esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Formulation and Brief System Architecture
### Primary Layer
#### 4.1.1 Data Collection  
Our proposed data-collection scheme focuses on data generated by the individual patient and overall data generated by a hospital. An interaction between patient and smart device can provide a better outcome to the new healthcare system and explore more information to diagnose a disease. Presently, a lot of biosensors, motion sensors, and wearable devices including smartphone and smartwatches are being developed to measure a patient's heart rate, temperature, breath rate, ECG, back pain, and many other medical parameters. Those devices can compute a user's biological condition and transmit the result to any server. Another important fact related to health is a patient's emotional state. We adopt an advanced emotion-detecting technology known as EQ radio, which is used to find out a user's emotional state. The patient's medical history will be collected from the hospital server. A doctor's note is collected from e-prescription. The EHR also provides many more data about an individual patient. Another significant source of data is the medical clinic and hospital. Those hospital data are gathered from the medical hospital server. A cloud-based environment can provide some social networking facilities as well as secure storage and high computing capabilities. We create interactions between all patients and doctors through cloud-based social networking to collect similar patients' public health status as well as familial genetic syndromes. Altogether, all those data are sent to a high-performance computer server.
# Settlement Prediction of Footings Using \(V_{s}\)
## 3 Centrifuge Study
### Results and Discussion
#### 3.2.4 Modulus Profiles  
In order to check modulus profiles utilized in settlement predictions, numerically computed modulus profiles mobilized during the settlement predictions are shown with respect to normalized depth (\(z/B\)) in Figure 10. Each modulus profile was obtained at each applied pressure (\(q\)) from 100 kPa to 1600 kPa, and the normalized settlements (\(s/B\)), corresponding to the applied pressure are presented as a percentage. For determination of initial modulus profile, the shear wave velocity profiles measured before the 2 nd loading of each \(L/B\) ratio were utilized. General trends in the modulus profile transitions were observed such that the modulus increased under low applied pressures and decreased near the surface above 800 kPa. The threshold depth, where the modulus becomes smaller than the previous value, deepened with increasing \(L/B\) ratio. This phenomenon could occur due to the shapes of the \(I_{z}\) derived from the elasticity theory. To sum up, the increase in modulus by confining pressure and modulus reduction by strain level are properly modelled in the developed settlement prediction method considering the shape of \(I_{z}\).  
Figure 8: Comparisons of load-settlement curves with predictions (\(L/B\) = 2.5): (a) 1 st loading; (b) 2 nd loading.  
Figure 9: Comparisons of load-settlement curves with predictions (\(L/B\) = 5.0): (a) 1 st loading; (b) 2 nd loading.
## 3 Methodology
### One-Dimensional Convolution Neural Network
#### 3.1.1 Convolution Layer  
Let's suppose that the input to \(x\)th layer is a tensor of order 3 with dimension: \(H^{x}\times W^{x}\times D^{x}\), where \(H\), \(W\), and \(D\) represent rows, columns, and channels of the layer, respectively. A convolution kernel layered on top of an input tensor at any spatial point eases the computation of the products of relevant components, and the summation of their products helps to obtain the convolution result at that particular location. The kernel moves from top to bottom or left to right to finish the one-dimensional convolution operation. Let us now consider the case where the stride is 1 (one) and there is no padding. As a result, output \(O\) (or \(I^{x+1}\)) is found in vector \(\mathbb{R}^{H^{x+1}\times W^{x+1}\times D^{x}}\), where \(H^{x+1}=H^{x}-H+1\), \(W^{x+1}=W^{x}-W+1\), and \(D^{x+1}=D\). The convolution technique can be mathematically written as:  
\[O_{I^{x+1},I^{x+1},d}=\sum_{i=0}^{H}\sum_{j=0}^{W}\sum_{d^{x}=0}^{d^{x}}f_{i,j, d^{x},d}\times l_{I^{x+1}+i,j^{x+1}+j,d^{x}}^{x}+B_{t} \tag{1}\]  
where \(B_{t}\) represents the bias term. Applying a bias term to a convolution process helps the output become positive at horizontal edges in one direction and negative in other areas.
## 3 Results and Discussions
### Future Vulnerability Assessment
#### 3.6.1 First Scenario  
Assuming the conditions in the first scenario (current operating rate), Figure 11a represents the future distribution of the vulnerability classes according to the DRASTIC method for the year 2030. The results show a decrease in the average-high vulnerability classes compared to the reference groundwater vulnerability map (2010) in the study area. Conversely, the zone of the low vulnerability class will increase in the southern part (west of Bekkaria city). This class increases by 2.5% compared to the map based on the data in 2010.
###### Abstract  
When performing in vivo imaging of live samples, it is a big challenge to penetrate thick tissues while still maintaining high resolution and a large field of view because of the sample-induced aberrations. These requirements can be met by combining the benefits of two-photon excitation, beam modulation and adaptive optics in an illumination path. However, the relationship between aberrations and the performance of such a microscopy system has never been systematically and comprehensively assessed. Here, two-photon Gaussian and Bessel beams are modulated as illumination beams, and how aberrations affect the thickness of the illumination beams is evaluated. It is found that the thickness variation is highly related to the azimuthal order of Zernike modes. The thickness of the two-photon Gaussian beam is more sensitive to Zernike modes with lower azimuthal order, while the thickness of the two-photon Bessel beam is more sensitive to the higher-azimuthal order Zernike modes. So, it is necessary to design a new strategy to correct aberrations according to the effects of different Zernike modes in order to maximize the correction capability of correctors and reduce the correction errors for those insensitive Zernike modes. These results may provide important guidance for the design and evaluation of adaptive optical systems in a two-photon excitation microscope.  
aberation analysis; beam modulation; two-photon excitation microscope; resolution +
Footnote †: journal:  
Article  
The Effects of Optical Aberrations to Illumination Beam Thickness in Two-Photon Excitation Microscopes
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 4 Camera-Based Non-Line-Of-Sight Effect Mitigation
### Image Segmentation-Based NLOS Mitigation Algorithm
This reduces/eliminates edges within the same structures, which leads to accurate edge detection between segments, as depicted in the fourth box. Accurate edge detection naturally leads to accurate image segmentation (fifth box). In the segmented image (fifth box), the sky is represented in blue colour, and the non-sky or obstacles are represented in dark grey colour. Figure 6: Flood-fill algorithm illustration. Pixels are filled from the centre of the image until an edge is reached following the eight arrows indicated in the first (left-most) box.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 5 Conclusions  
In this paper, we have addressed the HSTNs in which an energy-constrained terrestrial UT cooperates with a shadowed terrestrial IoT device in D2D communication. For this set-up, we have proposed PS-D2D and TS-D2D schemes for the energy-constrained UT to obtain sustainable energy for transmitting information to the shadowed IoT device. We have analyzed the system performance by deriving the closed-form expressions for the OP of both the UT and the IoT device, and have validated our theoretical analyses via Monte Carlo simulations. In the proposed schemes, the UT can utilize the energy harvested from the terrestrial relay to provide a relaying link for the IoT device, thereby improving the outage performance of the IoT device and expanding the terrestrial coverage area of the HSTNs.  
Conceptualization, [PERSON] and J.X.; methodology, Z.L.; software, J.X.; validation, [PERSON], J.X. and J.H.; formal analysis, Z.L.; investigation, Z.L. and J.X.; resources, J.X.; data curation, J.X.; writing--original draft preparation, [PERSON] and J.X.; writing--review and editing, [PERSON], J.X. and J.H.; visualization, [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 6: The OP of \(U_{2}\) versus the PS coefficient with different Nakagami coefficients under HS and AS conditions for the PS-D2D scheme.
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 3 Structural Damage Identification Methods
### Fractal Features Extraction  
Correlation dimension [37; 38; 39] is a common dimension used in fractals. Fractal correlation dimension not only represents the degree of density of the system in multidimensional space, but also can reflect the correlation degree between two points of the system. Hence,fractal correlation dimension is often used to describe the characteristic quantity of a system. Since the correlation dimension can reflect the irregularity and complexity of the signal to be analyzed, it can be used to distinguish the classification of structural health state and damage state. Among the methods to obtain the correlation dimension, the G-P algorithm [39,40] is simpler and more reliable than other methods.  
The principle of the G-P algorithm to solve the correlation dimension can be expressed by assuming a measured time data series with \(\{x_{1},x_{2},\ldots,x_{N}\}\), where \(N\) is the length of the time series signal, the phase-space reconstruction of the original signal data sequence is carried out by using the time-delay method, and its length is \(N_{m}\). Then, its pseudo-phase-space orbit can be represented by the following formula:  
\[\mathrm{X}_{i}=[x_{i},x_{i+\tau},x_{i+2\tau},\cdots,x_{i+(m-1)\tau}]^{T} \tag{9}\]  
where, \(\tau\) is the time delay; \(m\) is embedding dimension; \(i=1,2,\ldots,N_{m}\) and \(N_{m}=N-(m-1)\tau\).  
Then, the attractor correlation dimension is obtained by the following correlation integral:  
\[\mathrm{C}(\tau)=\frac{2}{N_{m}(N_{m}-1)}\sum_{N_{m}}\sum_{N_{m}}^{N_{m}}H(r- \|X_{i}-X_{j}\|) \tag{10}\]  
where, \(\mathrm{C}(r)\) is the correlation integral of signal; \(H\) is the Heaviside function, when \(u\geq 0\), \(H(u)=1\); when \(u<0\), \(H(u)=0\); \(r\) is the hyperspherical radius of phase space; \(\|X_{i}-X_{j}\|\) is the distance between two vectors:  
\[\|X_{i}-X_{j}\|=\|\sum_{i=0}^{m-1}\left(x_{i+l\tau}-x_{j+l\tau}\right)\|^{1/2} \tag{11}\]  
Here, the relevant integral \(\mathrm{C}(r)\) means the probability of a pair of vectors with an embedding space distance less than or equal to \(r\). In addition,  
\[\mathrm{C}(r)\;\mathrm{cor}^{\mathrm{D}_{2}}\ldots r\to 0 \tag{12}\]  
where, \(\mathrm{D}_{2}\) is correlation dimension, it can be solved by the following equation:  
\[\mathrm{D}_{2}=\lim(\ln\mathrm{C}(r)/\ln r)\ldots r\to 0 \tag{13}\]
# An Integration Strategy for Acoustic Metamaterials to Achieve Absorption by Design
###### Abstract  
As much of metamaterials' properties originate from resonances, the novel characteristics displayed by acoustic metamaterials are a narrow bandwidth and high dispersive in nature. However, for practical applications, broadband is often a necessity. Furthermore, it would even be better if acoustic metamaterials can display tunable bandwidth characteristics, e.g., with an absorption spectrum that is tailored to fit the noise spectrum. In this article we present a designed integration strategy for acoustic metamaterials that not only overcomes the narrow-band Achilles' heel for acoustic absorption but also achieves such effect with the minimum sample thickness as dictated by the law of nature. The three elements of the design strategy comprise: (a) the causality constraint, (b) the determination of resonant mode density in accordance with the input target impedance, and (c) the accounting of absorption by evanescent waves. Here, the causality constraint relates the absorption spectrum to a minimum sample thickness, derived from the causal nature of the acoustic response. We have successfully implemented the design strategy by realizing three structures of which one acoustic metamaterial structure, comprising 16 Fabry-Perot resonators, is shown to exhibit near-perfect flat absorption spectrum starting at 400 Hz. The sample has a thickness of 10.86 cm, whereas the minimum thickness as dictated by the causality constraint is 10.55 cm in this particular case. A second structure demonstrates the flexible tunability of the design strategy by opening a reflection notch in the absorption spectrum, extending from 600 to 1000 Hz, with a sample thickness that is only 3 mm above the causality minimum. We compare the designed absorption structure with conventional absorption materials/structures, such as the acoustic sponge and micro-perforated plate, with equal thicknesses as the metamaterial structure. In both cases the designed metamaterial structure displays superior absorption performance in the target frequency range.  
acoustic metamaterials; causality constraint; impedance matching; design strategy; mode density; oscillator strength +
Footnote †: journal: Applied Physics Letters
## 3 Inverse Solving Process
### Signal Separation Based on Robust Principal Component Analysis  
The robust principal component analysis (RPCA) [50] method can decompose the sound pressure matrix \(\mathbf{P}\) into a low-rank matrix and a sparse matrix. Combined with Equation (4), it can be expressed as:  
\[\mathbf{P}=\mathbf{G}\mathbf{Q}+\mathbf{E}=\mathbf{R}+\mathbf{E} \tag{19}\]  
\(\mathbf{R}\) is a low-rank matrix representing the signal component and \(\mathbf{E}\) is a sparse matrix representing the noise component. Considering the complex composition of real noise components, [PERSON] [51] modeled noise as a mixture of Gaussians (MoG), which can approximate any continuous distribution, and developed a generative RPCA model under a Bayesian framework.  
Assume that each component \(e_{i}\) in \(\mathbf{E}\) follows the MoG distribution, that is:  
\[e_{i}\sim\sum_{k=1}^{K}\pi_{k}N(e_{i}|\mu_{k},\tau_{k}^{-1}) \tag{20}\]  
\(K\) is the number of Gaussian distributions, \(\pi_{k}\geq 0\) is the weight, and \(\sum_{k=1}^{K}\pi_{k}=1\), \(\mu_{k}\) and \(\tau_{k}^{-1}\) represent the mean and standard deviation of the Gaussian distribution, respectively. The conjugate prior parameters \(\mu_{k},\tau_{k}\) and weights \(\mathbf{\pi}=(\pi_{1},\dots,\pi_{k})\) can be expressed as:  
\[\mu_{k},\tau_{k}\sim\mathcal{N}(\mu_{k}|\mu_{0},(\beta_{0}\tau_{k})^{-1})Gam( \tau_{k}|c_{0},d_{0}) \tag{21}\]  
\[\mathbf{\pi}\sim Dir(\mathbf{\pi}|\alpha_{0}) \tag{22}\]  
\(Gam(\tau|c_{0},d_{0})\) is the gamma distribution of parameters \(c_{0},d_{0}\), and \(Dir(\mathbf{\pi}|\alpha_{0})\) represents the Dirichlet distribution of \(\alpha_{0}=(\alpha_{01},\dots,\alpha_{0k})\) parameterization.  
A low-rank component model is established based on the automatic correlation determination method, and the matrix \(\mathbf{R}_{M\times N}\) with rank \(r\leq\min(M,N)\) is expressed as the product of \(\mathbf{U}_{M\times H}\) and \(\mathbf{V}_{N\times H}\), that is:  
\[\mathbf{R}=\mathbf{U}\mathbf{V}^{T}=\sum_{h=1}^{H}\mathbf{u}_{,h}\mathbf{v}_{,h}^{T} \tag{23}\]where \(H>r\), \(\mathbf{u}_{h}\) and \(\mathbf{v}_{h}\) are the \(h\)-th columns of \(\mathbf{U}\) and \(\mathbf{V}\), respectively. Column sparsity is applied to \(\mathbf{U}\) and \(\mathbf{V}\) to ensure the low rank of \(\mathbf{R}\):  
\[\mathbf{u}_{h}\sim\mathcal{N}(\mathbf{u}_{h}|0,\zeta_{h}^{-1}\mathbf{I}_{M}),\mathbf{v}_{h} \sim N(\mathbf{v}_{h}|0,\zeta_{h}^{-1}\mathbf{I}_{N}) \tag{24}\]  
\(\mathbf{I}_{M}\) and \(\mathbf{I}_{N}\) are the identity matrices. The conjugate prior for each accuracy variable \(\zeta_{h}\) is:  
\[\zeta_{h}\sim Gam(\zeta_{h}|a_{0},b_{0}) \tag{25}\]  
Combining Equations (19), (20) and (25), a complete Bayesian robust principal component analysis model based on MoG is obtained, and the goal is to infer the posterior of all relevant variables.
# Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network
###### Abstract  
As the existing Blade Tip Timing (BTT) vibration measurement methods have serious under-sampling problems, where the blade resonance frequency is usually higher than the sampling frequency of the data acquisition system of the BTT method, resulting in large errors in the identification of blade vibration parameters, new solutions are needed to extend the capability of BTT to nonlinear and multimodal vibration analysis. Therefore, it is the current research direction to pursue new and more accurate measurement and signal processing methods. By analyzing the waveform data from the BTT sensor and using it for vibration analysis, it significantly extends the BTT database. To avoid the current problems of under-sampling and low recognition accuracy, this paper conducts a study on the recognition of rotating blade vibration parameters based on the Radial Basis Function (RBF) model by establishing a RBF neural network prediction model to analyze the static calibration experimental data and predict the waveform of the BTT sensor, and comparing the prediction curves of various models. As the results show, for the RBF model, the prediction accuracy is closely related to the source data of the sampling point data, when the source data predicted by the RBF model is close to the center of the samples, the prediction accuracy is high, meanwhile, the prediction accuracy decreases as it is far away from the center of these data. At the same time, the number of samples is too small to affect the prediction ability of the RBF model. By using this method, more waveforms under the Blade Tip Clearance (BTC) can be predicted with the available sample point data, and the errors in the experimental measurement process can be corrected.  
blade tip timing; waveform; RBF neural network; stationary calibration +
Footnote †: journal: Applied sciences
## 2 System Model
### Wireless Localization Network  
As shown in Figure 1, there are two kinds of nodes in the network: anchors and radars. The anchors are nodes with known positions, while the positions of radars need to be estimated. Anchors and radars can obtain TOA measurements by receiving line-of-sight (LOS) signals. At the same time, considering that radars can receive and transmit signals, we have introduced spatial cooperation between radars to assist in positioning radars in those scenarios where the number of anchors is limited.
## 5 Evaluation and Testing
### Data Integration and Interoperability Tests Using Ontop SPARQL Endpoint to Evaluate Federated VKG  
The section illustrates a state-of-the-art Ontop technique for retrieving data from the FVKG, facilitating the integration of diverse data from various structures and sources, both external and internal. Figure 10 shows the Ontop plugin's graphical interface in Protege 5.6.4 for formulating user queries using the Ontop SPARQL endpoint. It translates the user SPARQL query into SQL query using SQL Translator and generates an Intermediate Query (IQ), which efficiently helps retrieve data from data sources and ontological models. The Ontop SPARQL endpoint is an interface provided by the Ontop platform that enables users to query virtual knowledge graphs leveraging the SPARQL query language. Ontop SPARQL serves as a mediating layer between users to submit SPARQL to the VKG generated by Ontop and allows users to query virtually with data migration from the integrated, distributed data sources in a unified manner. It supports data integration and querying across heterogeneous data sources, fostering interoperability and enabling advanced data-driven applications and landscapes. Figure 10: Evaluation of DI&I and mapping of disparate data sources; retrieval using Ontop. Figure 10 illustrates the evaluation procedure of FVKG and data retrieval based on customized queries written by end users in a virtual environment without data migration or movement across distributed data sources. In FVKG, the virtualized approach results in lower data latency and a unified data format with a standardized vocabulary, effectively addressing data integration and interoperability challenges. This behavior of data helps for data exchange between different data-driven applications or IS within exact or other domains. For particle validation and evaluation, we used published real-world datasets (e.g., cardiovascular disease-related data from the Kaggle data repository) to deploy a proof-of-concept system. Comparative Synthesis with Existing Approaches: Ontop and SQL for Data Retrieval with Time over Databases  
After constructing the FVKG, we tested the system's performance by executing various SPARQL queries that mapped distributed database record values and generated corresponding SQL translation values (see Table 3). We made a comparison in two steps: (i) we compared the Ontop mapping approach using the Mapping Manager plugin in Protege 5.6.4 and created the Ontop mapping language, then, the Ontop SPARQL plugin generated the SPARQL results and SPARQL translation in SQL. (ii) We tested the exact SPARQL translation generated query in MySQL Workbench 8.0 and received the same results. Table 4 highlights the mapping phenomenon of the Ontop and SQL approaches and presents an average execution time comparison of SPARQL queries and SQL queries executed using the Ontop plugin in Protege and the implementation of the virtualized knowledge graph approach to construct FVKG.
## 3 Signal Candidates  
In the past, multiple potential signals were proposed to be transmitted as a future GAL E1D signal. In the early 2010s, E1D candidates with no offset to the already transmit signal were the subject of discussions [2; 8]. In the second half of the decade, E1D signal candidates with an offset to the carrier of the E1B, E1C, and PRS signals were proposed and investigated [4; 5; 6; 11; 16]. Inspired by the aforementioned proposals, seven E1D signal candidates are investigated in the following, which are either BPSK-rectangular (R), CBOC, or BOC-modulated and have different chipping rates and carrier frequency placements within the upper L-band. The detailed parameters of the signal candidates can be found in Table 1. The spectral allocations of the main lobes and signal carrier placement of the E1D candidates, which where already suggested in earlier studies [5], are either slightly above or below the upper main lobe of the modernized authorized services provided by GAL and other GNSS to avoid inter- and intra-system interference. The signal candidates E1D-a/b/c have \(4\times 1.023\) MHz, E1D-g has \(12\times 1.023\) MHz, and E1D-d/e/f have \(20\times 1.023\) MHz center frequency offsets with respect to the L1/E1 carrier frequency. The most likely modulation for an E1D signal is BPSK-R due to its simplicity suited for reception by low complexity receivers. The CBOC and BOC-modulated candidates were included due to interesting ACF properties when assuming a coherent signal generation. The shape of the meta-signal E1C+E1D ACFs is illustrated by Figure 4b. The complex E1C+E1D meta-signal replicas for the simulations are defined as  
\[\centering\varepsilon_{n}(t)=c_{\text{E1C}}(t)e^{-j\pi f_{n}^{\Delta t}}+c_{ \text{E1D}}(t)e^{+j\pi f_{n}^{\Delta t}}.\@add@centering \tag{15}\]