esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 2 System Model
### Targeted Problems  
#### 2.2.1 Construction of Anti-Jamming Knowledge Graph  
Since there is no research on the anti-jamming knowledge graph in existing literatures, how to construct a knowledge graph is the first problem to be solved. The prior knowledge of anti-conventional jamming patterns is abundant, and due to the small scale of data in anti-jamming field, knowledge is extracted mainly by manual methods in this paper, including jamming patterns, time-frequency domain features of jamming patterns, and anti-jamming strategies. Then, the knowledge is processed manually, and the relationship between jamming patterns, features and anti-jamming strategies is summarized to form Resource Description Framework (RDF) triplet data. Finally, the relational data are stored in the graph database Neo4j to form the anti-jamming knowledge graph of conventional jamming patterns. The detailed construction process and results of the anti-jamming knowledge map can be found in [26]. Based on this knowledge graph, all relations of an entity can be searched, or whether there is a correlation between the two entities can be judged, which provides the basis for the decision-making part.
## 2 Governing Equations  
### Fractional-Order Flow Rule  
The fractional plasticity [16; 17; 18] uses a so-called fractional-order flow rule to account for the extent of non-coaxially between the plastic flow direction and the loading direction without assuming an extra plastic potential. This study adopts the Caputo derivative due to its weak singularity [19]:  
\[D^{\alpha}f(t)=\left\{\begin{array}{l}\frac{1}{\Gamma(n-\alpha)}\int_{0}^{t }(t-\tau)^{n-\alpha-1}f^{(n)}(\tau)\mathrm{d}\tau,\alpha\
eq n,\\ f^{(n)}(t),\alpha=n\end{array}\right. \tag{1}\]  
where \(\alpha\in R^{+}\) is the fractional-order; \(n\in N\) and \(n-1<\alpha\leq n\); and \(\Gamma(x)\) is the Gamma function.  
The proposed model is defined in terms of the mean effective stress \(p=tr(\mathbf{\sigma})/3\) and deviatoric stress \(q=\sqrt{3l_{s,2}}\), where \(l_{S,2}=\mathbf{S}:\mathbf{S}/2\) is the second invariant of the deviatoric stress tensor \(\mathbf{s}=\mathbf{\sigma}-p\mathbf{I}\) and \(\mathbf{\sigma}\) and \(\mathbf{I}\) are the stress and the second-order identity tensors, respectively. Based on the fractional-order flow rule, the plastic strain tensor \(de^{p}\) is calculated by:  
\[de^{p}=\Lambda\frac{\partial^{\alpha}f}{\partial\sigma^{\alpha}} \tag{2}\]  
where \(\Lambda\) is the plastic multiplier, the \(\alpha\)-order fractional derivative of the yield function \(f\) with respect to  
Similarly, the definition of the loading direction \(\mathbf{n}\) is given in the following Equation (5) but only requires a first-order derivative since only \(\mathbf{n}\) is always normal to the yield surface.  
\[\mathbf{n}=\frac{\partial f/\partial\sigma}{\parallel\partial f/\partial\sigma \parallel} \tag{5}\]  
with  
\[\frac{\partial f}{\partial\sigma}=\frac{1}{3}\Big{(}\frac{\partial f}{ \partial p}\Big{)}\mathbf{1}+\sqrt{\frac{3}{2}}\Big{(}\frac{\partial f}{ \partial q}\Big{)}\frac{\mathbf{s}}{\parallel\mathbf{s}\parallel} \tag{6}\]  
Figure 1 shows the difference between \(\mathbf{m}\) and \(\mathbf{n}\) in the principal stress space. When \(\alpha\) equals 1.0, \(\mathbf{m}\) coincides with \(\mathbf{n}\), and the strain vector is normal to the yield surface. In this case, the fractional-order flow rule is the same as the associated flow rule in the classic plastic theory. Otherwise, a non-associated flow rule is obtained. It can be found that the fractional-order flow rule is flexible to account for the non-orthogonal deformation characteristics of different kinds of geomaterials with a solid mathematical foundation.
## 4 Methodology and Interpretation of Dilatometer Test (DMT) Results  
More than 30 years ago, Dr. [PERSON] designed and constructed the first dilatometer at the L'Aquila University in Italy; this device and principles of soil investigation were presented in 1975 at the conference of the American Society of Civil Engineers (ASCE) in Raleigh [11]. Dilatometer investigations consist of measurements of gas pressure acting on the membrane of the dilatometer blade at selected depths. In soil tests, two pressure measurements (\(A\) and \(B\)) are usually carried out, which force the membrane center to move by 0.05 mm to the ground (\(A\) reading) and the diaphragm center to the ground by approximately 1.05 mm (\(B\) reading). In order to extend the dilatometer tests, pressure measurements are sometimes carried out when the membrane returns to the ground contact position (\(C\) reading). The values of readings \(A\), \(B\), and \(C\) are corrected due to the inertia of the diaphragm and marked as \(p_{o}\), \(p_{1}\), and \(p_{2}\), respectively. The value of the vertical component of effective vertical stress \(\sigma^{\prime}v_{o}\) are used to determine the following dilatometer indexes: material index \(I_{D}\), lateral stress index \(K_{D}\), and dilatometer modulus \(E_{D}\)[12; 13; 14; 15; 16; 17; 18]. The seismic dilatometer (SDMT) is a combination of a standard dilatometer presented by [PERSON] (1980) and a module measuring the velocity of the seismic propagation V\({}_{\rm S}\). Supplementing the equipment used to perform dilatometer tests with two geophones in the SDMT seismic dilatometer extended the possibilities of interpretation of dilatometer tests. It was first introduced by [PERSON] and then refined at Georgia Institute of Technology in Atlanta, USA. A new system with a seismic dilatometer has been recently developed in Italy [19; 20; 21; 22; 23; 24].  
Material index \(I_{D}\):  
\[I_{D}=\mathrm{f}(A,\ B,\ u_{0})=\frac{p_{1}-p_{0}}{p_{0}-u_{0}} \tag{4}\]  
Horizontal stress index \(K_{D}\):  
\[K_{D}=\mathrm{f}(A,\ u_{0},\ \sigma^{\prime}_{vo},\ B)=\frac{p_{0}-u_{0}}{ \sigma^{\prime}_{vo}} \tag{5}\]  
Dilatometer modulus \(E_{D}\):  
\[E_{D}=\mathrm{f}(A,\ B)=34.7\times(p_{1}-p_{0}) \tag{6}\]Pore pressure index \(U_{D}\):  
\[U_{D}=\text{f}(A,\ C,\ u_{0},\ B)=\frac{p_{2}-u_{0}}{p_{0}-u_{0}} \tag{7}\]
## 4 Detection of Artificial Delaminations
### Band Power  
The band power is calculated over the total excitation bandwidth according to Equation (6) and is shown in Figure 6. The band power of the linear component \(BP^{lin}\) reveals only the shallow delamination (see Figure 6a). There is no sign of the backside-delamination because this area does not show an increase in the linear velocity component. This observation is in agreement with the \(DBR\) curves in Figure 5a,e: the shallow delamination has \(DBR^{lin}(f)>1\) for the majority of the frequency bins, while the deep backside-delamination has \(DBR^{lin}(f)\approx 1\) for most frequency bins.  
Figure 6: Band power maps calculated using Equation (6) for (**a**) linear velocity component, (**b**) second harmonic component and (**c**) third harmonic component.  
The band power map of the second harmonic component (\(BP^{HH2}\)) reveals the presence of both delaminations (see Figure 6b). As such, this band power map can be used as a robust indicator for detection of both shallow and deep defects. The third harmonic band power map reveals the shallow delamination with very high contrast (see Figure 6c). However, for the deep defect, the third harmonic components are not efficiently transferred through the thickness of the component. This makes the backside-delamination hardly distinguishable from the damage-free material in the \(BP^{HH3}\) map. The exact origin of this absent \(HH3\) is currently being investigated.
## 5 Conclusions  
In conclusion, the new analog-digital combined high-secure physical layer encryption optical communication system was proposed to protect data transmission. The chaotic signal generated by the chaotic circuit can encrypt the data as the driving signal. Compared with the traditional chaotic light source, this scheme avoids the nonlinear conversion of photoelectric signal. In the process of combining the digital domain and the analog domain, it can effectively double encrypt data and has a promising secure transmission scheme for future applications.  
Conceptualization, Q.Z. and B.L.; methodology, Q.Z., J.R. and Z.G.; formal analysis, Q.Z., J.R., Y.M. and Y.J.; data curation, Q.Z. and Y.J.; writing--original draft preparation, Q.Z. and Z.G.; writing--review and editing, Q.Z., R.U., X.W., Y.W., L.Z. and T.S.; funding acquisition, B.L., Y.W., L.Z. and T.S. All authors have read and agreed to the published version of the manuscript.  
National Key Research and Development Program of China (No. 2018 YFB1801703); National Natural Science Foundation of China (No. 61875248, 61835005, 62171227, 61727817, U2001601, 62035018, 61935005, 61935011, 61720106015, and 61975084); Jiangsu team of innovation and entrepreneurship; The Startup Foundation for Introducing Talent of NUIST.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 Mechanism of SPPTMD and Numerical Model of Structure-SPPTMD System
### Mechanism of the SPPTMD  
Even though the SP amplifies the vibration absorbing ability by introducing the internal phenomenon, its energy dissipation is still limited. Therefore, in this paper, the impact damping is introduced to the damper to develop a novel damping device, namely the SPPTMD. As shown in Figure 2 [51], the SPPTMD is composed of a connection plate, a spring, a mass block, and an annular limiter embedded with viscoelastic material. The damper is connected to the controlled structure via the connection plate. The spring forms a spring pendulum system by attaching one end to the connection plate and the other end to the mass block. The SPPTMD is essentially an SP with an additional limiter, and it is a nonlinear vibration absorber that relies on the SP and the bounding of the additional mass with the limiter to dissipate energy. When the structure vibrates with a small amplitude,  
Figure 1: Vibration modes of the SP: (**a**) radial mode; (**b**) swing mode.  
there is no bounding of the additional mass with the limiter, and the SPPTMD functions as an SP that suppresses structural oscillations through both the inertial force generated by the viscoelastic damper and the internal resonance mechanism of the spring system; when the structure vibrates with a relatively large amplitude, the bounding of the additional mass with the limiter occurs, and the SPPTMD dissipates the kinetic energy of the system absorbed by the damper through the bounding.
# Characteristic Analysis of Compact Spectrometer Based on Off-Axis Meta-Lens
###### Abstract  
Ultra-compact spectrometers with high-resolution and/or broadband features have long been pursued for their wide application prospects. The off-axis meta-lens, a new species of planar optical instruments, provides a unique and feasible way to realize these goals. Here we give a detailed investigation of the influences of structural parameters of meta-lens-based spectrometers on the effective spectral range and the spectral resolution using both wave optics and geometrical optics methods. Aimed for different usages, two types of meta-lens based spectrometers are numerically proposed: one is a wideband spectrometer working at 800-1800 nm wavelengths with the spectral resolution of 2-5 nm and the other is a narrowband one working at the 780-920 nm band but with a much higher spectral resolution of 0.15-0.6 nm. The tolerance for fabrication errors is also discussed in the end. These provides a prominent way to design and integrate planar film-based spectrometers for various instrumental applications.  
meta-lens; metasurface; spectrometer; dispersion; off-axis optical system +
Footnote †: journal: Applied
# Effect of Subsequent Subgrade on Seismic Response of the High-Speed Railway Track-Bridge System
## 5 Seismic Response Analysis of Each Critical Component in HSTRBS
### Seismic Response Analysis of Main Beam
The results show that under the combined effect of longitudinal direction and vertical ground vibration, the longitudinal confinement effect of SSTS leads to a reduction in the longitudinal displacement produced by the main beam, while there is no significant change in the vertical displacement. This phenomenon is attributed to the SSTS's continuity along the bridge's longitudinal direction, which imparts additional longitudinal stiffness to the HERTBS boundary, with minimal influence on its vertical stiffness. Notably, as the PGA incrementally increases, the SSTS's influence on the main beam's longitudinal confinement correspondingly intensifies. This is because the seismic response of the bridge structure is almost linearly proportional to PGA variations. In contrast, the seismic response of the flexible subgrade escalates more gradually and nonlinearly when PGA increases. When the value of PGA is taken as 0.4 g, the longitudinal confinement effect of SSTS leads to a reduction of 5.1 mm in the longitudinal displacement and an increase of 1.7 mm in the vertical displacement produced by the main beam.
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 3 The Proposed Strategy
### STUKF- and MB-based Centralized Fault Diagnosis
#### 3.1.1 \(Stukf\)-based State Prediction  
First, we introduce fault state related time-varying parameter \(\theta(k)\). Then, joint estimation requires (1) to be rewritten as:  
\[\left\{\begin{array}{l}x(k+1)=f(x(k))+\Gamma(k)v(k)\\ \theta(k+1)=\theta(k)\\ z(k+1)=h(x(k+1))+\theta(k+1)+e(k+1)\end{array}\right.. \tag{2}\]  
By applying joint estimation of states and parameters, faulty states can be detected. Then, we extent state, noise, and transition as follows:  
\[x_{e}(k+1)=\begin{bmatrix}x(k+1)\\ \theta(k+1)\end{bmatrix}, \tag{3}\]  
\[\Gamma_{e}(k)=\begin{bmatrix}\Gamma(k)\\ 0\end{bmatrix}, \tag{4}\]  
\[f_{e}(x_{e}(k))=\begin{bmatrix}f(x(k))\\ \theta(k)\end{bmatrix}, \tag{5}\]  
\[h_{e}(x_{e}(k+1))=h(x_{e}(k+1))+\theta(k+1). \tag{6}\]  
Substituting Equations (3)-(6) into Equation (2) yields:  
\[\left\{\begin{array}{l}x_{e}(k+1)=f_{e}(x_{e}(k))+\Gamma_{e}(k)v(k)\\ \theta(k+1)=\theta(k)\\ z(k+1)=h_{e}(x_{e}(k+1))+\theta(k+1)+e(k+1)\end{array}\right.. \tag{7}\]  
It is worth mentioning that \(x_{e}(k+1)\) can be predicted by \(STUKF\)[39].
# Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
## 3 Result and Analysis
### Illustrative Examples
#### 3.2.4 Challenge 3: Customized Collection Interface
Figure 6a shows spatial data collected by personnel, indicating that our collection software supports geometric elements like points, lines, and polygons. After drawing these elements, personnel must enter the corresponding attribute information in the attribute collection interface. Figure 8b-d display this interface dynamically based on user selections. Figure 8b,c show how different collection items appear when different crops are selected, thanks to visibility scripts. Figure 8b,d illustrate the differences between the crop cultivation and livestock production collection interfaces. Importantly, collection personnel can record nested data in the attribute collection interface. Figure 8e shows the process of collecting \"Operating Entity\" and \"Contact Information\". The figure demonstrates that collection personnel can add multiple contacts but only one operating entity, proving that our platform supports nested data collection. Figure 7: XML Configuration file. This task also utilizes value scripts to minimize user errors. The platform allows users to define numerical relationships between different attributes, reducing workload and calculation errors. For instance, during crop cultivation data collection, entering the area and yield will automatically calculate the yield per unit area. Figure 8e showcases the functionality of these value scripts in a real-world scenario.
## 2 Materials and Methods
### Characterization  
#### 2.2.1 Differential Scanning Calorimetry (DSC)  
The analysis of melting and crystallization temperatures was conducted via DSC 823\({}^{\mathrm{e}}\) by METTLER TOLEDO (Columbus, OH, USA) between \(-\)25 and 100 \({}^{\circ}\)C at both 2 and 10 \({}^{\circ}\)C/min heating rates. The DSC was calibrated with indium standard before the measurements, and the uncertainty of the instrument was \(\pm\)0.1 K. The samples were measured under a constant flow of nitrogen at a rate of 100 mL/min, and sample masses were typically between 5 and 15 mg. In order to test the stability of the samples, the heating/cooling cycles were repeated three times for each heating rate with the same method for every sample, for a total of six cycles per sample. Moreover, as stated previously, all syntheses were performed three times to ensure reproducibility. Therefore, each ester type was measured three times with six cycles per sample. The values reported were calculated as follows: For each replicate of the same ester the average for all six cycles at 10 and 2 \({}^{\circ}\)C/min for T\({}_{\mathrm{m}}\), T\({}_{\mathrm{c}}\) and \(\Delta\)H was derived. Afterwards, the average of the three final values obtained for each repetition was retrieved and is reported in the next sections alongside the relative standard deviation. Therefore, all uncertainty values reported in the next sections are based on replicate measurements, thus denoting the accuracy of the results. The melting and crystallization peaks were calculated by the STAR\({}^{\mathrm{e}}\) software through the tangent method, and the enthalpies were obtained by integration of the corresponding peak. The degree of supercooling was estimated considering the difference between the onset melting peak and the onset crystallization peak reported from the instrument.
## 3 Adaptive Background Calibration Method  
Based on the model presented in Figure 3, an adaptive blind calibration method for nonlinear mismatch error in \(M\)-TIADCs is proposed in this section. Our method can track the small variation of the nonlinear parameters, and no prior information about the input signal is required. The only requirement is a slight oversampling, which is anyhow commonly adopted in most practical TIADC systems. Compared with the approach proposed in [25], our method consumes much less hardware resources by replacing the sinusoidal modulation sequences with the binary Hadamard sequences.  
The basic principle of our calibration method is to reconstruct the error signal \(e[n]\) exploiting the estimated nonlinear parameters \(\hat{c}_{k,l}[n]\) and then subtract it from the \(M\)-TIADC output \(y[n]\). The nonlinearities' induced error signal in the input-free band stemming from oversampling is filtered out to estimate the nonlinear parameters utilizing the filtered-X least mean square (FxLMS) algorithm.
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 3 Methods
### Fondue-Nda  
From the above section, it is clear that the NDA problem can be decomposed into two subproblems:  
1. Estimating the multiplicities of all \(i\in\hat{\mathcal{G}}\)--i.e., the number of unambiguous nodes from \(\mathcal{G}\) represented by the node from \(\mathcal{G}\). This essentially amounts to estimation the contraction \(c\). Note that the number of nodes \(n\) in \(V\) is then equal to the sum of these multiplicities, and arbitrarily assigning these \(n\) nodes to the sets \(c^{-1}(i)\) defines \(c^{-1}\) and, thus, \(c\);
2. Given \(c\), estimating the edge set \(E\). To ensure that \(c(\mathcal{G})=\hat{\mathcal{G}}\), for each \(\{i,j\}\in\hat{E}\) there must exist at least one edge \(\{k,l\}\in E\) with \(k\in c^{-1}(i)\) and \(l\in c^{-1}(j)\). However, this leaves the problem underdetermined (making this problem ill-posed), as there may also exist multiple such edges.  
As an inductive bias for the second step, we will additionally assume that the graph \(\mathcal{G}\) is sparse. Thus, FONDUE-NDA estimates \(\mathcal{G}\) as the graph with the smallest set \(E\) for which \(c(\mathcal{G})=\hat{\mathcal{G}}\). Practically, this means that an edge \(\{i,j\}\in\hat{E}\) results in exactly one edge \(\{k,l\}\in E\) with \(k\in c^{-1}(i)\) and \(l\in c^{-1}(j)\), and that equivalent nodes \(k\sim_{c}l\) with \(k,l\in V\) are never connected by an edge, i.e., \(\{k,l\}\
ot\in E\). This bias is justified by the sparsity of most 'natural' graphs, and our experiments indicate it is justified.  
We approach the NE-based NDA Problem 6 in a greedy and iterative manner. In each iteration, FONDUE-NDA identifies the node that has a split which will result in the smallest value of the cost function among all nodes. To further reduce the computational complexity, FONDUE-NDA only splits one node into two nodes at a time (e.g., Figure 1b), i.e., it splits node \(i\) into two nodes \(i^{\prime}\) and \(i^{\prime\prime}\) with corresponding adjacency vectors \(\mathbf{a}_{i^{\prime}},\mathbf{a}_{i^{\prime\prime}}\in\{0,1\}^{n}\), \(\mathbf{a}_{i^{\prime}}+\mathbf{a}_{i^{\prime\prime}}=\mathbf{a}_{i}\). We refer to such a split as a _binary split_. Note that repeated binary splits can of course be used to achieve the same result as a single split into several notes, so this assumption does not imply a loss of generality or applicability. Once the best binary split of the best node is identified, FONDUE-NDA splits that node and starts the next iteration. The evaluation of each split requires recomputing the embedding, and comparing the resulting optimal NE cost functions with each other.  
Unfortunately, this naive strategy is computationally intractable: computing a single NE is already computationally demanding for most (if not all) NE methods. Thus, having to compute a re-embedding for all possible splits, even binary ones (there are \(O(n2^{d})\) of them, with \(n\) the number of nodes and \(d\) the maximal degree), is entirely infeasible for practical networks.