esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Sequential Sampling Method
### The Multiple-Update-Infill Sampling Method  
The multiple-update-infill sampling method can be defined as a combination of the infill criteria. For efficient global surrogate modeling, the SS method should contain two conflicting parts [10; 25; 26]: (1) global exploration and (2) local exploitation. The global exploration discovers interesting regions that have not been visited before. By doing so, it can reduce the bias in sampling due to the incomplete exploration of the entire input space. On the contrary, the local exploitation plays a key role in finding the regions of particular interest (maximum and minimum values or large prediction errors). Our previous study proposed the multiple-update-infill sampling method with three infill criteria (\(MSE+EI_{min}+EI_{max}\)) to contain both the global exploration and local exploitation for global surrogate modeling [10]. The rationale of the previous multiple-update-infill sampling method is as follows: (1) \(MSE\) can select infill samples from the regions where training samples are spares, and (2) \(EI_{min}\) and \(EI_{max}\) can explore and exploit the potential regions near maximum and minimum to update the surrogate model.  
The proposed method uses the MED to improve the capability for the balanced exploration and exploitation (that is, a similar role of \(EI_{min}\) and \(EI_{max}\)). The potential problem in using the EI criteria is that the infill samples are sometimes clustered, and this results in the waste of the SS sampling and potential numerical instability. This is the motivation of considering the MED instead of the EI criteria in the proposed multiple-update-infill sampling method. The MED can select infill samples from the regions of particular interest (that is, maximum and minimum) by maximizing the inter-distance among the training samples. This feature of the MED is good for global surrogate modeling in order to avoid ill-conditioning due to clustering of the infill samples to some regions in the input space.  
In this study, five multiple-update-infill sampling methods are considered to add multiple infill samples to ensure the global exploration and local exploitation. As a representative of the single-update-infill sampling method, the MSE is used to compare the multiple-update-infill sampling methods. The multiple-update-infill sampling methods are considered as follows: (1) \(MSE\), (2) \(EI_{max}+EI_{min}\), (3) \(MED_{max}+MED_{min}\), (4) \(MSE+EI_{max}+EI_{min}\), (5) \(MSE+MED_{max}+MED_{min}\), and (6) \(MSE+EI_{max}+EI_{min}+MED_{max}+MED_{min}\) (All).
## 5 V. Test Framework
### Hardware Setup  
According to Figure 3, we developed a hardware-based experimental setup at FIU smart grid testbed, shown in Figure 4. In this setup, we used four Beaglebone Blacks as defined agents for this communication framework (each one has an AM335x Arm(r) Cortex-A8 with 1 GHz processor and 512 MB RAM), a function generator to generate the input signal with 3.2 volte amplitude and frequency of 40 Hz, and an oscilloscope to monitor and compare the input and output signals. A personal computer (PC) was deployed to monitor the RTI admin console, where we can monitor the DDS system participants including the publishers and subscribers, DDS domains, and topics.
## 5 Simulation and Results
### Equal Order-Fractional PMSM Model  
The data of equal order fractional PMSM Model is obtained from [9] where the PMSM system 2 behaves chaotically at parameters values \(\sigma\), \(\gamma\) of 10, 100 respectively and the equal order fractional parameters \(q_{1}=q_{2}=q_{3}\) of value 0.95 as well as the initial values of \([i_{d},i_{q},\omega]\) are [2.5, 3, 1]. The number of samples in the sampling vector are 100 with a step of 0.001 s.  
The introduced algorithms EPSO and C.EPSO are employed to identify the parameters at which the motor behaves chaotically at 200 iteration and population size of 20. While the minimum and maximum limits of variables are within range of [5, 15] for \(\sigma\), [80, 120] for \(\gamma\) and [0.9, 1] for all \(q\).  
The obtained results by C.EPSO and EPSO variants are listed in Table 2. The table data shows that the effect of adding chaos maps into EPSO is significant on both of their accuracy and consistency. Where, EPSO provides \(mean\pm STD\) of MSE equalto \(1.963\times 10^{-16}\pm 4.601\times 10^{-16}\) While C.EPSO offers values in range of \(10^{-26}\pm 10^{-28}\) magnitude of orders especially with Gauss/mouse and tent maps.  
As the time factor and the algorithm convergence speed are essential factors, the mean convergence curves are plotted in Figure 2 to emerge the swiftness of convergence rate of the C.EPSO over the basic version. Where C.EPSO variants reach to lower MSE values compared to EPSO at 80 iteration however EPSO required more iterations trying to reach for closer values to that of C.EPSO as shown in the zoomed figure on Figure 2. Regarding for the execution time of the proposed algorithms are 4.951118 and 5.5194925 s for the C.EPSO and EPSO algorithms, respectively.  
Based on the previous results, it's obvious that merging chaos maps especially the Gauss/mouse and tent maps with the EPSO improves its accuracy, consistency of the results and convergence speed considerably. As C.EPSO with Gauss/mouse and tent maps provide \(mean\pm STD\) values of MSE equal to \(4.432\times 10^{-26}\) and \(4.985\times 10^{-28}\), respectively. Furthermore, the convergence curves indicate that the C.EPSO variants reach to the minimum values of MSE with lower number of iterations and execution time.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 6 Conclusions  
In this paper, the laser scanning thermography technique for detecting surface cracks of laser-repaired parts with irregular surface structure is studied. The main conclusions of this paper are as follows:  
1. Based on the simulation results of laser spot thermal excitation, the influences of the repaired component materials, the laser power and scanning speed of the thermal excitation device, and the capturing frequency of the infrared camera on the temperature gradient at the crack are analyzed. The results show that for nickel-based alloys and iron-based alloys used for laser repairing, when detecting surface cracks in the width range of 40-80 \(\upmu\)m, the scanning speed is set to be 10-20 mm/s, the scanning line spacing 1-3 mm, and the laser power 3000 mW, higher precision detection results can be obtained.
2. For real LR components, a thermal image post-processing algorithm based on Prewitt gradient operator is applied to achieve high SNR and fast detection of surface crack shape and geometric distribution. Compared with the traditional gradient algorithm, this algorithm has the advantages of high automation and computational efficiency, and can be applied to the crack detection on rough surfaces. The detection sensitivity of crack width can reach 10 \(\upmu\)m.
3. Thermographic detection is carried out on LR layers with and without a smooth surface, and the influence of surface roughness on inspection is analyzed. The results show that the periodic temperature distribution is caused by the periodic cladding tracks structure. When the amplitude of surface fluctuation is small, the global threshold segmentation algorithm can effectively eliminate the cladding track noise.  
Conceptualization, C.G., W.S. and H.X.; Funding acquisition, H.X.; Methodology, C.G., W.S., Z.L. and H.X.; Project administration, H.X.; Software, C.G. and W.S.; Supervision, H.X.; Validation, W.H.; Visualization, C.G.; Writing--original draft, C.G.; Writing--review & editing, C.G., W.S., H.X. and W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 12032013 and 11972209), the National Key Research and Development Program of China with grant No. 2017 YFB1103900 and the National Science and Technology Major Project (2017-VI-0003-0073).  
Not applicable.  
Figure 21: Temperature distribution of several different measured surfaces along a line \(L_{1}\).
## 3 Design Approach
### Open Source Network Stack  
Many commercial products exist today, but these do not provide access to their firmware, thereby limiting their potential for research purposes. In contrast, a number of open source academic solutions is available (see Section 2). However, as discussed, existing platforms mainly provide source code for the localization algorithm, omitting the code for MAC protocol design, and higher layer networking protocol stack for large scale wireless deployments. Our platform provides a full IPv6 compatible IoT protocol stack, including a MAC design framework for designing multi-technology MAC protocols and multi-hop mesh networking protocols.  
Figure 4: The four layers of the hardware PCB, their material, and their thickness.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 7 Technologies of Relevance for oupPLUS
### Quality Assurance Technologies and Principles
& \\ \cline{1-1}  & Oxford defines big data as “extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.” Big data is the basis for manifold smart analytics, services and applications in smart cities. Technologically speaking big data also refers to the processes, technical frameworks and tools involved in the data collection and information analysis from the captured data. & \\ \hline \multirow{4}{*}{Geographic Information Systems (GIS) constitute a technology which has been already in use by public administrations for the past decades, in order to manage different types of information with geographic relevance. GIS systems capture different map structures and manage geographically various objects, for instance properties, sizes of land area etc. There are different relevant formats which capture data and metadata for such GIS systems, with the Infrastructure for Spatial Information in the European Community (INSPIRE) being the most prominent standard set in this area. Examples of successful usages are given by land area management, asset management and tracking, as well as various types of visualizations (e.g., map-based COVID-19 dynamics illustrations). & \\ \hline \multirow{4}{*}{Data Analytics and Artificial Intelligence} & Data analytics as the name suggests is the process of cleansing, transforming, examining and visualization of datasets, usually to gather insights that assist in decision making and establish a correlation between the various factors involved. & \\ \cline{1-1}  & Very often, the integration of data from various sources is a pre-step to data analytics. & \\ \cline{1-1}  & Artificial intelligence (AI) is a field of computer science, in which machines act upon inputs from their environment by simulating human intelligence, while incrementally learning from the interpretation of the input values. AI can be trained to perform a variety of tasks. Some applications of AI in the scope of IoT are to predict maintenance, automation failures, connectivity issues and intelligent orchestration of tasks in a complex IoT system. & \\ \cline{1-1}  & \(\sim\) 10
# An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
## 4 Preliminaries
### Homomorphic Hash Functions  
The message (\(M\)) undergoes encryption using a homomorphic hash function that is resistant to collisions [34; 35].  
\[HF(M_{i})=(A_{i},B_{i})=(g^{HF_{i,\rho}(M_{i})},h^{HF_{i,\rho}(M_{i})}) \tag{2}\]  
In this case, both \(\delta\) and \(\rho\) are randomly chosen secret keys from \(\mathbb{Z}_{q}\). The homomorphic hash function possesses the following properties:  
1. \(HF(M_{1}+M_{2})\leftarrow(g^{HF_{i,\rho}(M_{1})+HF_{i,\rho}(M_{2})},h^{HF_{i, \rho}(M_{1})+HF_{i,\rho}(M_{2})})\).  
2. \(HF(\alpha M_{1})\leftarrow(g^{\pm HF_{i,\rho}(M_{1})},h^{\pm HF_{i,\rho}(M_{1})})\).  
Additional properties of this homomorphic hash function are documented in [34; 35].
# Performance Improvement for PMSM Driven by DTC Based on Discrete Duty Ratio Determination Method
###### Abstract  
In order to improve the performance of the servo control system, which is composed ofthe permanent magnet synchronous motor (PMSM) driven by novel direct torque control based on the fixed sector division criterion (FS-DTC) utilizing the composite active vectors, a discrete duty ratio determination method (FS-DDTC) is proposed in this paper. The determination of the accurate duty ratio is the key to obtain the desired error compensational results for PMSM, which is related to the performance of the servo system directly. As the applied master vector and slave vector during each control period are the adjacent vectors, therefore, the direction of the synthetic vector is between the directions of the two applied active vectors. Additionally, the analytical relationship between the sector angle of the synthetic vector and the error rate, which can realize the determination of the discrete duty ratio value without complicated calculations is deduced first. Furthermore, the duty ratio values of the two applied active vectors in FS-DTC are obtained through the selections of the duty ratio scale in the novel discrete duty ratio determination method directly, which can simplify the calculation process of the accurate duty ratio values effectively. The effectiveness of the proposed discrete duty ratio determination method is verified through the experimental results on a 100-W PMSM drive system.  
direct torque control (DTC); permanent magnet synchronous motor (PMSM); synthetic vector; discrete duty ratio Article
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 4 Big Data Algorithm for Integrated Cost-Schedule Data Management
### Three Principles for Cost-Schedule Integration Algorithm  
Based on the proposed data structure, this research develops a cost and schedule data integration algorithm using big data technology. The three principles have been applied to the development. The first one is that the data for construction management exists somewhere in any format. It means that organization charts, contract documents, schedule reports, quantity take-offs, and cost reports are available. Information units for the _WHO_ dimension can be retrieved from organization and contract documents, while the units for the _WHEN_ can be extracted from schedules. In addition, data for the _WHERE_, _WHAT_, and _HOW_ dimensions come from quantity take-offs; one for the _WHY_ dimension is captured from cost reports.  
The second principle is that a higher level of dimensions can be decided using a lower-level information unit. When lowest-level data is defined, the upper levels are automatically determined, using their hierarchical relationship within standard construction classifications (i.e., MasterFormat and UniFormat) and construction project management standards. For example, slip forming from the cost report is matched to \"031113.13 Concrete Slip Forming\" in Masterformat; from its code, all five levels in _HOW_ dimension can be assigned: Concrete Slip Forming (L5, code 031113.13) \(<\) Structural Cast-in-Place Concrete Forming (L4, code: 031113) \(<\) Concrete Forming (L3, code: 0311) \(<\) Concrete Forming and Accessories (L2, code: 031) \(<\) Concrete (L1, code: 03).  
The third principle is that big data technology is available to collect, structure, extract, and analyze cost and schedule data, which is scattering across different documents. Figure 3 shows the conceptual process of extracting and analyzing cost-schedule data using big data technology. The detailed processes will be discussed in Section 4.3.
## 5 Case Study  
The two-stage model proposed in this study is illustrated and verified by the following case study. The data for this case study were provided by the Dounan Maintenance Division of the Fifth District Maintenance Agency of the Directorate General of Highways, Ministry of Transportation and Communications, Taiwan. The actual maintenance information of the maintenance division, such as the number of bridges, cost, and maintenance period, was obtained through expert interviews. However, the maintenance information of some bridges was not completely preserved. Therefore, some parameters, such as the effect period, were based on the data provided by the maintenance unit, and reasonable assumptions were made. Table 5 shows the current situations of the eight bridges maintained by the unit. As shown in Table 5, this study considers the superstructure and substructure of each bridge separately. For example, the superstructure of facility one has been maintained according to plan two, and the maintenance has been completed, with a remaining effect period of 4 years.  
The maintenance unit is responsible for maintenance plans for a total of eight bridges under its jurisdiction in the next year. In this case, the planning period is set to 50 years. First, Table 6 illustrates the importance setting of the eight bridges. The larger the number, the more important the bridge.
## 3 Results and Discussion
### Test Result Analysis  
#### 3.3.1 RGB-D Image Validity Analysis  
To understand the influence of the tree-crown RGB-D image on model detection and segmentation performance, the MSEU R-CNN algorithm was trained and tested using Dataset 1 (RGB) and Dataset 2 (RGB-D). The experimental results are shown in Table 3 where the Dataset 2 bbox AP, bbox AR, seg AP, and F1-score were 0.5809, 0.6793, 0.5747, and 0.6263, respectively, which are higher than the model performance indices trained using Dataset 1. The results show that the MSEU R-CNN model trained from the RGB-D canopy image has better detection and segmentation performance than the model trained on the RGB canopy image. According to the differential analysis of the dataset, the orchard background in the natural environment was complex and diverse,  
Figure 8: Network structure of Unet++.  
and most of the fruit trees in the RGB image overlapped, resulting in the model becoming unsuitable for distinguishing individual tree crowns. This negatively affected the detection accuracy of the model. However, the RGB-D images included the original RGB images with color, texture, and shape tree crown and depth image information. By adjusting the visual distance and related operations, only the citrus tree crown in the foreground was retained, and most of the invalid background and nontargeted tree crown types that greatly differed from the positive samples were removed. The negative sample type required by the binary classifier can be reduced to improve the model detection accuracy. Nevertheless, the seg AP only increased by 0.1%, indicating that the RGB-D image had little impact on the model segmentation performance. These experiments confirmed that the RGB-D image reduces the interference complexity of background and non-target crown information on the detection of spraying objects.
## 3 Experiments and Results
### Performance Comparison of Different Models  
#### 3.7.1 Alternative Models  
To verify the effectiveness of RIIAnet, we compared it with current state-of-the-art neural network segmentation models on the same dataset. The alternative models were BiseNet [34], Segnet [25], DeepLabv3+ [28], PSPnet [27], U-Net [22], UNet++ [24], and DeepCrack [26]. All algorithms were trained to converge in the same environment.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 5 Performance Evaluation
### Quantitative Analysis  
As shown in Figure 5a, the four combinations exhibit similar convergence characteristics in loss function trends, indicating minimal differences in convergence speed. However, the long-term performance comparison in Figure 5b reveals that the DDQN significantly outperforms the DQN after training stabilization. The improved average cumulative reward demonstrates the DDQN's superior capabilities in strategy optimization and long-term return potential. Further analysis of value network structures shows that ResNet-based models achieve better overall performance compared to VGG modules, which correlates with ResNet's residual architecture enabling stronger approximation capabilities for complex value functions.  
To validate the limitations of greedy strategies, we conducted 100 experiments using the greedy algorithm, achieving an average deployment reward of 11.98. Although the greedy method demonstrates computational efficiency advantages, its myopic decision-making mechanism tends to fall into local optima--a defect particularly pronounced in combinatorial optimization tasks. In contrast, reinforcement learning-based methods balance immediate rewards and long-term returns, effectively overcoming the inherent drawbacks of greedy heuristics and exhibiting superior global optimization capabilities.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.5 CNN in Network Measurement  
To discriminate between different items in an image, CNN uses an input image and assigns importance (learnable biases and weights) to each one [45; 46]. A feature extractor made up of a convolutional and pooling layer is what sets a CNN apart from a typical neural network. Only some neurons in the adjacent layer are connected to all of the neurons in the convolutional layer of a convolutional neural network. The convolutional layer of a CNN often has many feature maps. A rectangle-shaped arrangement of neurons makes up each feature map. The weight is shared by neurons with the same feature map (convolutional kernel). Sequence data processing is the main application for one-dimensional convolutional neural networks, image text recognition is the main application for two-dimensional convolutional neural networks, and medical image and video data recognition is the main application for three-dimensional convolutional neural networks.  
To analyze enormous volumes of data, [PERSON] et al. [47] devised a method for calculating the available bandwidth based on a data-driven normal form and four ML techniques. The findings indicate that CNN is the most precise method for calculating the available bandwidth. The microscopic and macro aspects of queuing delay are used by CNN to calculate the precise amount of bandwidth that is available. Two fully connected layers, two pairs of convolutional and pooling layers, and a six-layer CNN were created to estimate bandwidth. A CNN can automatically extract features since it shares the convolution kernel, which relieves the processing burden when working with high-dimensional data. The use of BP propagation to change parameters will, however, impede the change in parameters near the input layer when the network level is too deep. By neglecting the correlation between the local and the overall, the pooling layer will easily cause the training results to converge to the local minimum rather than the global minimum when the gradient descent algorithm is used.