esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 3 Applications of Knowledge Graphs in the Power System Operation
### The Overall Construction Process of KG in the Power System Field
#### 3.1.4 Storage Options  
The two main approaches to storing KG are RDF tables and graph databases. As introduced above, the traditional RDF format [45] stores data in the form of triples and uses table links to perform graph traversal. The common query language SPARQL is not designed for complex graph analysis and will lead to obvious time consumption. The graph database represented by Neo4j [93] stores data in the form of graphs, which can significantly improve the storage and processing efficiency of massive data in the power grid and provides an implementation solution for building a ubiquitous power IoT. Therefore, graph databases are widely used to store domain knowledge in the power domain KG.  
Ref. [94] investigated the Neo4j storage method based on the topology of the power grid itself and verified the feasibility of the graph database in four practical scenarios in the power sector, respectively. The method visualizes the association between grid devices, provides easy equipment finding and management, and is a basic prerequisite for subsequent operations such as fault checking. Ref. [95] compared the performance of RDF tables and graph databases in a power equipment management KG. It was found that as the number of queries increased with the complexity of query content, the difference in query time between the two databases became apparent. Graph database queries were 2-3 times faster than RDF. Moreover, due to the built-in parallelism, the graph database queries were 16-17 times faster than RDF for aggregate queries with multiple starter nodes. Further, graph databases store data with attribute information that is more relevant to applications in complex power situations.
## 3 Design of Approximate QAM Demodulation Circuits  
To accommodate design space exploration, we design parallel \(M\)-QAM demodulation architectures in a modular approach. In this way, we are able to adopt alternative arithmetic and/or replace the arithmetic units with approximate ones.  
We consider \(M\)-QAM keying signals (amplitude+phase) and their corresponding gray-coded constellation map, where each constellation point is represented by a vector \(b\) of \(N=\log_{2}M\) bits. Assuming that \(s\) is the transmitted symbol, each symbol is expressed as \(z=s\cdot G_{ch}+w_{0}\), where \(G_{ch}\) is the channel frequency response and \(w_{0}\) is the additive white Gaussian noise (AWGN) of variance \(\sigma_{0}^{2}\). By performing zero-forcing frequency equalization and phase correction, the received symbol is equal to \(r=s+w\), where \(w\) is the AWGN of variance \(\sigma^{2}=\sigma_{0}^{2}/|G_{ch}|^{2}\).  
Subsequently, we present the QAM demodulation algorithms, their high-level block architecture, and implementation details regarding their basic components and the applied approximations.
## 2 FSCMR Wall Panel Structure  
### CMR Wall Panel Structure  
In response to increasing global temperatures, all countries are committed to encouraging the development of low-carbon industries. As structural, material, and construction techniques have raised the international criteria for low-carbon residences, research teams have incorporated building performance in terms of energy consumption and collapse resistance, and have developed a CMR wall panel system. The structure stands in line with energy-saving reforms, overall trends of residence industrialization, and strategic requirements for sustainable development.  
With a layer-by-layer embedded design, a CMR wall panel structure is composed of a cast-in-situ edge frame, layer slabs, and a prefabricated CMR panel. The latter, as a newly-proposed composite panel, uses lightweight blocks with a low elasticity modulus embedded in the CMR frame, manufactured by densely arranged reinforced-concrete beams and columns. The edge frame (constructed from concrete edge columns, connected columns, and concealed beams) both bonds and simultaneously restrains the CMR panel [10]. Thereby, the above-mentioned parts comprise the major force-bearing members of the structure, namely the CMR wall panel (see Figure 1). Floor slab of cast-in-situ reinforced concrete shall be applied as layer slab, the steel bars of which shall be well connected with the concrete beams, the edge columns and the connected columns. The force bearing calculation and construction procedures of layer slab shall follow the rules set out in the National Standard for People's Republic of China (GB50011-2010 and GB50010-2010) [11; 12].  
Compared to its counterparts, the CMR wall panel structure stands out for its force-bearing performance, seismic resistance, energy efficiency, temperature insulation, and potential for industrialization [13; 14]:  
1. Outstanding structural integrity and working performance;
2. Exceptional force-bearing performance;
3. Easy adjustment of structural stiffness;4 Multiple seismic lines of defence;
5 Effective seismic buffer;
6 Promising prospects for industrialization;
7 Strong performance in collapse resistance;
8 A practical, cost-efficient, energy-saving and environmental friendly choice.  
Because the residence pilot projects have taken place in Yinchuan, Shanxi, Henan, and many other provinces, the CMR wall panel structure has reached into to every aspect of social production and practice (see Figure 2), and efforts of the country toward this end have yielded both economic and social results [15; 16; 17]. Under such circumstances, the Ministry of Housing and Urban Development, P.R. China, has issued an industrial standard, _The Directive Rules for Concealed Multi-Ribbed Wall Panel Structures with Energy-efficient Blocks_, which became effective 1 June 2014 [13].
## 1 Introduction
(2011) [31] also explored the empirical correlations between \(V_{s}\) and SPT-N values and other geotechnical parameters. A considerable number of works on this topic have also been done in India: [PERSON] and [PERSON] (2010) [32], [PERSON] et al. (2013) [33], [PERSON] and [PERSON] (2008) [34], and [PERSON] et al. (2010) [35]. [PERSON] et al. (2011) [36] correlated \(V_{s}\) in terms of soil characteristics such as depth, soil type, and SPT-N value, using datasets from Taiwan. Due to differences in soil type and properties from region to region, which arise from differences in geologic settings and active geomorphic processes, the proposed empirical correlations are local and were only extrapolated to areas with a similar lithology. The application was generally constrained to similar soil conditions and geomorphic settings. As such, despite the numerous proposed empirical correlations, there is still a great importance in correlating \(V_{s}\) and geotechnical parameters using datasets from local settings. Additionally, a variety of reasons, such as lack of drilling and geophysical equipment, personnel, and/or budget, can hinder the carrying out of downhole measurements of \(V_{s}\), especially in smaller projects in the Philippines. Direct \(V_{s}\) measurements may be preferable, but in most cases where this is not feasible, empirical correlations between \(V_{s}\) and geotechnical parameters can be useful for site investigations. The main objective of this paper was to develop an empirical relationship between \(V_{s}\) values from a refraction microtremor to N-values from standard penetration test drillings, using statistical regression analysis. Twenty sites (20) in Metro Manila, Philippines were surveyed using the refraction microtremor technique to establish one-dimensional \(V_{s}\) profiles correlated to N-values from SPT. The proposed empirical relationship between \(V_{s}\) and the N-value of soils in Metro Manila represents an important development in site characterization techniques and seismic microzonation efforts in the study area.
## 3 Experiment Result and Discussion
### Performance Analysis
Therefore, the rotation of only ten degrees destroys the embedded watermark. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **PSNR (dB)** \\ \hline Our method & 40.12 \\ Method [36] & 35.63 \\ Method [37] & 36.09 \\ Method [38] & 35.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the imperceptibility of different watermark embedding methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attacks** & **No Attacks** & **Gaussian Noise (v = 0.1)** & **White Noise** & **Rotation by \(10^{\circ}\)** & **JPEG Compression** & **Gaussian Filtering** \\ \hline Extracted & & & & & \\ ’SCU’ & & & & & \\ watermark & & & & & \\ Extracted & & & & & \\ ’ZY’ & & & & & \\ watermark & & & & & \\ Extracted & & & & & \\ ’TL’ & & & & & \\ watermark & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Watermark extraction result after adding attacks. Figure 7: Image of the carrier after the attack. The robustness of a watermarking algorithm refers to the high similarity between the extracted watermark and the original watermark after various attacks on the embedded watermarked image. Normalized correlation coefficient (NC) and bit error rate (BER) are common measures of robustness. They are defined as shown below. \[NC=\frac{\sum\limits_{p=1}^{p}\left(m_{p}-u_{m}\right)\left(m_{p}^{*}-u_{m^{*}} \right)}{\sqrt{\sum\limits_{p=1}^{p}\left(m_{p}-u_{m}\right)^{2}}\sqrt{\sum \limits_{p=1}^{p}\left(m_{p}^{*}-u_{m^{*}}\right)^{2}}} \tag{19}\]  
\[BER=\frac{\sum\limits_{p=1}^{p}\left(m_{p}\oplus m_{p}^{*}\right)}{P}\]  
\(m_{p}\) and \(m_{p}^{*}\) are the original watermark and the extracted watermark, respectively. \(u_{m}\) and \(u_{m}^{*}\) are the average of the original watermark and the extracted watermark. In order to verify that the proposed watermarking algorithm has better performance compared with existing ones, similar algorithms [36; 37] are selected for comparison with it in this paper. We compare the robustness by calculating the NC and BER values as shown in Table 3.
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
###### Abstract  
Nowadays, wireless communication technology is evolving towards high data rates, a low latency, and a high throughput to meet increasingly complex business demands. Key technologies in this direction include multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM). This research is based on our previous work DMCNet. In this article, we focus on studying the deep learning (DL) application of neural networks to solve the reception of single-antenna OFDM signals. Specifically, in multi-antenna scenarios, the channel model is more complex compared to single-antenna cases. By leveraging the characteristics of DL, such as automatic learning of parameters using deep neural networks, we treat the reception process of MIMO-OFDM signals as a black box and utilize neural networks to accomplish the signal reception task. Moreover, we propose a data-driven multi-pilot convolution neural network for MIMO-OFDM receivers (DMCNet). By incorporating complex convolution and complex fully connected structures, we design a receiver network to recover the transmitted signals from the received signals. We validate the accuracy and robustness of DMCNet under different channel conditions, comparing the bit error rates with different schemes. Additionally, we discuss the factors influencing various channel effects. At the same time, we also propose a model-driven scheme, DMCNet-pro, which has a higher accuracy and fewer parameters in some scenarios. The experimental results demonstrate that the DL-based reception scheme exhibits promising feasibility in terms of accuracy and interference resistance when compared to traditional approaches.  
MIMO; OFDM; multiple pilots; neural networks; complex convolution Article
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
###### Abstract  
Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance.  
YOLOv8; object detection; attention mechanism; BiFPN; feature fusion Article
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 2 WSN Coverage Optimization Based on PSO
### Optimization Model of WSN Coverage  
In this work, we use the joint monitoring probability of the node set to measure whether each pixel of the target area is covered. \(P_{th}\) is the expected coverage threshold, then:  
\[P_{cov}\big{(}Cov_{j}\big{)}=\ \left\{\begin{array}{ll}1,&\quad\quad if\ P_{cov} \big{(}Cov_{j}\big{)}<\ P_{th}\\ 0,&\quad\quad if\ P_{cov}\big{(}Cov_{j}\big{)}\ \geq\ P_{th}\end{array}\right. \tag{6}\]  
where \(P_{cov}\big{(}Cov_{j}\big{)}=1\) indicates that the target pixel \(j\) is overwritten; otherwise, the target pixel \(j\) is not overwritten [23].  
The effective coverage points are calculated by the previous formula, and the number of sampling points of the whole target area is \(l\times w\), so the coverage rate \(\phi\) of the monitoring area A is calculated with the following formula:  
\[\phi=\frac{\sum_{j}P_{cov}\big{(}Cov_{j}\big{)}}{l\times w} \tag{7}\]  
The objective function in this work is the monitoring area coverage rate \(\phi\), the node in the monitoring area establishes the wireless sensor network coverage optimization model according to the previous formula.  
\[maximize\ \phi=\frac{\sum_{j}P_{cov}\big{(}Cov_{j}\big{)}}{l\times w} \tag{8}\]  
where \(\left\{\begin{array}{ll}0\leq x_{i}\ \leq l\\ 0\leq y_{i}\leq w\end{array}\right..\)
## 3 2-D Frequency-Domain Focusing Based on BiCoT-ESRM
### Modified Azimuth Preprocessing
To this end, the following azimuth trajectory scaling (ATS) is formulated  
\[v_{e}t^{\prime}=2\sin\frac{\omega_{e}t}{2}R_{e}. \tag{18}\]  
After ATS, the azimuth phase can be expressed as  
\[\phi_{2}\big{(}f_{\tau},t^{\prime}\big{)}= -\pi\frac{f_{\tau}^{2}}{k_{r}}-2\pi\frac{f_{0}+f_{\tau}}{c}R_{bi}^{ Eq}\big{(}t^{\prime}\big{)}\] \[+\pi K_{rot}^{bi}t^{\prime 2}-2\pi f_{dc}\big{(}f_{\tau}\big{)}t^{ \prime} \tag{19}\]  
where \(R_{bi}^{Eq}\big{(}t^{\prime}\big{)}\) is given by  
\[R_{bi}^{Eq}\big{(}t^{\prime}\big{)} =R_{T0s}+\sqrt{R_{R0}^{2}+v_{e}^{2}{t^{\prime}}^{2}-2R_{R0}v_{e}t ^{\prime}\sin\theta_{e}}\] \[H_{1}\big{(}f_{\tau},t\big{)}=\exp\Big{\{}-j2\pi f_{dc}\big{(}f_{\tau}\big{)} \bigg{(}\frac{2R_{e}}{v_{e}}\sin\frac{\omega_{e}t}{2}\bigg{)}\Big{\}} \tag{13}\]0  
It can be observed from (20) that the equivalent radar movement is scaled to a uniform linear motion with velocity \(v_{e}\) by ATS, where azimuth variance of motion parameters has been removed. Noted that ATS can be implemented by an azimuth interpolation procedure. After azimuth trajectory scaling, zero padding, azimuth FFT and phase compensation can be subsequently employed according to the original two-step method [56]. The compensation factor in azimuth time domain is given by  
\[H_{1}\big{(}f_{\tau},t\big{)}=\exp\Big{\{}-j2\pi f_{dc}\big{(}f_{\tau}\big{)} \bigg{(}\frac{2R_{e}}{v_{e}}\sin\frac{\omega_{e}t}{2}\bigg{)}\Big{\}} \tag{13}\]1The above preprocessing is essentially a convolution process. Therefore, the echo is transformed into 2-D frequency domain by an azimuth FFT and the following phase function is introduced to offset the convolution operation. \[H_{1}\big{(}f_{\tau},t\big{)}=\exp\Big{\{}-j2\pi f_{dc}\big{(}f_{\tau}\big{)} \bigg{(}\frac{2R_{e}}{v_{e}}\sin\frac{\omega_{e}t}{2}\bigg{)}\Big{\}} \tag{13}\]2  
where \(f_{t}\) is the azimuth frequency variable. After the above preprocessing, the effective pulse repetition frequency (PRF) is changed, and azimuth aliasing is removed in the azimuth frequency domain [56]. Moreover, the azimuth variance caused by circular orbit trajectory is also eliminated.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
###### Abstract  
The ambitious goals of decarbonization of the European economy by mid-century pose significant challenges, especially when relying heavily on resources whose nature is inherently intermittent, specifically wind and solar energy. The situation is even more serious in isolated regions with limited connections to larger power grids. Using EnergyPLAN software, three scenarios for 2023 were modeled: a diesel-only system, the current hybrid renewable system, and an optimized scenario. This paper evaluates the performance of the usual generation system existing in isolated systems, based on fossil fuels, and proposes an optimized system considering both the cost of the system and the penalties for emissions. All this is applied to the case study of the island of El Hierro, but the findings are applicable to any location with similar characteristics. This system is projected to reduce emissions by over 75% and cut costs by one-third compared to the current configuration. A system has been proposed that preserves the economic viability and reliability of diesel-based systems while achieving low emission levels. This is accomplished primarily through the use of renewable energy generation, supported by pumped hydro storage. The approach is specifically designed for remote regions with small isolated grids, where reliability is critical. Importantly, the system relies on appropriately sized renewable installations, avoiding oversizing, which--although it could further reduce emissions--would lead to significant energy surpluses and require even more efficient storage solutions. This emphasizes the importance of implementing high emission penalties as a key policy measure to phase out fossil fuel generation.  
Pareto front; single objective collapse; energy planning; standalone system; renewable hybrid system; fossil generation +
Footnote †: journal: Applied Sciences  
Article
# Heterodyne Angle Deviation Interferometry in Vibration and Bubble Measurements
## 4 Discussion  
This method, named Heterodyne Angle Deviation Interferometry (HADI), is demonstrated in small vibration and bubble measurements. In the small vibration measurement, due to the common-path structure, the environmental disturbances from all vibrant sources will not influence the interference signal, but the vibration will change the angle deviation of the test beam. Therefore, the amplitude of the signal is not changed, but the vibration information is preserved in the phase of interference. The anti-influence characteristics of the common-path optical setup assists us in measuring the vibration level of the speaker either with the influence of environmental interference or not. In our experiment, we could observe the speaker's vibration situation in real time. Therefore, the feasibility of this method for vibration measurement is demonstrated. In the bubble measurements, the bubble can deflect the test beam from the resonant angle of the SPR. The structure can minimize the effect of environmental disturbance and can be easily used to measure the phase shift due to the angle deviation. The minimum bubble size that can be detected is 0.2 mm, and the maximum counter frequency is 1 kHz.
## 1 Introduction
Although the data-fusion approaches can be used to generate high spatial and temporal data, the accuracy of synthetic data is reduced with the increase of days from the date of a base image. Since the data-acquisition frequency of wide field-of-view satellites like HJ and GF-1 is higher than that of Landsat, the gaps in the temporal observations of wide field-of-view satellites are limited. Such small gaps could be effectively filled using the synthetic data by fusing HJ and GF-1 data with MODIS data. Indeed, [PERSON] _et al._[40] demonstrated both ESTARFM and STDFA methods can be applied to combine HJ charge coupled device (CCD) and GF-1 wide field-of-view camera (WFV) with MODIS reflectance. As a result, combining data acquired by wide field-of-view satellites and synthetic data, we can obtain high spatial and temporal resolution remote sensing data. However, the application of STDFA mainly focus on the generation and use of multi-temporal synthetic medium-resolution data [37, 38, 40], while daily synthetic medium-resolution data are needed in the time series analysis for phenology monitoring and crop mapping. To reach this goal, the objectives of this study are (1) to propose a method for the reconstruction of daily 30 m remote-sensing data from HJ CCD, GF-1 WFV, Landsat, and MODIS data; (2) to extract phenology using the reconstructed daily 30 m data; and (3) to test the ability of those data in crop mapping.
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 2 System Model and Assessment Formulation
### Weight Calculation and Quantitative Assessment
#### 2.2.5 Quantitative Assessment Algorithm Based on TOPSIS  
The number of classes in each attribute is different, and the significance does not have the evaluation function. In light of this situation, we propose a learning algorithm, summarized in Algorithm 1, based on the attribute weight and the TOPSIS method for quantitative assessment.  
In RST, the information system can be concisely expressed in matrix format as follows:  
\[X=\left[\begin{array}{cccc}x_{11}&x_{12}&\cdots&x_{1m}\\ x_{21}&x_{22}&\cdots&x_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ x_{n1}&x_{n2}&\cdots&x_{nm}\end{array}\right] \tag{8}\]  
where \(X\) is the attribute matrix, \(X_{ij}\) is the observed value of each element for \(i=1,2\cdot\cdot\cdot n\), and \(j=1,2\cdot\cdot\cdot m\).  
In order to eliminate anomalies with different measurement units and scales, the initial matrix should be standardized. The standardized matrix is expressed as follows:  
\[Z=\left[\begin{array}{cccc}z_{11}&z_{12}&\cdots&z_{1m}\\ z_{21}&z_{22}&\cdots&z_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ z_{n1}&z_{n2}&\cdots&z_{nm}\end{array}\right],z_{ij}=-\frac{x_{ij}}{\sqrt{ \sum\limits_{i=1}^{n}x_{ij}^{2}}} \tag{9}\]  
The positive and negative ideal reference points can be outlined as follows:  
\[\begin{array}{l}Z^{+}=\left(\begin{array}{cccc}\max\{z_{11},z_{21},\cdots,z_{n1}\},\\ \max\{z_{12},z_{22},\cdots,z_{n2}\},\\ \cdots,\max\{z_{1m},z_{2m},\cdots,z_{nm}\}\end{array}\right)\\ =\left(Z_{1}^{+},Z_{2}^{+},\cdots,Z_{m}^{+}\right)\\ Z^{-}=\left(\begin{array}{cccc}\min\{z_{11},z_{21},\cdots,z_{n1}\},\\ \min\{z_{12},z_{22},\cdots,z_{n2}\},\\ \cdots,\min\{z_{1m},z_{2m},\cdots,z_{nm}\}\end{array}\right)\\ =\left(Z_{1}^{-},Z_{2}^{-},\cdots,Z_{m}^{-}\right)\\ \end{array} \tag{11}\]  
The distances to the positive and negative ideal reference points are calculated using the following formulas:\[D_{i}^{+}=\sqrt{\sum_{j=1}^{m}w_{j}\Big{(}Z_{j}^{+}-z_{ij}\Big{)}^{2}},D_{i}^{-}= \sqrt{\sum_{j=1}^{m}w_{j}\Big{(}Z_{j}^{-}-z_{ij}\Big{)}^{2}} \tag{12}\]  
where \(w_{j}\) is the weight of each attribute as determined by the proposed method and \(D_{i}^{+}\) and \(D_{i}^{-}\) are the distances to the positive and negative ideal reference points, respectively.  
The closeness coefficient (CC) of each element can be calculated as follows:  
\[CC_{i}=\frac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}} \tag{13}\]  
After the CCs have been calculated, the HT threat can be determined. The higher the value of the CC, the higher the HT threat is.