esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Components of the Generator of Expert Systems for Fault Diagnosis of IMs
### The Harmonic Order Tracking Analysis (HOTA) Method  
In this section, the HOTA method, introduced in [4; 5; 18], is briefly described. Unlike other methods where the frequency position of fault features (obtained from the current or its envelope) depend on other variables, in the HOTA method the fault frequencies are normalized to an integer, harmonic k-order scale, which is independent of the motor supply frequency and of the motor slip.  
This further simplifies and accelerates the procedure of conditioning the current signal prior to its processing by the expert system.  
HOTA method for transient state is a method based on reducing the 2D time-frequency content of the fault harmonics of the motor current to a much simpler 1D harmonic \(k\)-order domain. As explained in [5], this simplification process is implemented using a Gaussian window and a short time Fourier transform (STFT), by iteratively moving the Gaussian window along the time domain and performing the frequency axis re-scaling at each step (see an example in Figure 1).  
An alternative option is to replace the STFT transform in HOTA by the short-frequency Fourier transform (SFFT), as in [6]. SFFT generates a time-frequency Gaussian window which is displaced along the frequency axis, instead of the time axis (Figure 1). Both the STFT and the SFFT transform generate the same time/frequency representations, although the SFFT has speed computing advantages when applied to fault diagnosis of IMs, as will be shown in Section 4.1.  
In the last step of HOTA algorithm, once the time/frequency space is generated, a conversion into the harmonic \(k\)-order domain is made, obtaining as a result a single vector. Inside this vector each fault component is clearly shown through its \(k\) component number, as shown in Figure 2. This figure shows an example of the final result generated from the time-harmonic \(k\)-order space example shown in Figure 1. This graphic shows a fault \(k\) component within the harmonic \(k\)-order domain corresponding to a rotor broken bar fault.  
Figure 1: Example of time/harmonic \(k\)-order space generated with HOTA method for induction motors fault diagnosis in transient state.
## 4 Test Results and Discussion  
The developed E-dReg controller in this paper is designed and developed based on the relevant specifications provided by the Taiwan Power Company. Specific constraints and control strategies for E-dReg are proposed to enable the controller to adjust the grid  
Figure 10: Schematic diagram of E-dReg execution quality calculation method.  
frequency and control the transfer of electricity during peak and off-peak periods. In our research, LabVIEW predominantly functions within a tailored industrial computing ecosystem. LabVIEW 2018 software is deployed on the IPC, serving as the platform for code execution, data capture, and device control.  
In the experimental testing of this paper, measurements were conducted using CPM-80 and CPM-72 to measure frequency and power parameters. The testing specifically included the complete control actions of E-dReg and the execution of emergency dispatch capacity instructions from the Taiwan Power Company. Through the analysis of various test results and data, the paper discusses and explains the entire execution process of E-dReg control for frequency regulation and electricity transfer.  
During actual testing, we utilize the programming archival function to record the frequency and corresponding output power ratio (\(P_{out}\)) every second for the entire day. The archived data include the frequency measurements, output power ratio, and other related electrical parameters collected throughout the testing period. The schematic diagram of the archived raw data is shown in Figure 11. Subsequently, we will utilize these archived data to generate the waveform diagrams presented in Section 4.
## 2 Literature Review
### Asset Management Methodologies and Models Applied to Warranty Management
#### 2.2.2 Enterprise Asset Management (EAM) Methodology  
Enterprise Asset Management (EAM) is a comprehensive approach that covers the entire asset life cycle from acquisition to retirement and relies on advanced technologies such as the Internet of Things (IoT) and predictive analytics to optimize asset performance. This model enables not only continuous, real-time asset monitoring, but also predictive analytics, which is critical in warranty management to reduce downtime and improve system reliability. In the context of warranty management, EAM offers several advantages that help transform traditional approaches into more proactive and personalized ones, thus improving customer experience and operational efficiency:  
* Maintenance Planning: EAM platforms allow maintenance interventions to be planned based on the current status and performance of assets, thereby reducing unexpected claims. According to [19], EAM facilitates advanced diagnostics and condition-based maintenance, ensuring that interventions are carried out accurately and in a timely manner. This condition-focused approach helps avoid costly downtime and maintain equipment availability.
* Asset Life-Cycle Management: This approach ensures that each phase of the asset life cycle (acquisition, operation, maintenance, and replacement) is geared towards maximizing its performance and availability over the warranty period. By managing the entire life cycle, EAM enables maintenance policies to be tailored and adjusted to the operating conditions of the asset, promoting a reduction in the number of claims and increased customer satisfaction [20].
* Data Analytics and Prediction: With artificial intelligence (AI) and machine learning tools, EAM systems make it possible to anticipate failures and customize warranty policies based on the asset's actual condition and usage. This predictive analytics capability helps identify wear patterns and optimize warranty coverage, ensuring that interventions are timely and effective [7]. Through this customization, EAM improves asset reliability and ensures that maintenance resources are used effectively.  
To implement an effective EAM system, specialized applications, such as Maximo(r), integrate maintenance management and asset life cycle into a unified platform. Maximo facilitates data analysis and maintenance planning, maximizing operational efficiency and ensuring optimal asset performance throughout the life cycle [21]. For all these reasons, it can be said that EAM facilitates efficient warranty management by maintaining the precise control of an asset's performance throughout its life cycle, allowing for reduced costs associated with claims, improved asset reliability, and, ultimately, increased customer satisfaction. In addition, it should be noted that this tool reduces maintenance costs and allows for a quick response to claims, improving customer satisfaction and adjusting warranty policies to the actual state of the assets [4].
# On Sharing an FIB Table in Named Data Networking
## 4 Performance Evaluation  
The proposed scheme was evaluated using ndnSIM, which is an NS-3 based Named Data Networking simulator [5]. We used four real topologies acquired from Rockefuel [28]. Detailed information about the topologies is shown in Table 1. _Total nodes_ represents the total number of nodes in each topology. A node in a network can be classified as backbone, gateway, or leaf, and the number of each type is also shown. _Links_ represents the total number of links which connect the nodes and all links have the same delay. One thousand producers and consumers were randomly placed among backbone routers and leaf routers, respectively. Thus, the topologies used for the evaluation were equivalent to a network with more than 2000 nodes in total, including the producers and the consumers, which is the largest scale based on our knowledge. While producers were evenly connected to ten backbone nodes, the number of consumers in a single node was randomly chosen. We assumed that each name prefix was responsible for ten contents and each consumer generates _Interest_ packets for a single name prefix. Real URL data acquired from the ALEXA [29] were used as name prefixes. The performance evaluation was conducted under various Zipf parameters [30], which indicate the popularity of contents. The run time for each experiment was 60 seconds, and each consumer generates ten _Interest_ packets per second according to Poisson distribution. Thus, 600 thousand Interest packets in total were issued.
## 4 Conclusions  
In conclusion, we have designed and prepared a hot-electron photodetector-based on a Au/GaN-nanopillar structure, which can operate in the short-wave infrared regime well below the GaN bandgap energy. A suitable SBH has been developed for a higher photo-to-dark current ratio by post-etching annealing. The surface plasmons generated by the Au/GaN-nanopillar arrays can effectively improve the light absorption efficiency. The responsivity of the Au/GaN-nanopillar device in the SWIR range can be enhanced by about two orders of magnitude compared with the planar device. When light enters the Au/GaN-nanopillar device from the substrate, the UV can be absorbed by GaN and the UV detection is based on the interband transitions of electrons in GaN. While the IR can pass through the GaN to the Au/GaN interface and is absorbed by the Au film, the IR detection is based on the hot-electron photodetection. Therefore, the Au/GaN-nanopillar device  
Figure 7: (**a**) Responsivity spectra of the Au/GaN-nanopillar device and Au/GaN planar-type device. (**b**) The responsivity ratio of the Au/GaN-nanopillar device to the Au/GaN planar-type device.  
can achieve UV-IR dual-color photodetection. With the advantages of a simple structure and easy fabrication, the proposed devices have promising potential application in UV-IR dual-color photodetection.  
Conceptualization, Z.H. and Y.L.; methodology, X.T. and Z.H.; software, X.T.; validation, L.W., C.S., B.X., J.W., Y.H. and H.L.; formal analysis, X.T. and Z.H.; investigation, X.T. and Z.H.; resources, L.W., X.W. and J.Y.; data curation, X.T.; writing--original draft preparation, X.T.; writing--review and editing, X.T., Z.H., Y.L. and J.Y.; visualization, X.T.; supervision, Z.H. and Y.L.; project administration, Z.H. and Y.L.; funding acquisition, Z.H. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2017 YFA0205800), the National Natural Science Foundation of China (61991443, 61904093, 61975093, 61974080, 61927811, 61822404, and 61875104), the Key Lab Program of BNRist (BNR2019 ZS01005), the China Postdoctoral Science Foundation (2019T120090), and the Collaborative Innovation Centre of Solid-State Lighting and Energy-Saving Electronics.  
The authors declare no conflict of interest.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 4 Secure and Efficient Routing Protocol for Jellyfish Attack Prevention in V2X (SecE-V2X)
### Calculating the Node Honesty
#### 4.3.2 Direct Confidence  
The direct confidence can be calculated by integrating the communication confidence, delay, and packet reordering, \(PR\), using Equation (5):  
\[C_{direct}=\left\{\begin{array}{l}{w_{com}\times C_{com}+\frac{w_{Delay}}{D }}&{PR=0}\\ {0}&{PR>0}\end{array}\right. \tag{5}\]  
\(C_{com}\), \(D\), and \(PR\) are the communication confidence from Equation (4), delay from Equation (1) and packet reordering calculated in Equation (3), respectively. \(w_{com}\) and \(w_{Delay}\) are the weight adjustment parameters of communication confidence and delay, respectively, with \(w_{com}+w_{Delay}=1\) and \(w_{com}\), \(w_{Delay}\)\(\in\) [0, 1].
### Further Detail on Regulatory Aspects of Reference (The Italian Case)
In this context, it is significant to recall the official communication sent by the Italian Ministry of the Environment and Protection of Land and Sea to the EU in 2019, in which Italy committed to the designation of two new Natura 2000 Sites of Community Importance (SCIs) under habitat type \(1170-\)rocky reefs, specifically for the Palinuro and Vercelli Seamounts. This decision was taken in response to a formal request from the European Union through EUPILOT 8348/16/ENV. This commitment strengthens the ecological significance of Italy's EPZs by integrating them into the Natura 2000 Network established under the Habitats Directive (92/43/EEC), and confirms the conservation priority of the Palinuro Seamount over extractive activities, such as deep-sea mining. Thus, a more integrated governance framework\(-\)one that explicitly links geoscientific data with conservation obligations and resource management\(-\)is urgently needed to balance resource potential and ecosystem vulnerability. This would require stronger coordination between national regulatory bodies, scientific institutions, and international organizations to harmonize objectives and avoid conflicting mandates.
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 2 Literature Review
### PV Array to Inverter Sizing Strategies  
InMalaysia, the typical derating factors for the PV to inverter power size ratios utilized are 1.00 to 1.30 Thin-Film and 0.75 to 0.80 for the c-Si PV type [41]. These calculations takeinto account a variety of variables, including the environment, the mounting structure installation, system applications, PV module technology, type of inverter, and others. The PV-to-inverter ratio (PR\({}_{\text{k}}\)) for derating factor might be determined for the system configuration's dimensioning using Equation (1);  
\[\text{PR}_{\text{k}}=\frac{\text{P}_{\text{ac,inv}}}{\text{P}_{\text{PV} \text{SVC}}} \tag{1}\]  
According to [42], the PV-to-inverter power ratios can be expressed by Equation (2) for thin-film PV modules and (3) for crystalline PV modules by:  
\[\text{P}_{\text{ac,inv}}=0.889\times\text{P}_{\text{PV}\text{SVC}} \tag{2}\]  
\[\text{P}_{\text{ac,inv}}=0.76\times\text{P}_{\text{PV}\text{SVC}} \tag{3}\]  
Researchers and members of the solar industry created a number of sizing techniques, which were gathered and separated into two approaches. The following is a summary of all the data that was used to determine the optimal plan according to inverter techniques associated with the PV-to-inverter ratio sizing:  
* Manufacturers' recommendations based on PV guidelines.
* DC/AC sizing ratio according to third-party publications.  
In order to provide an overview of the effect of the ratio of the DC power of PV to inverter AC output power on the cost, the revenue factor was introduced [43]. The normalized revenue (R\({}_{\text{norm}}\)) relation can be derived by:  
\[\text{R}_{\text{norm}}=\frac{1+\text{A}\delta}{\left(\frac{\text{y}(\delta= \text{S}_{\text{new}})}{\text{y}(\delta=1\delta)}\right)} \tag{4}\]  
where, according to a solar farm's DC/AC ratio, \"A\" represents the portion of the total capital invested in the solar farm that has been spent. The \(\delta\) is the DC/AC ratio (>1.0) and y denotes the amount of annual yield. Normalized revenue vs. DC/AC ratio at 35\({}^{\circ}\) Tilt, 0
# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 3 Purpose and Methodology
### Machine Learning Model for Construction Cost Prediction  
In this study, the learning algorithm used is Deep Learning (DNN), with each deep learning model layer being composed of a fully connected layer. The concept behind this approach is as follows: DNN is a type of neural network model developed from Artificial Neural Networks (ANN), which features a deep structure comprising multiple hidden layers. DNN has the advantage of being able to model more complex data, and it represents an improvement over the overfitting problem encountered in ANN [14].  
To identify the influencing factors suitable for use in the learning process, the Pearson correlation coefficient was employed. The Pearson correlation coefficient is a statistical measure used to quantify the strength and direction of the linear relationship between two variables, X and Y. The coefficient has a value between \(+1\) and \(-1\), as dictated by the Kosh-Schwarz Inequality. A value of \(+1\) indicates a perfect positive linear correlation between the variables, while a value of \(0\) indicates no linear correlation. A value of \(-1\) indicates a perfect negative linear correlation between the variables. [PERSON] et al. [15] used Pearson correlation coefficients to determine the strength of the correlation between variables, and found that the performance of the model can be improved by identifying and utilizing variables with stronger correlation.  
[PERSON] et al. [16] analyzed a variety of documents proposing cost estimation with machine learning techniques, finding that the size of the dataset used to train the model is critical to accuracy. Due to the limited number of data samples available, which is considered relatively insufficient for effective learning, the cross-validation technique was applied to ensure the reliability of the study results. Cross-validation is a widely used method for evaluating the performance of machine learning models. Its purpose is to assess how well a model will generalize to new, unseen data. One common reason for using cross-validation, specifically k-fold cross-validation, is to prevent overfitting of the model. Overfitting occurs when a model is trained too well on the training data, resulting in poor performance on new data. The k-fold verification can use the entire data for learning without separating verification and test data, which can be used to accurately determine whether training data are suitable for prediction. [PERSON] [17] used k-fold cross-validation techniques to evaluate the accuracy of trained models on small datasets.  
Figure 1: Research flow.
## 4 Theoretical Calculation and Analysis
### Finite Element Calculation Results and Analysis
#### 4.3.3 Contrastive Analysis of Damage Morphology and Stress Cloud  
In order to analyze the entire damage process of the specimens, test specimen BD4-40-3 was taken as an example to provide a deformation cloud chart of the specimens before and after punching, as shown in Figure 20.  
It can be seen from Figure 20b that the deformation of the simulated specimen was more symmetrical when destroyed, and it is obvious that the loading area buckled downward, while the test area buckled downward only to one side (Figure 20c), which is mainly due to the ideal constraint of the specimen in the simulation. At the same time, the initial error (for example, the loading center is not coincident with the  
Figure 19: Comparison of calculated and experimental curves. (**a**) BD4-40-1; (**b**) BD4-40-2; (**c**) BD4-40-3; (**d**) PD4-40-3.  
Figure 20: Simulated deformation and failure modes of specimens. (**a**) Simulated deformation before loading; (**b**) Simulated deformation after loading; (**c**) Failure mode of specimen BD4-40-3; (**d**) Failure mode of specimen PD4-40-3.  
Figure 23: Mises stress contour figures of concrete and steel skeleton (damaged). (**a**) Mises stress in the concrete compression zone; (**b**) Mises stress of the reinforcement skeleton; (**c**) S22 stress in the concrete compression zone; (**d**) S22 stress in the concrete tension zone.  
Figure 22: Mises stress contour figures of concrete and steel skeleton (ultimate). (**a**) Mises stress in the concrete compression zone; (**b**) Mises stress of the reinforcement skeleton; (**c**) S22 stress in the concrete compression zone; (**d**) S22 stress in the concrete tension zone.  
As can be seen from Figure 21 to Figure 23,  
1. When the load was small, the main stress of the concrete in the compression zone of the specimen also was not very large. When the stress in the concrete tension zone reached 1.26-1.38 MPa, the concrete in the tension zone cracked, and the concrete stress in the compression zone was between 15.9-19.4 MPa. At this time, the steel's stress reached 546 MPa, so the distribution rebar did not yet yield.
2. When the specimen reached its ultimate load, the compressive strength of the concrete reached its ultimate compressive stress of 39.5 MPa, while the distribution rebar did not yet reach its yield strength (702 MPa). This indicates that the failure process of the specimen in the simulation process first has the center of the distribution rebar yielding, followed by essentially crushing of the concrete in the compression zone of the specimen, which is consistent with the experimental results.
3. When the specimen reached its breaking load, the stress of the concrete in the compression zone was reduced (corresponding to the strain increasing), and the area of the concrete tension zone continued to increase. At this time, the steel rebar entered its strengthening stage, and the stress of the steel bar continued to increase.
# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 4 Active Learning and Object Detection: A Literature-Driven Multi-Perspective Analysis
### White box-based methods
#### 4.4.2 Feature Distribution-Based Methods  
In this group, it was possible to differentiate four categories: diversity-, density-, rareness-, and representativeness-based methods. According to [49], diversity-based methods measure the information overlap between instances at the feature or label dimension. It is a sampling strategy purely exploratory that aims at selecting the most dispersed samples (outliers) according to a feature distance [41; 60]. The simplest diversity function is the Euclidean distance, but others can be considered in high dimensional data scenarios, such as Gaussian or Cosine functions, which are radial and angular approaches [37].  
Moreover, this class of methods (feature distribution-based) anchor on the key-concept of forcing the selection of instances where there is more density (a core subset [8]), relying on feature space analysis. This avoids the selection of outliers from the unlabelled dataset and focuses the selection on a relatively dense region [37].  
In the opposite direction, rareness-based method search instances that do not frequently appear in the training dataset [15; 50]. Ref. [15] proposed the selection of rare instances based on density estimations from the 3D data to improve long-tail performance and, in [50], recognition of unknown instances/classes absent in the training dataset was addressed.  
Another alternative is the representativeness-based approach, which seeks instances in the centre of the cluster of instances as in [67]. In this case, the sampling strategy quantifies the capability of an instance to represent the overall input patterns of unlabelled data [49].  
Figure 11 depicts a graphical example to explain the previously mentioned concepts. The red region is known as the area where the detector has more uncertainty in selecting an instance, while the union of the blue and green region represents the zone where a higher diversity of instances can be found.  
A few articles [16; 78] applying a diversity sampling strategy exclusively were identified. In [78], the authors applied a two-stream active clustering method intending to deal with bias in small datasets, i.e., instances far from representative of the whole dataset. In [16], the focus was the diversity over the spatial and temporal space to achieve increased performances. Moreover, Ref. [18] focuses mainly on diversity sampling but mixed with weakly-supervised OD techniques.