target in the next frame. Then, the center of the search area is cropped in accordance with the assessed position with the intention of obtaining a more precise search area.
* Siamese network. Input template image and search image into convolutional neural network to generate template feature map and search feature map.
* Attention network. Template features and search features obtain new template features and search features through spatial and channel attention mechanism networks, respectively, and the new features perform cross-correlation operations to obtain response graphs.
## 5 Discussion  
### Comparison with the Existing Literature  
The introduction of the OSWS in the realm of weather monitoring marks a significant stride. To contextualize its place amidst the extensive literature on the subject, it is vital to delineate how the OSWS aligns with, deviates from, and potentially surpasses contemporary solutions. The core strength of OSWS lies in its commitment in democratizing weather data access using cost-effective sensors. Many monitoring systems in the current literature  
Figure 27: Bland–Altman plot for PM 2.5 measured outdoors.  
Figure 26: Differences between the two PM 2.5 time series.  
either prioritize accuracy over affordability or vice versa. However, the OSWS strikes a balance, ensuring that smaller communities and users are not left behind in the age of data-driven decision-making. This holistic approach offers a twofold advantage: it ensures affordability without compromising the essential quality of weather data.  
Furthermore, real-time data sharing and monitoring capabilities distinguish the OSWS from many conventional systems. In sectors where swift responses to changing weather conditions are paramount, the immediacy of data becomes indispensable. The potential of OSWS to create a connected network of weather stations exponentially amplifies this benefit, enhancing data accessibility and utility across regions.  
To encapsulate the distinctions and similarities between the OSWS and other research endeavors, the comparative Table 8 offers a concise quantitative perspective, aiding in discerning the unique propositions the OSWS offers. This table shows the root mean square error that each station has against a professional measuring tool in an indoor scenario and whose values are considered as the reference.
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 2 Bearing Fault Diagnosis Method Based on Knowledge Reasoning
### Construction of Multi-Local Fault Diagnosis Model Based on Bi-LSTM
#### 3.1.1 Selection of Operating Characteristic Signals of Industrial Motor Bearings  
When the industrial motor is running normally, the raceway of the inner ring of the bearing rotates around the main shaft, and the raceway of the outer ring of the bearing is stationary. The bearing retainer guides the rolling elements between the inner and outer raceway of the bearing and keeps them within the bearing to minimize wear and heat generation. When industrial motor bearings fail, it is usually accompanied by certain abnormal friction and vibration. Therefore, the vibration signals of industrial motor bearings often contain a large amount of fault information, and the analysis of bearing vibration signals is also one of the most widely used technical means at present. However, the vibration signal, as the external sensor signal of the industrial motor bearing, is easily affected by the resonance frequency of loose screws and different parts of  
more comprehensive bearing status information through the external vibration signal and built-in current signal of industrial motor bearings to achieve high-precision fault diagnosis. In addition, these two kinds of bearing signals have different statistical characteristics and physical properties, which can reflect the information of different directions and different physical fields of industrial motor bearings simultaneously. In this paper, the original vibration signal \(V=(v_{1},v_{2},\ldots,v_{t})\) and the original current signal \(C=(c_{1},c_{2},\ldots,c_{t})\) are collected respectively through the bearing vibration sensor and the current sensor, and input to the corresponding noise self-encoder to perform preliminary feature signal cleaning.
# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 2 Relevant PV Problems
### Maximum Power Point Tracking (MPPT)  
PV cells have a complex relationship between their environment and the power they can produce. Along the IV curve (Figure 2) of solar cells there is a point where the power will be maximized, this is called the Maximum Power Point (MPP). This point usually changes depending on conditions such as irradiation, temperature or the state of the PV cell. These conditions can change the shape of the curve, making the problem nonlinear and time-varying due to the changes produced by the atmospheric and load conditions.  
Another problem is that it is not possible to directly obtain the IV curve of a single PV cell. The IV curves are usually taken from one single module or even from a PV array. The measured curves are more complex than the IV curve of a cell. The more complex a curve is, the harder it is to track the MPP.  
The algorithms to solve this problem can be classified according to different criteria; one of the most important ones is according to the number of variables used for measuring the tension. Another interesting approach is to classify the method according to the control strategy used. A brief explanation of the most important traditional methods can be found in Table 1, and more information can be found in works such as [15; 16].  
Figure 2: Different IV curves. Green circle: Max Power Point; Red Star: Open-Circuit Voltage (\(V_{\text{OC}}\)); Yellow Square: Short-Circuit Current (\(I_{\text{SC}}\)).