esa-sceva/satcom-chunk-collection huggingface dataset:
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
## 3 Materials and Methods  
### Time-Varying Filters  
The concept of time-varying coefficients has primarily been explored in the context of adaptive filtering techniques, where the filter's coefficients evolve over time to address specific problems or align with the chosen method. In this work, we propose using a predefined control rule to achieve the fastest possible response from the equalized filter while preserving its long-term frequency characteristics. Unlike adaptive filtering, our approach involves calculating the control rule once during the design phase, allowing it to be applied consistently whenever a relevant excitation occurs. The difference equation of a digital linear time-variant system (LTV) can be written as follows:  
\[y\;[n]=\sum_{k=1}^{M}a_{k}[n]\cdot y[n-k]+\sum_{k=0}^{N}b_{k}\cdot x[n-k], \tag{1}\]  
where\(M\)--The number of previous outputs (\(y[n-k]\)) included in the equation.  
\(N\)--The number of current and previous inputs (\(x[n-k]\)) considered.  
\(a_{k}[n]\)--Time-varying coefficients for the feedback terms (previous outputs).  
\(b_{k}[n]\)--Time-varying coefficients for the feedforward terms (inputs).  
\(y[n]\)--Current output at time step \(n\).  
\(x[n]\)--Current input at time step \(n\).
## 4 Measurement of the 8-Channel T/R Module with 7-Bit TTD
### Output Power  
Under transmit mode, the output power was measured using the Agilent N1912A Power Meter. Within the bandwidth of 1.2 GHz, GaN power amplifiers can work under saturation mode with input power varying from \(-2\) dBm to 2 dBm. The maximum duration for transmit is 100 microseconds with a duty ratio of 10%. The measured output power is larger than 42.3 dBm (17 W).  
Figure 10: Measured results of receive.  
Figure 9: Measured SWR of the receive input port.  
The measurements of transmit through the network analyzer are shown in Figure 11. A 30 dB attenuator is added in front of port 2 to protect the network analyzer from overpower. The insertion loss of the cable for measurement is about 3 dB.  
The measured output power is between 9.12 dBm and 9.76 dBm, with a difference of less than 0.7 dB.
## 5 Results
### Full Scale Testing  
Fatigue life assessment of components working in an H\({}_{2}\) environment is one of the topics of interest, for compressed H\({}_{2}\) storage vessels that undergo pressure cycling during operation. The actual fatigue assessment of similar components is based on fracture mechanics models, but there are conflicting opinions regarding the predictive capabilities of the models with respect to the test results, a sign that the topic must be further investigated. [PERSON] et al. [58,59] and [PERSON] et al. [60,61] performed H\({}_{2}\) full-scale cycling tests of pre-notched CrMo pressure vessels at 45 MPa and they demonstrated that the number of cycles to the failure found experimentally was higher than predicted by fracture mechanics design methods. On the contrary, [PERSON] et al. [62] showed as the number of cycles before the break was lesser than predicted by calculation and they attributed the differences to several parameters, such as cycling frequency, notch geometry, and crack morphology. Consequently, it is evident that the topic must be further investigated and that there is not a univocal testing procedure to address this issue. First pressure cycling full-scale tests in the \(\Delta\)H lab were performed to start investigating this issue. A preliminary test was performed  
Figure 5: (a) SEM fracture surface of SSR sample tested in H\({}_{2}\). (b) Lateral Image of SEM fracture surface of SSR sample tested in H\({}_{2}\). (c) Secondary crack image of SEM fracture surface of SSR sample tested in H\({}_{2}\). (d) Surface of the crack of SEM fracture surface of SSR sample tested in H\({}_{2}\).  
Figure 6: (a) SEM fracture surface area of SSR sample tested in N\({}_{2}\); (b) SEM image of the surface of SSR sample tested in N\({}_{2}\).  
cycling an internally pre-notched AISI 4145 vessel (see Figure 8a) between 5 and 45 MPa for 3500 cycles.  
The post-test analysis does not highlight any crack propagation in correspondence to the two notched areas (see Figure 8b), showing that this type of vessel can withstand a much higher number of cycles, as reported by [PERSON] et al. [58; 59] and [PERSON] et al. [59] on Cr-Mo steel pressure vessels. This preliminary test allowed to verify the suitability of the testing procedure and to obtain a first result. Further activities are planned to continue the investigations.
# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 2 Machine Learning, Deep Learning and Related Methods
The results achieved show that the proposed method obtains accuracies similar to those of the winners of the GEFCom14 competition, with a difference in terms of the pinball loss values obtained below 1%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Reference** & **Forecasting Horizon \& Sampling** & **Parameters** & **Tested on One** \\  & & & & **Location for Regional** \\ \hline \hline \multirow{2}{*}{2021} & [31] & 1-its steps-ahead & Foresonal irradiance & DELL model that uses a5 Dining data selection model based on query condition analysis (applied on irradiance values) and Poisson correlation (applied on product product evaluation). A novel decomposition method is 15 min & 1-its steps-ahead \\  & 15 min & Historical powers & One location & the forepage embedding time series, first training time series for DELL forecasting horizon 15 min & 1-its at a 1s. \\  & 15 min & Historical powers & One location & the forepage embedding time series for DELL forecast horizon 15 min & 1-its at a 1s. \\  & 15 min & Historical number & & (i)-its at a 1s. \\  & 15 min & Historical number & & (ii)-its at a 1s. \\  & 15 min & Historical number & & (iii)-its at a 1s. \\  & 15 min & Historical number & & (iv)-its at a 1s. \\  & 15 min & Historical number & & (v)-its at a 1s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Reference** & **Forecasting Horizon \& Sampling** & **Parameters** & **Tested on One Location** & **Methods \& Notes** \\ \hline \multirow{2}{*}{2021} & \multirow{2}{*}{[47]} & \multirow{2}{*}{1-6 h ahead (21 steps)} & Historical Power (morever level and plant level) & \multirow{2}{*}{One location} & \multirow{2}{*}{FFNN \& LSTM-RNN+GRU-RNN} \\  & & 15 min & Forecast altitude \& azimuth sum position & & \\  & & (prib-solar position) & & & \\ \hline \multirow{2}{*}{2021} & \multirow{2}{*}{[50]} & \multirow{2}{*}{[51]} & \multirow{2}{*}{1-24 h ahead} & Direct, diffuse and horizontal solar irradiance, & \multirow{2}{*}{Five locations} & \multirow{2}{*}{Gaussian process regression (GFR) with Statem 5/2 kernel function on pre-clustered data (by k-means)} \\  & & Hourly data & temperature, zenith \& azimuth sable position & \\ \hline \multirow{2}{*}{2020} & \multirow{2}{*}{[51]} & \multirow{2}{*}{1-24 h ahead} & Solar irradiance, temperature, humidity, & \multirow{2}{*}{One location} & \multirow{2}{*}{Quantile CNN (QCNN), two-stage training strategy to solve the training problem of the QCN-based
# Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
## 4 Problem Statement  
We consider systems described by real-valued attributes and a set of trajectories that describe the way these attributes vary over time. We aim to mine specifications that (1) describe such trajectories (2) in a way that the specifications are readable and interpretable for a human.  
Formally, let \(X=\{\mathbf{x}_{1},\dots,\mathbf{x}_{n}\}\) be a set of trajectories gathered from a system described by attributes \(A=\{a_{1},a_{2},\dots,a_{|A|}\}\). Let \(\Phi\) be the space of all possible STL formulas defined over \(A\). We aim at finding a \(\varphi^{*}\in\Phi\) that is both human-readable and describes \(X\). More specifically, \(\varphi^{*}\) should be _tight_ with respect to \(X\), i.e., the robustness value \(|\rho(\varphi^{*},\mathbf{x}_{i},t)|\) should be as much as possible close to zero for all \(\mathbf{x}_{i}\in X\) and \(t\). From another point of view, small perturbations on tight formulas \(\varphi^{*}\) should result in an overall increase in robustness for some trajectories of \(X\); i.e., some trajectories could be described by a tighter formula \(\varphi^{\prime}\
eq\varphi^{*}\).  
Tightness is of fundamental importance when no labels are provided; in fact, the satisfaction of a robustness metric can be trivially maximized by \"pushing\" the parameters towards the boundaries of the parameter space, resulting in rules that are of no relevance. For example, if \(a<0\) is satisfied by all trajectories, then \(a<1000\) will be satisfied as well and have a much greater degree of robustness.  
Concerning human-interpretability, a formal, widely accepted definition for STL formulas does not exist. Indeed, the lack of such a definition holds for many other kinds of Machine Learning modules. Moreover, it is acknowledged that human-interpretability is not, itself, universal: different subjects might perceive the same model as differently interpretable [22]. In this work, we circumvent the problem of defining interpretability with a practical approach: we force our method to search in a subset of \(\Phi\) containing structurally simple formulas, where both the overall size of the formula and the maximum degree of nesting of temporal operators are limited. The rationale of the latter constraint is in the fact that the interpretability of symbolic models is affected differently by different kinds of components [23].  
We remark that our problem is more complex than the mere specification mining of a template formula where the structure of the formula is already fixed (see Section 2), which searches only in the parameter space of the formula. In the easier case, the user has to provide a template formula \(\hat{\varphi}\), and the problem is a numerical optimization problem; i.e., the solution has to be found in \(\mathbb{R}^{m}\), where \(m\) is the number of numerical parameters in \(\hat{\varphi}\). In our case, no burden is on the user, but the system is required to search in \(\Phi\) rather than \(\mathbb{R}^{m}\). In Section 5, we detail the methodology we employed to address this problem statement.
# A Femto-Satellite Localization Method Based on TDOA and AOA Using Two CubeSats
## 4 Discussion and Conclusions
This work has been also partially funded by the grants: CONICYT PIA ACT1405, FONDECYT Regular 1151476, FONDECYT Regular 1211144, FONDECYT Regular 1211695, FONDECYT Regular 1221907, QUIMAL 190004 and CONICYT-PFCHA/Doctorado Nacional/2017-2117111. Not applicable. The figures and simulations can be reproduced using the Python script called AOAwithTDOA.py, which is available in the GitHub webpage [[https://github.com/spel-uchile/Pypredict](https://github.com/spel-uchile/Pypredict)]([https://github.com/spel-uchile/Pypredict](https://github.com/spel-uchile/Pypredict)), last accessed on 15 December 2021. We want to acknowledge [PERSON] and [PERSON] from SPEL for helping with the reviewing of this article. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 4 Experimental Results and Discussion  
The testing code was implemented under the MATLAB version R2014B. The proposed method was carried out on a standard workstation equipped with an Intel Core i5-4460 3.2 GHz CPU with 8 GB of main memory, an NVIDIA GeForce GT 745 graphics card and Windows 8.1 OS.  
In our work, we used fabric defect images from the automation laboratory sample database of Hong Kong University, TILDA Textile Texture Database and Guang Dong Esquel Textiles with a resolution of 600 dpi, scanned by Canon Scanner 9000F. The images have a size of 256 \(\times\) 256 pixels and an 8-bit grey level. Various fabric images (including plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric, and statistical-texture fabrics) were used for evaluating our method.
# Photoacoustic Imaging with a Finite-Size Circular Integrating Detector
## 4 Test and Discussion  
In this section, we use K-Wave to simulate the generation of blurred pressure distribution images in PAI and test the proposed algorithm to verify its validity [35]. First, we validate the algorithm by calculating a small number of initial pressure distributions, which involves computing the eigenvalues and the eigenvectors corresponding to the minimum eigenvalue. Based on these results, we estimate the blurring kernel. The estimated blurring kernel was then applied to the deblurring process, effectively restoring a clear pressure distribution image. Furthermore, this section explores how to improve the efficiency and accuracy of blurring kernel estimations by selecting an appropriate number of radial column samples from the blurred pressure distribution in the polar coordinate system. To further evaluate the algorithm's performance, we compared it with the traditional Wiener deconvolution method. The results demonstrated that the proposed deconvolution method outperformed the Wiener deconvolution method in terms of processing accuracy and image quality. Finally, to better reflect real-world scenarios, simulated noise was introduced during the tests to comprehensively assess the robustness and performance of the optimized algorithm under complex conditions.
# RF Energy Harvesting Wireless Communications:
## 3 RF-EHWC Hardware
### Design Principles of RF Energy Harvester
#### 3.1.1 Impedance Matching Circuit  
To maximize the efficiency of energy conversion, the input impedance of a receiving antenna and the output impedance of the receiving circuit should be identical; otherwise, part of radio energy received by the antenna will be reflected back to the environment.  
The matching circuits for energy harvest and data reception are slightly different. As illustrated in Figure 8, for the data reception, the antenna's load is a front-end amplifier, which usually has 50 \(\Omega\) input resistance. Therefore, the antenna can simply match its output impedance to 50 \(\Omega\) through a transmission line, which is typically a coax cable. For the energy harvest, the antenna's load is a rectifier and a voltage multiplier. Its input impedance depends on the structure of the circuit. Accordingly, we need a specifically designed matching circuit between the antenna and the rectifier to match the input impedance of the rectifier with the output impedance of the receiving antenna [3]. The matching circuit is composed of inductive and capacitive components, as shown in Figure 8.
## 4 Materials and Methods: Simulation and Assumptions  
The proposed direct ammonia system for the SOFC-GT-SRC-KC-ORC integrated system was simulated using ASPEN-HYSYS, which offers a large database and powerful methods for calculating physical parameters [39; 40]. The simulation used REFPROP of the Aspen Physical Property System [39]. The Peng-Robinson (PR) equation of states was selected for estimating the thermodynamic properties of the compositions of the stream and operating conditions of all components of the SOFC-GT-SRC-KC-ORC integrated system. The spreadsheet tool is employed to calculate cell voltage, fuel and air utilization factors and electrical aspects of the SOFC under the given current density value [41]. This is also used to run of process variables and post-processing calculated. The trial and error method is selected to evaluated and determined the average stack operating temperature of the SOFC. The internal reforming module is used to model the temperature profile of the fuel cells and the change of composition in the internal reforming process. These steps are repeated until the assumed average cell temperature and the calculated average cell temperature agree.  
The following general assumptions were made to simplify the thermodynamic analysis of the system:  
1. The ammonia entering the fuel gas supply system was at 29.85 \({}^{\circ}\)C and 400 kPa.
2. The pressure drop in all the pipe components was ignored.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Exergy Destruction Rate** & \\ \hline Gas turbine & \(\dot{E}x_{9}-\dot{E}x_{10}-\dot{W}_{Gass\ turbine}=\dot{E}x_{des}\) & (67) \\ \hline Air heat exchange (Air Hex) & \(\dot{E}x_{1}+\dot{E}x_{10}-\dot{E}x_{2}-\dot{E}_{11}=\dot{E}x_{des}\) & (68) \\ \hline Fuel heat exchange (Fuel Hex) & \(\dot{E}x_{ammonia}\ from\ tank+\dot{E}x_{11}-\dot{E}x_{12}-\dot{E}_{3}=\dot{E}x_{ des}\) & (69) \\ \hline SRC regenerator (SRC Hex) & \(\dot{E}x_{12}+\dot{E}x_{16}-\dot{E}x_{17}-\dot{E}_{13}=\dot{E}x_{des}\) & (70) \\ \hline SRC turbine & \(\dot{E}x_{17}-\dot{E}x_{18}-\dot{W}_{S,\_Turbine}=\dot{E}x_{des}\) & (71) \\ \hline KC regenerator (KC Hex) & \(\dot{E}x_{13}+\dot{E}x_{22}-\dot{E}x_{14}-\dot{E}_{23}=\dot{E}x_{des}\) & (72) \\ \hline KC turbine & \(\dot{E}x_{25}-\dot{E}x_{26}-\dot{W}_{K,\_turbine}=\dot{E}x_{des}\) & (73) \\ \hline ORC regenerator (ORC Hex) & \(\dot{E}x_{14}+\dot{E}x_{34}-\dot{E}x_{15}-\dot{E}_{35}=\dot{E}x_{des}\) & (74) \\ \hline ORC turbine & \(\dot{E}x_{35}-\dot{E}x_{36}-\dot{W}_{O,\_Turbine}=\dot{E}x_{des}\) & (75) \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._The pressure drops on the tube side and shell side of the heat exchange were 34.47 and 6.895 kPa, respectively.  
The system was in a steady state, and its heat loss was ignored.  
The air is mainly composed of 79% N\({}_{2}\) and 21% O\({}_{2}\) at 29.85 \({}^{\circ}\)C and 100 kPa  
The minimum temperature approach of the heat exchangers was 7 \({}^{\circ}\)C.  
The reference state temperature and pressure of the system were 29.85 \({}^{\circ}\)C and 1.013 bar, respectively.  
The simulation boundary conditions are presented in Table 5[11,19].
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 4 Diagnosability Design Based on Structural Analysis
### Inherent Diagnosability Design
#### 4.1.2 Sensor Configuration  
Faults in the structural overdetermination are detectable. But whether or not they can be isolated depends on whether or not the fault is in a different structurally overdetermined part. Therefore, for non-isolatable faults, the sensitive relationship between the fault and the structural overdetermination of the system can be optimized by sensor configuration, increasing the fault's isolability.  
[PERSON] proposed a sensor configuration algorithm based on structural analysis. First, a causal structural model was established to analyze the fault's structural detectability and isolation. Second, based on the causal relationship between variables, the sensor configuration algorithm was designed to minimize the sensor configuration cost while ensuring the fault diagnosis capability. The causal relationship was fully considered in order to find the set of MSOs used to generate the residuals [54]. [PERSON] investigated the sensor configuration problem of linear systems to analyze the impact of the location and number of sensors on the fault diagnosis performance. First, from the perspective of structural analysis, the influence of the positional distribution of sensors on the fault diagnosis performance was analyzed. Through structural analysis, redundant sensors that did not contribute to fault diagnosis were removed. Second, a simplified sensor configuration system model was established to transfer the sensor configuration problem of the original system to the simplified system, which dramatically simplified the difficulty of sensor configuration. Finally, considering the number and cost of sensor configurations, an optimization algorithm was designed to obtain the optimal solution. The optimal solution not only ensured the fault diagnosis performance but also ensured that the number of sensors and the cost were minimized [55]. [PERSON] addressed the sensor configuration problem of the anticlock braking system to ensure that the fault diagnosis needs were met; the ARR was increased through the sensor configuration, which increased the fault diagnosis performance [56]. [PERSON] transformed the problem of diagnosing a system's faults into solving the system's minimum structural overdetermination and generating the system's residuals using the set of the minimum structural overdetermination [57]. [PERSON] analyzed the impact of the number and type of sensors on the fault ability of the pneumatic system. He classified the diagnosability of pneumatic system faults and proposed a structural analysis-based fault diagnosis method. First, a structural analysis model was established to analyze the structural detectability and isolability based on DM decomposition. A sensor configuration algorithm was designed to optimize the sensor configuration scheme [58].
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 3 Proposed Method
### Optimization Stage
#### 3.3.4 Reverse Transformation  
After solving (11) using G2O, the optimal local path \(B^{*}=\{\hat{s}_{i},\hat{d}_{i}\}\) can be obtained. However, since \(B^{*}\) is still in the planning coordinate system, a coordinate transformation is required to return it to the reference coordinate system. The formula for coordinate transformation is expressed as  
\[\begin{bmatrix}\hat{x}_{i}\\ \hat{y}_{i}\\ 1\end{bmatrix}=\begin{bmatrix}cos\theta&-sin\theta&x_{0}\\ sin\theta&cos\theta&y_{0}\\ 0&0&1\end{bmatrix}\cdot\begin{bmatrix}\hat{s}_{i}\\ \hat{d}_{i}\\ 1\end{bmatrix} \tag{16}\]  
where \((\hat{x}_{i},\hat{y}_{i})\) represents the coordinates of the optimal local path in the reference coordinate system.