esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Fano Resonances in Dynamic Resonant Tunneling Processes
###### Abstract  
The existence of Fano resonances in dynamic resonant tunneling (RT) systems has been investigated. Fano resonances are characterized by the appearance of a 100% reflection coefficient in proximity to a high transmission coefficient. For a Fano resonance to appear, a bound state must exist. On the other hand, a resonant tunneling process is characterized by a high transmission and the existence of a quasi-bound state (QBS) instead of a bound one. It has been shown that, by narrowing the width of the barrier, the resonance energy of the QBS gradually decreases and eventually turns into a bound state. Consequently, in a dynamic RT process, there are two scenarios: either a bound state exists, in which case, Fano resonances exist for any barrier width, or a QBS exists, and the barrier should be narrow enough for the Fano resonance to appear. In both cases, the incoming particle's frequency must be lower than the oscillating well's frequency. In this work, these resonances are investigated in detail, and both exactly numerically and approximated analytical expressions are derived for both the weak and strong oscillating amplitude regimes. One of the conclusions is that, when the oscillating frequency is low enough, multiple Fano resonances can appear by varying the barrier's width. Since these resonances are very sharp and zero transmission can easily be detected, this property can be used as a very accurate method for measuring the barrier's width, even when the particle's de-Broglie wavelength is much larger than the barrier's width.  
resonant tunneling; quantum dynamics; dynamic resonant tunneling; suppression of tunneling; coherent destruction of tunneling; total reflection; Fano resonance; quasi-bound state +
Footnote †: journal: Applied sciences
## 4 Results and Discussion  
In this section, the proposed system components' performance is analyzed, validated, and estimated. The simulation of various configurations is also analyzed. Figure 6 shows the specific approach for modeling and analyzing the system performance. To enrich the understanding of the proposed system, the parametric analysis of the system components is considered in conjunction with the important design parameters. Several performance analyses have been discussed to support the design and performance of the proposed model based on the existing designs. The performance analysis of the solar hybrid cooling system includes the energy utilized under various design conditions. The discussion also includes the performance of various solar hybrid cooling configuration models possible to enhance the performance of the solar hybrid cooling technology. Finally, with the obtained performance analysis, the effect of the proposed models on the environment is discussed.
# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
## 2 Related Work
### Anomaly Detection Process Based on the iForest-AE Method  
The anomaly detection process based on the AE-iForest is shown in Figure 1, which is separated into two phases: model training and model detection.  
The data collected by the metallurgical information collection system are divided into two parts: training data and test data. In order to speed up the convergence of the model, the two parts of data are normalized by the following equation [27].  
\[x^{\prime}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{6}\]  
\(x^{\prime}\) is the normalized data, \(x\) is the input data, \(x_{max}\) and \(x_{min}\) are the maximum and minimum values of the input data.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 5 Performance Evaluation
### Evaluation Environment and Settings  
To evaluate our proposed algorithm, we perform simulations in the following evaluation environment: Intel (R) core (TM) i7-10875H CPU, 32.0 GB RAM. The open-source tool GLPK [15] is used to solve integer linear programming.  
We implement a discrete event simulator to simulate the arrival and departure of virtual networks. In the substrate network topology, the number of nodes is set to 40, and each pair of substrate nodes is randomly connected with a probability 0.5, which corresponds to this medium-scaled network. The setting is consistent with [14]. Nodes are randomly distributed within a grid of (20 \(\times\) 20). The computing resources and storage resources of the substrate nodes are uniformly distributed between 50 and 100 units, the bandwidth resources of the substrate links are uniformly distributed between 50 and 100 units, and the propagation delay of the substrate links is set to be uniformly distributed between 1 and 3 units.  
The arrival of virtual network requests follows Poisson distribution. In the experiment, the results of the arrival rate from 2 to 6 virtual network requests per 100 time units are evaluated. The virtual network survival time follows the exponential distribution, the average value is set to 1000 time units. The number of virtual nodes is uniformly distributed between 2 and 8, and the probability of connectivity between virtual nodes is 0.5. The requirements of computing resources and storage resources of virtual nodes are uniformly distributed between 1 and 20, and the bandwidth requirements of virtual links are uniformly distributed from 1 to 50. The propagation delay of virtual links for delay-sensitive requests is limited to 2 to 8 uniformly distributed. The value of D is set to 5. The ratio of ordinary virtual network requests to delay-sensitive virtual network requestsis 1:1. The weight factor \(\alpha,\beta,\lambda\) is set to 1. In the experiment, the simulation time is set to 10,000 time units.  
In our experiments, in order to avoid extreme situations such as a very high VNR acceptance ratio or very low VNR acceptance ratio, we conducted 10 experiments for each virtual network request arrival ratio, and each experiment generated a new set of VNRS and a new substrate network. We recorded the arithmetic average of the results of 10 operations as the final result.  
In order to evaluate the algorithm performance, we compared the proposed algorithm with [14, 18, 21, 22] in the simulation experiment. We marked them as CAN-ILP, GR-SP, RW-SP, and RCR-VNE, respectively, and kept the parameters consistent. The objective functions of two types of virtual network requests in [14] are CAN-CF and CAN-CLDF, respectively.
## 2 Principles
### Synchrosqueezing Transform (SST)  
SST is a new reassignment technique and a powerful tool for precisely decomposing and analyzing a signal. It can provide a sharpened time-frequency representation by applying a post-processing reallocation to the original representation. Thus, the SST algorithm can be divided into two step: (1) computing the instantaneous frequency using the original time-frequency coefficients and (2) squeezing and reassigning step. So far, many researchers have developed SST algorithms combining a reassignment step and different time-frequency decomposing methods such as continuous wavelet transform, short-time Fourier transform, curvelet tranform, S-transform, and wavelet packet transform.  
Among these algorithms, synchrosqueezing continuous wavelet transform (SSCWT) is used for comparison with the new method proposed in this paper. It was proposed by [PERSON] et al. [24] and can obtain higher resolution time-frequency spectra by squeezing and reconstructing the values of continuous wavelet transform (CWT) in the frequency direction. Thus, taking this method as an example, we briefly introduce the principle of SST.  
First, we calculate the CWT of a signal \(x(t)\) as,  
\[WT_{x}(a,b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}x(t)\overline{\psi\left( \frac{t-b}{a}\right)}dt, \tag{7}\]where \(WT_{x}\) denotes the continuous wavelet transform of a signal \(x(t)\). \(a\) is the scale factor, and \(b\) is the time translation factor. \(\psi(t)\) is a mother wavelet and \(\overline{\psi}\) is the complex conjugate of \(\psi(t)\).  
Then, the instantaneous frequency at any time-scale location \((a,b)\) is,  
\[\omega(a,b)=\left\{\begin{array}{cc}\left[iWT_{x}(a,b)\right]^{-1}\frac{2 WT_ {x}(a,b)}{\partial b}&WT_{x}(a,b)\
eq 0\\ \infty&WT_{x}(a,b)=0\end{array}\right. \tag{8}\]  
Secondly, we reassign values of the time-frequency representation based on its local oscillation. The result of SST is,  
\[SST_{x}(\omega_{l},b)=\frac{1}{\Delta\omega}\sum_{a_{i}:|\omega(a_{i},b)- \omega_{l}|\leq\frac{\omega_{l}}{2}}WT_{x}(a_{i},b)a_{i}^{-\frac{3}{2}}\Delta a _{i}, \tag{9}\]  
where \(a_{i}\) is the \(i-th\) scale value and \(\Delta a_{i}=a_{i}-a_{i-1}\). \(\omega_{l}\) denotes the \(l-th\) frequency point. The Equation (9) means that the CWT spectra values between \(\left[\omega_{l}-\frac{1}{2}\Delta\omega,\omega_{l}+\frac{1}{2}\Delta\omega\right]\) are squeezed to the \(\omega_{l}\) frequency point along the frequency direction. Thus, the time-frequency resolution becomes better.  
The inverse transform of SST is written as  
\[x(b)=\text{Re}\Bigg{[}C_{\psi}^{-1}\sum_{l}SST_{x}(\omega_{l},b)\Delta\omega \Bigg{]} \tag{10}\]  
In Equation (10), \(C_{\psi}=\frac{1}{2}\int_{0}^{\infty}\overline{\hat{\psi}(\xi)}\xi^{-1}d\xi\), \(\overline{\hat{\psi}(\xi)}\) is the complex conjugate of the Fourier transform of the mother wavelet \(\psi(t)\).
# NURBS-Enhanced Meshfree Method with an Integration Subtraction Technique for Complex Topology
## 4 Numerical Examples
### Infinite Plate with a Circular Hole
It has been shown by [PERSON] and [PERSON] [40] that a purely MQ-RBF cannot always ensure to exactly reproduce a linear field function. This could be one of the major reasons for the poor h-convergence in using MQ-RBF for field variable interpolations. \[\sigma_{yy}(x,y)=-\frac{a^{2}}{r^{2}}\Big{(}\frac{1}{2}{\rm cos}2\theta-{\rm cos }4\theta\Big{)}-\frac{3a^{4}}{2r^{4}}{\rm cos}4\theta \tag{31}\]2 Discretization of FEM and radial point interpolation method (RPIM) with node interval \(h=0.2\). \[\sigma_{yy}(x,y)=-\frac{a^{2}}{r^{2}}\Big{(}\frac{1}{2}{\rm cos}2\theta-{\rm cos }4\theta\Big{)}-\frac{3a^{4}}{2r^{4}}{\rm cos}4\theta \tag{31}\]3 Convergence in the problem of the plate with a hole: (**a**) displacement; (**b**) energy. As a result of the shortcomings of the MQ-RBF, its energy efficiency is still not satisfactory. However, with regard to displacement, the meshfree methods, RPIM and RSUB, achieve better efficiency than FEM, as shown in \[\sigma_{yy}(x,y)=-\frac{a^{2}}{r^{2}}\Big{(}\frac{1}{2}{\rm cos}2\theta-{\rm cos }4\theta\Big{)}-\frac{3a^{4}}{2r^{4}}{\rm cos}4\theta \tag{31}\]4 While RSUB seems to be a little more time consuming than RPIM, the duplicate integration in the hole region for subtraction yields greater accuracy. Thus, RSUB is actually more efficient than RPIM. Figure 14 shows the \(\sigma_{xx}\) distribution of this example, recovered in the same method that was illustrated in the mesh with node interval \(h=0.2\). Obvious oscillations can be observed in the results of FEM and RPIM. In contrast, the proposed RSUB method results in a smooth stress field and no spurious oscillations present. This is compatible with the previous super accuracy property of RSUB. \[\sigma_{yy}(x,y)=-\frac{a^{2}}{r^{2}}\Big{(}\frac{1}{2}{\rm cos}2\theta-{\rm cos }4\theta\Big{)}-\frac{3a^{4}}{2r^{4}}{\rm cos}4\theta \tag{31}\]5 Stress fields in the problem of the plate with a hole obtained by (**a**) FEM; (**b**) RPIM; (**c**) RSUB; (**d**) exact solution. \[\sigma_{yy}(x,y)=-\frac{a^{2}}{r^{2}}\Big{(}\frac{1}{2}{\rm cos}2\theta-{\rm cos }4\theta\Big{)}-\frac{3a^{4}}{2r^{4}}{\rm cos}4\theta \tag{31}\]6 Computational efficiency in the problem of the plate with a hole: (**a**) displacement; (**b**) energy.
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 2 Materials And Methods
### Fishnet Creation  
We generate fishnets equally dividing the urban parts of the city using QGIS ([[https://www.qgis.org/](https://www.qgis.org/)]([https://www.qgis.org/](https://www.qgis.org/)), accessed on 30 May 2021), an open-source geographic information analysis tool. Each fishnet eye has dimensions of 500 m by 500 m and an area of around 0.2655 km2. POI data gathered from different sources, such as Google Maps API offering non-residential locations and from a real estate listing website showcasing both residential and workplaces, are mapped to each fishnet.
## 1 Introduction  
As the situations of climate change and energy crises become more severe, renewable energies will gradually replace fossil energies, leading to fundamental changes in the structure of the energy system. The popularity of the Internet and the rapid development of information technology has brought new opportunities for the revolution of the traditional energy system, thus enabling multiple energy sources to develop coordinately. Therefore, the concept of energy Internet (EI) came into being.  
EI has just been proposed, and there is no unified understanding or definition of its structure and composition. How to build an EI system is still in dispute [1; 2; 3; 4]. In general, the main features of EI are: through mutual conversion and complementation of energies such as electricity, heat and mechanical energy, the interconnection of the power grid, heating network, gas network, oil network, water network etc. are integrated together, which can realize information sharing of various energy systems [5; 6].  
In the background of EI, energy storage (ES) will become an important basic support and key technology for EI, and it plays a key role in the transferring, matching and optimization of energies [7,8]. EI consists of all kinds of energy networks, in which energy subnets can provide energy support for each other, which leads to great requirement for capacity and types of energy storages (ESs), so that the redundancy of ESs will occur in the entire EI system. The coordination of various ESs helps to reduce the total demand for stored energy and improve economic efficiency (EE). Therefore, according to the overall demand of EI, it is necessary to coordinate and cooperate with diverse types of ESs with the aim of EE and energy utilization efficiency (EUE).  
ES plays a significant role in EI, which attracts research interest [9]. Previous literature has mostly concentrated on the application of ES in EI and the coordination of ESs or ES with renewable energies. A table to classify the literature has been created, as shown in Table 11 in the Appendix A.
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 4 Result  
### Experimental Setup  
Figure 8 depicts the field experiment that was conducted at Sinjeongnegeori Station in Korea, on Subway Line 2 for the verification of the HMIPS. A total of 60 markers were installed on the first basement floor, 20 markers on the second basement floor, and 18 markers on the third basement floor. These markers were placed on the floor in the form of square stickers with dimensions of 20 \(\times\) 20 cm. The distance between the consecutive markers was set to 10 m. A total of 540 road guide scenarios were set up with 17 intersection points, 17 attribute change points, and 17 final destinations. In order to get on and off the train and connect with other transportation options, the platform and each exit number were set as the destination. The route network was configured such that it provided a guide from the marker to the final destination. When selecting the final destination, it was set to provide indoor navigation using elevators or escalators. It avoided the stairs on the assumption that users would choose elevator- or escalator-connected exits and platforms.
## 5 Simulation Results and Analysis
### Comparison of the DRFTM Performance in Different Attack Modes
#### 5.3.2 Error Rate under Hybrid Attack  
To further assess the performance of the DRFTM trust model, we compare the false detection rates of the four trust models under various parameters, maintaining the same experimental setup as detailed in Section 5.3.1. As illustrated in Figure 8a, the DRFTM consistently maintains an error rate below 0.1 after stabilization. In contrast, the ARTMM and STMS exhibit initially high error rates that gradually decrease over time. Figure 8b demonstrates that a lower node count corresponds to higher error rates. Reduced node numbers result in limited trust evidence, which can negatively impact the model's trust determination. With an increasing node count, the DRFTM's error rate steadily decreases, establishing a stable pattern of low error rates. Regarding different ratios of malicious nodes, Figure 8c reveals that when the ratio remains below 15%, all four trust models maintain an error rate below 0.1. However, as the proportion of malicious nodes increases, the ARTMM and STMS experience a rapid escalation in error rates. This is attributed to the growing ratio of malicious nodes introducing false information into the trust evidence. The ARTMM, employing a weighted fusion method for trust calculation, exhibits limited ability to discern false trust, resulting in high error rates. In contrast, the STMS employs an unsupervised method for label assignment in the first stage, and errors in this initial phase have cascading negative effects on the subsequent anomaly identification process in the second stage. The DRFTM and TUMRL perform better with a slower increasing trend in the error rate, which stabilizes at an error rate of less than 0.15.
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 4 Results and Discussion
### Steady-State Operation  
For the steady-state performance evaluation, all FCS-MPC algorithms were discussed in this work have been observed during the steady-state operation. Apparently and during the tests stage, all algorithms showed very good performance in terms of controller stability. However, N-CMPC requires more computation time, which introduces some difficulties in the implementation. On the other hand, the power qualites of N-IMPC, SP-IMPC, and P-IMPC have a significant improvement compared to N-CMPC, SP-CMPC, and P-CMPC. The P-IMPC has around 5% reduction in the active power fluctuation comparing to the P-CMPC, which is a significant outcome compared to the N-IMPC and SP-IMPC. Bearing in mind that the steady-state performance of all algorithms can be observed also in Figures 11, 12, 17 and 20.  
Figure 19: Dynamic response of active power using frequency control methodologies with improved CF (IMPC). (**a**) Notch filter control using the improved CF (N-IMPC), where the power deviation is less than the conventional CF (N-CMPC) with 1.47 % average; (**b**) periodic control using the improved CF (P-IMPC), where the power deviation is less than the conventional (P-CMPC) with 5.36 % average; (**c**) simple penalization control using the improved CF (SP-IMPC), where the power deviation is less than the conventional CF (SP-CMPC) with 1.39 % average; and (**d**) improved (IMPC).
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 3 Ergodic Capacities Analysis  
This section provides the derivation of both DF and AF protocols. The capacity expressions will reveal the effects of hardware impairments on EH-TWR. The channel gain are set to be \(h_{i}=g_{i}/d_{i}^{m}\), where the magnitude of \(g_{i}\) is modeled as independent but non-identically distribute Rayleigh random variables with variance \(\lambda_{i}\), \(d_{i}\) is the distance between \(S_{i}\) and \(R\), and \(m\) is pass loss exponent.  
Two lemmas are presented to help the following derivations of ergodic capacities.  
**Lemma 1**.: _Since \(g_{i}\) obeys Rayleigh distribute, \(\gamma_{i}=\frac{h_{i}P_{i}}{\sigma^{2}}=\frac{g_{i}V_{i}}{d_{i}^{m}}\) also obeys [PERSON] distribute as well. The probability distribution function (PDF) of \(\gamma_{i}\) is presented as_  
\[f_{\gamma_{i}}(\gamma)=A_{i}e^{-A_{i}\gamma},\text{ for }\gamma>0 \tag{19}\]_where \(A_{i}=\frac{d_{i}^{m}}{\lambda_{i}V_{i}}\), \(V_{i}=\frac{P_{i}}{\sigma^{2}}\)._  
Proof.: The proof is straightforward, which is omitted here.  
Lemma 2: _Let \(\mathrm{Y}\) be an arbitrary variable. Then_  
\[\mathbb{C}=\mathbb{E}\{C(\mathrm{Y})\}=\frac{1}{2\ln 2}\int_{0}^{+\infty}\frac{1-F _{\mathrm{Y}}(t)}{1+t}dt \tag{20}\]  
_where \(C(\mathrm{Y})=\frac{1}{2}\log_{2}(1+\mathrm{Y})\), \(F_{\mathrm{Y}}(\cdot)\) is the cumulative distribution function (CDF) of \(\mathrm{Y}\)._  
Proof.: With \(C(\mathrm{Y})=\frac{1}{2}\log_{2}(1+\mathrm{Y})\), \(\mathbb{E}\{C(\mathrm{Y})\}\) can be obtained by \(\mathbb{E}\{C(\mathrm{Y})\}=\frac{1}{2}\int_{0}^{\infty}\log_{2}(1+t)f_{ \mathrm{Y}}(t)dt\), Then utilizing partial integration, which leads to (20) by using the integration by parts.
## 3 Model Formulation
### Proposed Framework  
After establishing the necessary tools and techniques, the steps for implementing the predictive maintenance policy are outlined in Figure 1. The processes are categorized into four groups as follows:  
1. _Preliminary selection:_ During this initial phase, data are loaded, and a preliminary selection of all covariates is conducted for use in the PHM-ML model. Furthermore, the quantity of data allocated for training and testing datasets is also determined.
2. _Covariate weight estimation and covariate selection process:_ In this second phase, the main objective is to estimate the covariate weights and determine their significance in both preliminary and final covariate selection. The process begins with the preliminary selection of all covariates, which entails determining scaling types and calculating  
Figure 1: The steps of the proposed framework.  
initial Weibull parameters. Subsequently, the seed value is obtained, methods for limiting covariate values are defined, and either GA or IPOPT is selected to optimize the log-likelihood function in Equation (3). This step yields the parameters and weights of all covariates. Then, K-fold cross-validation is employed to evaluate weight consistency based on the coefficient of variation. Following this, final model covariates are selected via correlation analysis. The process is then iterated for covariate estimation using the previously selected parameters, resulting in the final Weibull parameters and covariate weights.
3. _Covariate band estimation:_ In this phase, GMM is applied to derive the ranges defining each state, following the procedure outlined in Section 3.5.2.
4. _Obtaining metrics for the maintenance policy:_ In this last section, the conditional reliability, RUL, MTBI, MTBF, and optimal costs are calculated by leveraging asset-specific conditional data to estimate the optimal risk level using the developed PHM-ML model, thereby deriving the warning-limit function. This function facilitates predictive intervention for assets, aiming to prevent immediate failure while balancing both risk and cost factors. Consequently, real-time data are overlaid into this function to establish the intervention decision rule; if an asset's data point surpasses the warning-limit of the worst conditional state, intervention must be undertaken to avoid failure.