esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Analysis
### Performance Analysis of an Unbalanced Dataset in the Dual-Channel Feature Fusion Model
#### 5.2.2 Experimental Results  
As can be seen from the results in Figure 8, when unbalanced data are used as the training set, the classification effect of plunger ball head wear and triangle hole blockage on the test set is somewhat different from that of other categories of data, decreasing by 31% and 25%, respectively. The main reason for this is that the real samples of these two types of faults are small, and the information learned during the training of the diagnosis algorithm is very limited, so it is easy to cause the problem of misclassification, which also confirms the problem of data imbalance restricting the performance of the diagnosis model mentioned above.
## 3 Prefiltered Frequency-Domain Equalization
### Complexity Analysis  
A comparison of the computational complexities of PF-SCFDE and SCFDE is presented in this subsection. \(N\) represents the length of the sequence after removing the front-end CP, and \(M\) represents the length of the tail-end CP. Because both algorithms use the same matched filter, only the complexities of the equalizer and detector are compared.  
The computational complexities of the two equalization algorithms are listed in Table 1. PF-SCFDE replaces the whitening filter with a prefilter before the MMSE equalization, and hence the complexities of this step are approximately equal, whereas the complexities of the symbol detection slightly differ because the information from the imaginary domain is used in PF-SCFDE. Due to the fact that the most computationally intensive steps in both equalization algorithms are DFT, the complexity of PF-SCFDE is similar to that of SCFDE.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 2 Background
### Other Preliminaries
#### 2.3.3 Incentive Mechanism  
The utilization of an incentive mechanism motivates vehicles to contribute and honestly share their data (i.e., traffic information) to improve the system's reliability. The existing scheme of incentive mechanisms such as the vehicle's reputation-based [30] and payment-based [31] generally use a central trusted authority. The trusted authority guarantees direct communication and secure transaction to prevent fraudulent vehicle behaviors. Therefore, the system's reliability, equity, and quality are influenced by the central trusted authority as a service provider. However, the centralized incentive mechanism is insufficient as it still facing the risk of a SPoF. It is worth noting that a single mistake affects the entire system orchestration. Further, the server may be a bottleneck caused by the necessity of large amounts of vehicle's transactions [32]. In the context of user privacy, a central service provider may also expose vehicles' private data or trade it for personal benefit. Once vehicles become malicious, they can manipulate the data sharing process by providing the wrong information to gather private information from other vehicles. Therefore, the privacy issue needs to be considered to achieve trustworthy and fairness VECNets. For this reason, we propose the decentralized incentive mechanism based on blockchain and smart contracts to achieve trustworthy environments. Our goal is to enhance reputation-based data management's security using blockchain to encourage vehicles to contribute honestly to VECNets data sharing transactions.
## 5 Simulation and Analysis  
In this section, we provide some numerical simulations for 1-D and 2-D missing data recovery. In order to validate the proposed approach, we generate 1-D and 2-D synthetic data which can be sparsely represented as DCT coefficients, and compare the estimation accuracy of the proposed approach with IDWA interpolation on these synthetic data sets. We also use real sensor data sets [2] to validate our method. All the simulation results with the sparsity-based data interpolation are accomplished only with DCT as the sparsifying transform, except the simulation in Figure 19 where the failure of wavelets is shown for real data. We do not compare our method with the Kriging method since no historical variogram is available in our experimental data.  
All the simulations are repeated 100 times, and the locations of missing samples are changed for each repeated simulation. The average and standard deviation of RMSE are computed. The main parameters in the simulations are \(N\), the number of samples in the original data; \(M\), the number of missing samples; and \(S\), the number of nonzero sparse coefficients. We also define the missing rate as \(m=\dfrac{M}{N}\) and sparsity \(s=\dfrac{S}{N}\). The relationships among estimation accuracy different missing rates and missing square block sizes are discussed in the next sections.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 1 Introduction
The existing image retrieval methods based on CNN features are primarily used in the context of supervised learning. However, in an unsupervised context, they also have problems such as only being object-centric without considering semantic information such as location, and deep features have large dimensions. The proposed method combines deep and shallow feature fusion. The method extracts the deep features from object-centric and place-centric pre-trained networks, respectively. DCT [39] is used for deep feature reduction. The shallow features are based on HSV, LBP, and DTCWT. Singular value decomposition (SVD) [40] is applied to LBP and DTCWT for dimension reduction [41]. Our contributions include:  
1. An image retrieval method based on CNN-DCT is proposed. Through DCT, the correlation of CNN coefficients can be reduced. DCT has good energy accumulation characteristics and can still maintain performance during dimensionality reduction. 2. A combination of shallow and deep feature fusion methods is used. Deep features are extracted from a deep neural network centered on objects and places. It has better image retrieval performance than using a single CNN feature. 3. Other factors that affect the experimental results are analyzed, such as measurement metrics, neural network model structure, feature extraction location, feature dimensionality reduction methods, etc. The remainder of the paper is organized as follows. Section 2 is an overview of related work. Section 3 introduces the proposed image retrieval method based on shallow and deep features fusion. Section 4 introduces the evaluation metrics and presents the experimental results on the Corel dataset and Oxford building dataset. Section 5 offers our conclusions.
## 8 Future Directions
### Environmental Adaptability  
Incorporating adaptive mechanisms that adjust to varying environmental conditions such as humidity and temperature can enhance the reliability of THz-VLC communication systems. These mechanisms include the real-time monitoring and adjustment of transmission power and beam direction. Developing an adaptive THz communication system that uses environmental sensors to monitor atmospheric conditions and dynamically adjust the transmission parameters can maintain optimal signal quality and reliability.
# Model Test Analysis of Subsurface Cavity and Ground Collapse Due to Broken Pipe Leakage
## 3 Results and Discussion
### Cavity Initiation and Evolution Processes
Moreover, when the crack size exceeded a certain threshold, the pipe seeped out an excessive amount of water, the soil quickly hollowed out in a short time, and soil cavity development and ground collapse occurred in parallel. Although the soil cavity formed by the two types of cracks were about the same size, the former cavity size was slightly smaller than the latter, as the 2 cm vertical crack set formed the ground collapse slightly faster than the 1 cm vertical crack set, as shown in Table 1.  
* From a comparison of the crack types, the vertical damage took only a short time for the pressure pipelines, because water at high speeds and pressures can quickly flow out and scour the soil around the pipelines; the cavity was significantly larger in area and degree of damage than the erosion cavity formed by circular cracks on the soil around the pipe.
## 6 Experiment and Analysis
### Dataset  
To substantiate the efficacy of our approach in road networks, characterized by varying scales and geographical regions, we meticulously selected five distinct areas for algorithm training and evaluation: Stanford, California, United States; Queenstown, Central Region, Singapore; Cambridge, United Kingdom; Rouen, France; and Culver City, California, United States, which are shown in Figure 3. These datasets were used for algorithm training and evaluation.  
* Road network data: The road network data for the aforementioned areas were obtained through Open Street Map [37]. We acquired information such as the coordinates \((X_{s},Y_{s})\), road types \((T_{s})\), and number of lanes \((N_{s})\) for each node in the road network.
* Charging station data: Existing charging infrastructure data for these road networks were obtained from the Open Charge Map [38]. These data include the location \((X_{c},Y_{c})\), type \((T_{c})\), number of chargers \((N_{c})\), charging capacity \((Ca_{c})\), charging cost \((Co_{c})\), and charger prices. In our calculations, publicly available charging station price data were used to estimate the costs associated with building new charging stations.  
The scale of the road networks, the number of nodes, and the number of charging stations in each city are presented in Table 1. It can be observed that the Stanford road network has the fewest nodes (534), while the Cambridge, UK area has the largest coverage with 3233 nodes and 7121 edges. Culver City, California, United States, possesses the highest number of existing charging station infrastructure \((113)\). The selection of road network data of varying scales allows for comprehensive evaluation of the algorithm's robustness and applicability.  
During the data preprocessing stage, we matched the road network nodes with the charging station information for each area. Each charging station was assigned to the nearest road network node to simplify the problem-solving process. Additionally, we calculated the offline values of \(r(s)\), \(scope(s)\), and \(cov(s)\) for each road node \(s\in S\) based on Equations (12)-(14) to facilitate subsequent calculations. As described in Section 5.1.1, we considered all the nodes \(V\) in the road network, their topological relationships \(E\), the charging demands \(Dem_{v}\) of all nodes, and the cost information \(Price_{v}\) associated with building new charging stations as inputs to establish the observation space for reinforcement learning.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**City** & **Nodes** & **Edges** &
\begin{tabular}{c} **Existing Charging** \\ **Stations** \\ \end{tabular} \\ \hline Stanford & 534 & 1178 & 13 \\ Queenstown, Central, & 1001 & 1008 & 3 \\ Singapore & 3233 & 7121 & 30 \\ Cambridge, UK & 2273 & 4824 & 29 \\ Rouen, France & 863 & 2086 & 113 \\ Culver City, & & & \\ California, United & 863 & 2086 & 113 \\ States & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of nodes and edges, and existing charging stations in different city road networks.  
Figure 3: The five road networks we selected for testing.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 2 Materials and Methods  
### Study Regions  
To evaluate seismic-induced soil moisture variations, a set of recent earthquakes coinciding with SMAP's lifespan from April 2015 to the present that are rich in recorded seismic data were identified. This study focused on seismic areas that experienced moderate to high-intensity earthquakes during the SMAP observation period. Three main criteria were considered for event selection: (1) target earthquakes had a moment magnitude greater than 6.0; (2) events are well-documented with post-event field investigations, seismic records, and site characterizations maps; and (3) high-quality SMAP data are available; for example, SMAP cannot measure soil moisture values when a region is covered by snow and SMAP data are not reliable if radio-frequency interference (RFI) corrupts the data in regions such as Japan [69]. The geographic location, the mainshock date, and the magnitude of the eleven target earthquakes are depicted in Figure 1. The wide temporal and spatial distribution of target earthquakes ensures that the observations from this study can be extended to other seismic events.  
For each earthquake, a focus zone was determined that contains all regions having a Modified Mercalli Intensity (MMI) of 5 or greater, based on data reported by the United States Geological Survey [70]; an example for the Petrinja, Croatia 2020 earthquake is shown in Figure 2 (focus zones for other study regions are shown in Figure A1 of the electronic supplement).
## 3 Results and Discussion
### Economical Quantity Analysis  
Cost analysis and quantity calculations were done for a full four-lane highway and estimation was calculated for a 1 km long patch of such highway, with 50 mm surface course thickness and additive was added in all respective proportions. Table 5 shows the saving, in terms of cost and quantity of material, per m\({}^{3}\) of asphalt mix. The eco-friendly and cheaper road could be developed from this study design. The calculation is provided in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Composition** & **Marshall Stability (60 \({}^{\circ}\)C)** & **Marshall Flow (60 \({}^{\circ}\)C)** & **Mixing Temp (\({}^{\circ}\)C)** \\ \hline
**Test Method** & \multicolumn{3}{c}{**ASTM: D1559**} & **ASTM: 1559** & \\ Units & \multicolumn{3}{c}{KN} & mm & \\ M 1 & 100\%B + 0\%M & 9.64 & 2.24 & 160 \\ M 2 & 90\%B + 10\%M & 10.13 & 2.71 & 160 \\ M 3 & 80\%B + 20\%M & 10.21 & 3.08 & 160 \\ M 4 & 70\%B + 30\%M & 11.70 & 3.42 & 160 \\ M 5 & 60\%B + 40\%M & 11.91 & 3.90 & 160 \\ M 6 & 50\%B + 50\%M & 12.1 & 4.03 & 160 \\
**Standard** & **With pure bitumen** & **>9** & **2–4** & **100–170** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stability and flow analysis of asphalt mix samples with modified and unmodified bitumen.  
Figure 3: Comparative performance analysis of bitumen modification vs stability (KN) and flow (mm).  
Economic analysis is one of the important aspects of the sustainable production process, which shows how economical a certain intervention is. An increasing trend in cost saving could be seen during the analysis, as shown in Figure 4.
## 5 Conclusions
### Added Value  
[PERSON] and [PERSON] [52] defined 10 types of added value for FM/CREM: satisfaction, cost, productivity, reliability, adaptation, culture, social, economic, spatial and environmental. The added value of occupancy sensing for the respondents focused mainly on satisfaction, cost, productivity and culture (image). Research shows that the added value of facility management is challenging to show because it is mainly seen as a cost item. From a facility management perspective, the added value of occupancy sensing is that they data can be used to set up the building more efficiently, offer employees more insight and create data-based decisions that allow them to work more proactively. The added value of occupancy sensing can contribute to the insight into the added value of facility management in an organisation. However, this can only be achieved if the factors mentioned earlier are taken into account. Smart features are still under development, but the expectation is that data will become even more critical in the future. When the FM/CREM department is well aligned with the strategy of the organisation, a facility manager with data-based facility management can have a significant impact on the functioning of the entire organisation. Research shows that to get added value from occupancy sensing, many conditions must be fulfilled. The non-user showed in the interview that not every organisation can comply with those conditions, and therefore it is not a sustainable investment.
## 1 Introduction
In addition, elastic supports, including nonlinearity, have been studied to enhance the performance of energy harvesting [41] and the efficient analysis of FGM-laminated shells [42], and many real-world problems can be analyzed utilizing network topology [43; 44]. However, the present study differs from those reviewed in the literature. The process of deducing the boundary condition at the beam joint and elastic supports is the same [45]. However, because the sum of the forces at arbitrary positions and at the joint is always zero, the shear force and bending moment are assumed to always be zero. Therefore, the free-free end conditions at both ends were considered to establish a frequency determinant for solving such problems, and various elastic boundary conditions were expressed by adjusting the spring values. When these spring values are infinite, it becomes a fully clamped end condition; when the spring value is zero, it becomes a free-end condition. Through this procedure, it was possible to define the effects of the elastic support and concentrated mass, as presented in the theory section. The accuracy and efficiency of the proposed method were demonstrated through the analyzed results and various comparisons. To determine the more accurate dynamic characteristics of many real-world problems for which fully clamped conditions cannot be provided, this study attempted to develop a simple numerical method for the vibration analysis of an elastically restrained tapered beam with an axial load and concentrated mass. The new tapered beam has a concentrated mass and elastic support at both ends, and an axial load acts on the right end. A concentrated mass attached to an elastic support was considered, and the elastic supports were modeled utilizing translational and extensional springs. Various boundary conditions were constructed by adjusting the values of the elastic springs, and large deformations or nonlinear problems [46] of the tapered beam were not considered. To demonstrate the method's efficiency, various examples were investigated for analyzing the effects of concentrated mass, axial force, and elastic supports, including intermediate elastic supports.
# Toward Defect Engineering Strategies to Optimize Energy and Electronic Materials
## 1 Introduction  
In the past few decades of the miniaturization of devices, the introduction of more structurally complicated materials in conjunction with demanding processing and operating conditions have made the study of defects in materials increasingly important [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. There is no single recipe of how to treat materials and what is the optimum concentration of defects. In a material, some defects and dopants can have a beneficial impact and in a similar material the same defects can deteriorate its physical properties. For example, \(n\)-type dopants in silicon are necessary to form devices and do not readily interact with vacancies due to the low concentration of the latter. In isostructural germanium, \(n\)-type dopants are hard to contain as they diffuse aided by vacancies and it is hard to form \(n\)-doped regions [3]. The same arguments can apply to energy related materials (for example nuclear, fuel cell, and battery materials) for which there is presently a requirement to employ and/or form materials with better properties that will need to operate at more difficult conditions for longer periods of time.  
Using defect engineering strategies is an efficient way to tune the physical properties and optimize materials for energy and electronic applications. Defect engineering strategies in essence use the differences that composition, structure, disorder, and strain can have on the physical properties of the material. It is impossible to highlight all the progress in the field and contain it in a single review. In the present review, we consider a range of applications and materials from nanocomposites to more traditional systems such as silicon and titanium dioxide aiming to demonstrate how the defect engineering strategies considered can be transferable to numerous systems and applications.
## 2 Materials and Methods
### High-Throughput Sequencing and Statistical Analysis  
To decipher bacterial community diversity, the V4 region of the 16S rDNA gene was amplified using primers 515F (5'-GTGCCACGCMCCGCGGATA-3') and 806R (5'-GGACTACHVGGGGTWTCTAAT-3'). For fungal communities, ITS9 (5'-GAACGCA GCGAAATGCGA-3') and ITS4 (5'-TCCTCCGCTTATTGATAGC-3') primers were used that target the ITS2 region [23]. Amplifications were performed at a final volume of 50 \(\upmu\)L for 35 cycles at annealing temperatures of 52 \({}^{\circ}\)C for V4 and 60 \({}^{\circ}\)C for ITS2, using the Invitrogen(tm) Platinum(tm) II Hot-Start PCR Master Mix (2X) kit (#14000-013, Thermo Fisher Scientific, Inc., USA). Subsequently, 20 \(\upmu\)L of each sample's ITS2 and V4 amplicons (40 \(\upmu\)L total) were pooled together. Amplicons were purified and tagged, then quantified and library prepped according to the manufacturer's instructions (#A26216, #4471250, #4474009, #4474518 Thermo Fisher Scientific, Inc., USA). Subsequently, the metagenomic library was sequenced the Sequencing Facility of the Laboratory of Plant Genetics and Breeding at Aristotle University of Thessaloniki, using the Gene Studio S5 platform (Thermo Fisher Scientific, Inc., USA). After checking quality parameters and performing necessary modifications (QC-, adaptor-, and barcode trimmed) through the Ion Torrent Suite, raw sequences were uploaded on the MG-RAST server [43] under the project number mgp97325. Processing of submitted samples included MG-RAST QC dynamic error removal (DRISEE), dynamic trimming (DYNAMICTRIM), denoising, and normalization (FASTQ-MCF). Taxonomy was assigned at 97% identity (e value \(10^{-5}\)) using Silva rRNA databases: SSU for bacteria and LSU for fungi. The resulting taxonomy matrix (OTUs Table [18] was used for all subsequent analysis. The PAST3 software [44] was used to calculate diversity indices (\(\upalpha\)-diversity, rarefaction curves) and statistics (PCoA, Bray-Curtis and UPGMA clustering, PERMANOVA, and ANOSIM). Venn diagrams were carried out according to [PERSON] et al. [45].
## 3 Methodology and Model
### Analysis of Algorithm Complexity  
In WSNs, node applications are typically constrained by energy and computational resources. Since different algorithms require varying amounts of time to optimize the same problem, analyzing the computational complexity of any proposed algorithm becomes essential.  
We use big \(O\) notation to analyze time complexity. Let \(M\) represent the number of nodes and \(N\) represent the number of beacon nodes. The population size in the PSA is denoted by \(n\), and the maximum number of iterations is \(T\). The dimension of the algorithm is represented by \(D\). The time complexity for population initialization and position updating in the PSA can be expressed as \(O(n)\) and \(O(nTD)\), respectively. The complexity of the weighted hyperbolic process is \(O(M^{2}-MN)\). In a 3D scenario, the PROSAC strategy is applied with a complexity of \(O(N^{2})\). Considering the proportion of beacon nodes is relatively small, the overall time complexity of the proposed algorithm can be approximated as \(O(M^{2}+nTD)\) and \(O(M^{3}+nTD+N^{2})\) in 2D and 3D scenarios, respectively. In contrast, DV-Hop uses the least square method to localize sensor nodes, with computational complexities of \(O(M^{2})\) and \(O(M^{3})\) in 2D and 3D scenarios, respectively. The proposed algorithm has moderately improved the localization accuracy by slightly increasing the computational complexity. This increase is deemed acceptable for the benefits it offers.