esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The motion of a continuously operating reference station is usually dominated by the long-term crustal motions of the tectonic block on which the station is located. Monitoring changes in the coordinates of reference stations located at tectonic plate boundaries allows for the calculation of velocity fields that reflect the spatial and temporal characteristics of the region. This study analyzes the spatiotemporal relationships of regional reference frame points with GNSS data from 25 reference stations in Sichuan, China, from 2015 to 2021. The common mode errors are extracted and eliminated by principal component analysis. A time series function model is developed for the reference stations and their constituent baselines for calculating the velocity field. Subsequently, the spatiotemporal characteristics of the regional reference frame in Sichuan is analyzed by a stochastic model. The results show that the influences of the common mode error on the horizontal and vertical directions of the reference stations is 2.5 mm and 4.3 mm, respectively. Generally, the horizontal motion of the reference stations in the Sichuan region tends to be in the southeast direction and the vertical motion trend is mainly uplifting. The east-west and vertical components of the baseline tend to be shortened, and the random influence among the reference stations is larger in the north-south and east-west directions--0.39 mm and 0.54 mm, respectively. Polynomial functions are more appropriate for constructing the fitted random influence covariance model.  
Keywords:regional reference frame; common mode errors; Sichuan spatiotemporal characteristics; time series +
Footnote †: journal: Appl. Sci. 2024, 14, 432. [[https://doi.org/10.3390/app14010432](https://doi.org/10.3390/app14010432)]([https://doi.org/10.3390/app14010432](https://doi.org/10.3390/app14010432))
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 3 A Method of Speaker-Independent Bone-Conducted Speech Enhancement  
### The Overall Framework of the Proposed BSEGAN-SI Method  
The framework of the BC speech enhancement method is shown in Figure 1. In this paper, the generation countermeasure network is used as the BC speech enhancement model. The WORLD vocoder [24] is used to extract the BC and AC speech feature parameters spectrum envelope (Sp) and convert them into mel-cepstral coefficients (MCEPs). Specifically, the MCEPs are the mel-log spectral approximation (MLSA) parameters of mel-frequency cepstral coefficients (MFCCs) [25]. Then the converted _MCEPs_ feature is applied to network model training to learn the mapping relationship between BC and AC speech; the fundamental frequency \(F_{0}\) is extracted for Gaussian normalization. The main goal of the generator in this framework is to model the nonlinear mapping relationship between the source speech and target speech, learn the distribution of all semantic information data of the target speech, and recover the missing information components of the source speech.  
After model training, in the blind enhancement phase, we first use the vocoder to extract the MCEPS features of BC speech to be enhanced and then input them into the trained GAN model. The \(F_{0}\) features of BC speech are converted by logarithm gaussian normalization (LGN) [26]. According to reference [27], \(Ap\) does not significantly affect speech quality, so we directly use the extracted \(Ap\) features of BC speech without transformation. Finally, according to the obtained speech features, we directly use the WORLD Vocoder to quickly synthesize the enhanced BC speech.
In this study, we propose a plasmonic metasurface design, based on the SSPP principle, which operates entirely on a flat metamaterial surface. This structure is capable of absorbing EM waves over a wide frequency range, from 12.30 to 14.80 GHz. The unit cell of the plasmonic metasurface consists of multiple split rings that have a circular shape, providing excellent responses to various polarizations and incident angles of incoming EM waves. Notably, our plasmonic metasurface was fabricated using standard PCB technology without specialized materials or external electrical components. The proposed structure was planar and fabricated from circular copper strips on an FR4 dielectric substrate, making it easy to manufacture with high stability. With an absorption exceeding 90% across a wide wavelength range in the K\({}_{\text{u}}\)-band, this plasmonic metasurface has potential applications in radar stealth, satellite communication, and spectral imaging.
# Deep Learning-Based FSS Spectral Characterization and Cross-Band Migration
## 1 Introduction
[18] used Goog-LeNet-Inception-V3 to predict the phase of a 28 \(\times\) 8 meta-atom with an accuracy of about 90%, realizing transfer learning from the field of image processing to the field of metasurface prediction. [PERSON] et al. [19] achieved transfer learning from cheap and simple source metasurface data (5 \(\times\) 5) to intensive and complex data (20 \(\times\) 20) by an encoder-decoder network. This transfer can speed up the metasurface design process and save target data. Previous studies have achieved the efficient design of FSSs within a defined frequency range, which is typically limited by the dataset. If an FSS needs to be designed in other frequency ranges, it becomes necessary to collect data and retrain the neural network again by the usual approach. However, collecting data is the most time-consuming and resource-intensive step, and how to improve data utilization efficiency and save computing resources remains the difficulty in developing FSS design methods based on deep learning. In this paper, the influence of FSS parameters on its spectral characteristics is analyzed in detail. The theoretical basis for transferable learning in the FSS design method, combining deep learning with intelligent algorithms, is clarified. More convolutional neural networks are implemented for transfer learning from terahertz to millimeter-wave bands. Thisresearch provides a reliable reference for the cross-fusion of deep learning and FSSs. FSS design has been further developed in an efficient and universal way.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 3 Improved Burst OFDM Synchronization Method
### Symbol Timing Synchronization Method Based on 2-Bit Non-Uniform Quantization
#### 3.2.4 Computational Complexity Analysis  
This section analyzes the computational complexity to evaluate the advantages of the proposed symbol timing synchronization method in hardware implementation. The main computational complexity of the symbol synchronization lies in the calculation of the correlation value between the received signal and the local training sequence. The traditional calculation of \(V_{n}\) requires \(4L\) multiplication operations and \(4L-1\) addition operations for each sampling point. The computational complexity reduces to \(4L\) XOR operations and \(4L-1\) addition operations for the synchronization method based on 1-bit quantization. However, this method ignores the magnitude information, resulting in significant synchronization performance loss. In contrast, when the 2-bit quantization method is used, the simplified calculation method given by Equations (13) and (14) can be used, wherein the calculation of \(V(n)\) requires \(4L\) XOR operations and \(8L-1\) addition operations. In practical systems, a larger bit width is necessary to achieve good synchronization performance. Meanwhile, the hardware implementation complexity increases linearly with the bit width. The computational complexity comparison of different correlation operations is shown in Table 2. The proposed symbol synchronization based on the 2-bit non-uniform quantization method performs well in computational complexity.
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Strategies for Enhancing ZnO-Based Nanomaterials Antibacterial Activity
#### 4.4.3 Noble Metals Doping into ZnO  
Due to the formation of Schottky barriers at the metal-semiconductor interface, noble metal ions (such as Au, Ag, Pd, etc.) doped on the ZnO surface are considered to be excellent photogenerated electron traps. In addition, noble metals delay electron-hole recombination by preventing photoexcited electrons from returning to the ZnO surface, which greatly enhances the photocatalytic antibacterial ability of the composites [194]. Of all the precious metals, silver is the most stable and suitable dopant with good thermal conductivity and  
Figure 16: TEM images of (**a**) pure, (**b**) Fe doped, and (**c**) capped ZnO nanoparticles. Reprinted/adapted with permission from Ref. [192]. Copyright © 2022, Elsevier B.V.  
Figure 17: XRD patterns of various ZnO-based nanomaterials. Reprinted/adapted with permission from Ref. [192]. Copyright © 2022, Elsevier B.V.  
electrical conductivity, which will better play the photocatalytic effect of the composite material. Therefore, it has potential as a catalyst. The surface plasmon resonance (SPR) properties of silver also contribute to visible light absorption and subsequent electron-hole pair generation for the degradation of contaminants in water and for antibacterial [195].  
[PERSON] et al. [184] reported the synthesis of a series of ZnO/Ag\({}_{2}\)MoO\({}_{4}\)/Ag(ZAA) samples with theoretical molar ratios of ZnO and Ag\({}_{2}\)MoO\({}_{4}\) of 20:1, 30:1, and 60:1 by ultrasonic-assisted hydrothermal synthesis, and named the corresponding products as ZAA-20, ZAA-30, and ZAA-60 to investigate the optimal Ag\({}_{2}\)MoO\({}_{4}\)/Ag loadings. As shown in Figure 18, the characteristic diffraction peaks of ZnO and Ag2 Mo4 can be clearly found in the XRD patterns, which indicates that the ZnO/Ag\({}_{2}\)Mo\({}_{4}\) composite was successfully synthesized. The sharp diffraction peaks reveal the ultra-high crystallinity of the ZnO-based nanocomposites. In addition, with the increase of the molar ratio of ZnO to Ag\({}_{2}\)MoO\({}_{4}\), the diffraction peak intensity of ZnO on the (002) and (110) crystal planes gradually weakened, while the diffraction peak intensity of Ag\({}_{2}\)MoO\({}_{4}\) gradually increased. The antibacterial properties of ZnO nanosheets and ZAA nanocomposites against different contents of G\(-\)_E. coli_ and G\(+\)_S. aureus_ were evaluated by the visible light electroplating counting method. In Figure 19, the bacterial cell numbers of all nanocomposites were shown to decrease with increasing contact time, and the photocatalytic antibacterial activities of the four ternary ZAA nanocomposites were much better than that of pure ZnO sheets. The experimental results demonstrate that the addition of noble metal Ag will significantly improve the antibacterial properties of ZnO nanomaterials.  
Figure 18: XRD patterns of ZnO, Ag\({}_{2}\)MoO\({}_{4}\)/Ag, ZA, and ZAA nanocomposite with different proportions. Reprinted/adapted with permission from Ref. [184]. Copyright © 2022, Elsevier B.V.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 6 Performance Evaluation
### Malicious RSUs Detection Success Rate  
As depicted in Figure 6, our observations reveal distinct trends in the detection rates of 10 malicious RSU nodes over a 60-min interval. Notably, as the trust threshold increases, the scheme's capability to distinguish abnormal nodes also grows. In this context, our CRMWSL scheme outperforms both the MWSL and TSL schemes in successfully detecting malicious RSUs.  
More specifically, when the detection success threshold is set to 0.45, the CRMWSL scheme achieves a 100% detection rate, which significantly surpasses the performance of the MWSL and TSL schemes. These results underscore the CRMWSL scheme's ability to reliably identify potential security threats, thereby enhancing the overall security level of the system.  
Figure 5: The change rate of reputation value of malicious RSUs.
# A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography
## 5 Numerical Simulation and Analysis
### Case 2  
The robustness of image reconstruction algorithm is another important index to evaluate the algorithm. In practical application, the ECT system will be affected by noise when collecting capacitance data. Small input errors will cause the output result to deviate significantly from the true result. Consequently, to make the simulation results of the RELM algorithm more convincing, we add 5% and 10% random noise to the original capacitance to assess the RELM algorithm. The phantom objects to be reconstructed and the imaging results are presented in Figure 7 and Figures 10 and 11. We executed 100 computations, and the image error mean is displayed in Table 7. The noise level is defined by the following equation:  
\[\eta=\frac{\left\|\hat{C}-C\right\|}{\left\|C\right\|}\times 100\% \tag{26}\]  
where \(\eta\) is the capacitance noise level, \(\hat{C}\) is the noisy capacitance data, and \(C\) is the raw capacitance data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** & **(g)** \\ \hline LBP & 40.44 & 56.60 & 41.47 & 45.70 & 38.45 & 46.88 & 57.41 \\ TR & 32.21 & 51.99 & 71.78 & 92.09 & 59.92 & 42.67 & 57.19 \\ TSVD & 23.64 & 47.11 & 72.15 & 121.49 & 44.31 & 37.70 & 44.20 \\ CG & 24.67 & 25.50 & 29.13 & 30.69 & 32.15 & 25.39 & 20.61 \\ Landweber & 22.54 & 21.78 & 27.99 & 30.66 & 35.11 & 28.99 & 26.24 \\ SIRT & 20.05 & 21.08 & 27.51 & 29.85 & 32.31 & 24.54 & 28.07 \\ GPR & 9.06 & 8.79 & 9.64 & 8.69 & 10.31 & 7.62 & 7.01 \\ ELM & 9.81 & 7.63 & 8.45 & 7.61 & 8.78 & 7.93 & 7.19 \\ RELM & 2.13 & 2.06 & 1.09 & 1.68 & 1.49 & 1.84 & 1.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Imaging errors (%).  
From the reconstruction results in Figures 10 and 11, we find that under the noise levels of 5% and 10%, the imaging results of phantom objects are still satisfactory, which shows that the RELM algorithm has strong robustness and stability under different noise levels. As can be seen quantitatively from the image error mean values in Table 7, the image errors still keep a small value under 10% noise level. The image error means of phantom object (a)-phantom object (g) are 4.51%, 4.16%, 4.79%, 3.61%, 4.27%, 3.79%, and 3.43%, respectively. The conclusion of the qualitative analysis and quantitative analysis shows that the RELM algorithm has a strong anti-noise performance under different levels of noise for various complex conditions.
## 4 Analysis  
### Relationship Between LCFs and Historical Landslides  
According to the FR analysis (Table 2), several factors were revealed to influence the occurrence of landslides to varying degrees. Vegetation coverage (NDVI) exhibited the highest FR value (3.70) within the range of 0.32 to 0.64. Altitude also showed a significant impact. The 2196 to 2820 m range obtained the max FR value of 2.37. Among the topographic factors, the slope angle (31.9\({}^{\circ}\) to 42.23\({}^{\circ}\)) had an FR value of 1.64, while the FR value for areas within 0 to 500 m from roads reached as high as 2.34. Other factors, such as the Topographic Wetness Index (TWI), Stream Power Index (SPI), and curvature, also displayed varying degrees of influence.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**LCFs** & **Class** & **Area Count** & **Landslides** & **Percent of Area (E)** & **Percent of Landslide (F)** & **FR = F/E** & **RF (\%)** & **Max** \\ \hline \multirow{4}{*}{Altitude} & 2820–4242 & 126,099 & 154 & 15\% & 26\% & 1.79 & 37\% & 2.37 \\  & 2196–2820 & 199,557 & 322 & 23\% & 55\% & 2.37 & 49\% & \\  & 1638–21
## 1 Introduction
### State of the Art
#### 1.1.4 Other Concepts  
Furthermore, there are numerous patents on related technologies. [PERSON] [8] proposes the idea of harvesting wind, wave, and solar energy and storing it in batteries to be used for propelling a ship, so it can maintain an appropriate speed on a route. [PERSON] [9] describes the use of a remote-controlled vessel: While sails propel the vessel, energy is harvested by a water turbine and stored as hydrogen. [PERSON] [10] proposes vessels using sails for propulsion. The vessels are propelled along a linear path. The harvested energy is then stored as hydrogen in inflatable containers. [PERSON] [11] patented the idea of an improved method of propulsion for floating systems that are not moored. An energy recovery method is also mentioned. [PERSON] [12] describes an idea to use modified sailboats (the sails are kept upright with the help of lighter-than-air balloons instead of masts) for the capture and conversion of wind energy to hydrogen. The generated hydrogen is stored in metal hydride. The transport of stored energy in chemical form is an important step. The supply  
Figure 3: Concept of the Wind Hunter Project; Reprinted with permission from Ref. [6]. 2020, MOL.  
Figure 2: Farwind energy ship [5].  
chain of hydrogen on a pilot scale is demonstrated in the project HySTRA [13]. Within the project, a liquefied hydrogen carrier ship is being built as well as gaseous hydrogen tanks for transport on roadways.
###### Abstract  
As a pillar industry of China's economy, the real estate sector has been challenged by the increasing prevalence of housing vacancies, which negatively impacts market stability. Traditional vacancy rate estimation methods, relying on labor-intensive surveys and lacking official statistical support, are limited in accuracy and scalability. To address these challenges, this study proposes a novel framework for assessing residential community-level housing vacancy rates through the integration of multi-source data. Its core is based on night-time lighting data, supplemented by other multi-source big data, for housing vacancy rate (HVR) estimation and practical validation. In the case study of Longquanyi District in Chengdu City, the main conclusions are as follows: (1) with low data resolution, the model estimates a root mean square error (RMSE) of 0.14, which is highly accurate; (2) the average housing vacancy rate (HVR) of houses in Longquanyi District's residential community is 46%; (3) the HVR rises progressively with the increase in the distance from the city center; (4) the correlation between the HVR of Longquanyi District and the house prices of the area is not obvious; (5) the correlation between the HVR of Longquanyi District and the time of completion of the communities in the region is not obvious, but the newly built communities have extremely high HVR. Compared to the existing literature, this study innovatively leverages multi-source big data to provide a scalable and accurate solution for HVR estimation. The framework enhances understanding of urban real estate dynamics and supports sustainable city development.  
residential vacancy rate; multi-source data; night light data; real estate +
Footnote †: journal: Applied sciences
# In Search of the Densest Subgraph
## 3 Measures of Graph Density and Associated Results
### Polynomial-Time Solvable Cases
#### 3.1.2 \(k\)-Core: Subgraph with Largest Minimum Degree  
Another reasonable density concept is obtained if we use the _minimum degree_ of the subgraph, rather than its average degree. In some sense, this provides a worst-case guarantee for the subgraph density, rather than an average-case guarantee.  
**Definition 1**.: _The \(k\)-core of a graph is the largest subgraph in which every vertex has degree at least \(k\). The largest value of \(k\) for which the graph has a non-empty \(k\)-core is called the degeneracy of the graph._  
The concept was originally introduced by [PERSON] and [PERSON] [8] in 1970, and then analyzed in many other papers. The \(k\)-core of a graph is uniquely determined for every \(k\), due to the fact that the property of having minimum degree at least \(k\) is closed with respect to the union of subgraphs. For different values of \(k\), the \(k\)-cores form a nested structure.  
A great advantage of the \(k\)-core is that it is very easy to find algorithmically. The principle of the algorithm can be described in one sentence: delete all nodes with degree \(<k\), and repeat this in the remaining graph, until either all nodes have degree \(\geq k\), or the remaining graph is empty. (In the latter case, we usually say that there is no \(k\)-core for the given value of \(k\), rather than saying that the \(k\)-core is empty). Note that the repetition is needed because the removal of nodes may decrease some degrees in the remaining graph. With a little further sophistication, this algorithm can be implemented in linear time, using the so-called bucket queue data structure (see [PERSON] and [PERSON] [9]).  
The simplicity of finding the optimal \(k\)-core leads to the question: Why do we need any other, more complicated, density concepts at all, if the \(k\)-core is so easy to find? A possible answer is that the \(k\)-core can behave counter-intuitively, with respect to the intuitive concept of the edge density, introduced in Section 3.1.1. Specifically, [PERSON] and [PERSON] [10] demonstrated that the \(k\)-core can have lower edge density than an \(\ell\)-core with \(\ell<k\) in the same graph, even though we intuitively expect that a core with higher \(k\) value should be more dense.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
###### Abstract
Lastly, the bistatic configuration can allow for a larger target radar cross-section depending on the bistatic angle--typically 180\({}^{\circ}\). This configuration is known as forward scatter radar (FSR) [27]. This work explores Doppler extraction through self-mixing processing [28] and filtering of the received direct and target-reflected signals. The bistatic baseline is defined as the distance between the transmitter and receiver [23]. At the time of baseline crossing, the direct and reflected signals arrive at the receiver at virtually the same time. As a result, zero Doppler is detected. However, additional receivers at discrete separation distances from the initial receiver will extract discrete Doppler values, allowing for unique target identification. Previous FSRs implementing the self-mixing technique have been designed with a 4G Long Term Evolution (LTE) IOO [29] and an E-band frequency modulated continuous wave (FMCW) transmitter [30]. These cases, however, only looked at single targets. This paper will explore multiple targets in conjunction with multiple receiver elements. This article is outlined as follows. A review of Mie scattering, bistatic radar geometry, and forward scatter RCS (FS-RCS) will be presented in Section 2. Next, the mathematical and time-frequency behavior of a baseline crossing event (BCE) will be explored in Section 3. Section 4 will describe the array geometry and self-mixing processing towards Doppler extraction. Section 5 will then present the simulation design parameters and a discussion of the results. Section 6 will describe the effects of noise and interference. Finally, the concluding remarks will be presented in Section 7.