esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Application of Lining Resistance to Water Pressure in Tunnel through Karst Cave
## 4 Engineering Application
### Field Test Verification  
To verify the reliability of the above numerical calculations, relevant field tests were performed on the water resistance of the structure after the construction of the tunnel at the New Yuanliang Mountain. During the rainy season, the water pressure in the cavity section  
Figure 19: Construction diagram of CRD method and CD method. (**a**) CRD method construction procedure diagram, in which excavation sequence installation 1–2–3–4–5–6 was carried out; (**b**) CD method construction procedure diagram, in which the excavation sequence installation 1–2–3–4–5 was carried out.  
Figure 20: Comparison of tunnel settlement caused by CRD method and CD method during excavation: (**a**) comparison of vertical settlement of archtop; (**b**) comparison of horizontal convergence of arch waist.  
was monitored and measured, and the selected sections DK340+300 and DK340+377 were tested for the water pressure between the initial support and the secondary lining, the stress on the secondary liner rebar, the contact pressure between the initial support and the secondary lining, etc. The sensor arrangement and field installation of each monitoring section are shown in Figure 21.  
The results of field monitoring are shown in Figure 22, which showed that, during the month-long monitoring at the rainy season, the results of all monitoring points of the two sections with respect to the water pressure behind the lining were lower than 0.1 MPa, reflecting the obvious efficiency of the above construction water resistance technology. The monitoring results of the rebar meter inside the second liner were all around 30 MPa, which indicated that the second liner was stable in force, and that the structure had good performance in resisting water pressure. As for the contact pressure between the initial support and the secondary lining, the pressure at the top of the arch was slightly larger than that at the foot and waist of the arch, about 0.5 MPa, and the pressure at the foot of the arch was the smallest, about 0.4 MPa, which was because the top of the arch featured vertical pressure, while the foot of the arch featured lateral pressure. The pressure at the waist of the arch was just between the two, consistent with the actual situation. Summarizing the results, it can be obtained that the actual construction using the abovementioned grouting excavation scheme allowed the grouted stone body and the lining structure to effectively meet the requirements of the project for the hydraulic pressure resistance, which qualitatively reflected the accuracy of the numerical results.  
Figure 21: Diagram of the layout of the monitoring section measurement points and the installation of instruments on site: (**a**) monitoring section measurement point layout diagram; (**b**) on-site water pressure meter installation diagram; (**c**) on-site rebar meter installation diagram; (**d**) on-site earth pressure meter installation diagram.
# Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoders
## 3 Proposed Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoder
### Structure of Forward Routing Network  
In this section, an efficient reconfigurable message passing network based on the barrel shifter and an additional forward routing stage is presented. Please note that the maximum input size that can be supported by the circular-shift network is \(P\), the block size of the input data for the circular shift is \(Z\), and the value of the required circular shift is \(c\), where \(0\leq c<Z\leq P\). The structure of the Forward Routing Circular-Shift (FRCS) network is illustrated in Figure 5a, which includes 3 stages. The first stage is the barrel shifter, the second stage is the forward routing module, and the final stage is the selection scheme. The barrel shifter with input size \(P\) in the first stage can be easily constructed from \(\lceil log_{2}P\rceil\) stages of 2-to-1 MUXs. The barrel shifter circularly shifts the input message \(shift\_in\) according to the specific shift amount \(c\). The control signals for the barrel shift network can be easily determined from the value of the shift amount \(c\). In the next step,  
Figure 3: Pre-rotator and MUX network structure.  
the forward routing module simply takes the value of the \((P-Z)\)th to \((P-1)\)th shifted message \(BS\_out\) as input data and fetches into next stage. The shifted message from the barrel shifter \(BS\_out\) and the forward routing message \(fw\_out\) is fed into the final stage to generate the expected output. The selection stage is in charge of selecting a signal from the barrel shifter or the forward routing network and routing a proper signal to the output. In the selection stage, the expected output message \(shift\_out\) is determined by a MUX network where the control signal \(FRCS_{ctrl}\) is defined as follows:  
\[FRCS_{ctrl}[i]=\begin{cases}0&\text{ if }0\leq i<Z-c,\\ 1&\text{ if }Z-c\leq i<Z.\end{cases} \tag{1}\]  
According to the controller design, the final output messages \(shift\_out[i]\) comes from the forward routing module if \(0\leq i<Z-c\) and from the barrel shifter if \(Z-c\leq i<Z\). An output block \(shift\_out[i]\) with \(i>Z\) can have any value because its value is ignored in the following blocks. Figure 5b illustrates the operation of the FRCS network with \(Z=8,P=12\) and \(c=3\). The control signal \(FRCS_{ctrl}\) is defined as follows:  
\[FRCS_{ctrl}[i]=\begin{cases}0&0\leq i<5,\\ 1&5\leq i<8.\end{cases} \tag{2}\]  
Figure 4: (**a**) Lifting size \(Z\) table; (**b**) Modified lifting size \(Z\) table for standard 5G LDPC codes.
## 1 Introduction
They discussed the stiffness, load transfer, and stress concentration on the joints from the FE results, and the effects of staggered patterns on the additional cracks were investigated. [PERSON] et al. [12] performed a 3d FE analysis to investigate the failure stages of double-lap-bolted joints. Using comparisons of results from laboratory experiments in their research, they recommended that FE analysis and artificial neural networks trained using FE results can be good alternatives for predicting the mechanical behavior of double-lap bolted joints while reducing the costs required for experiments. [PERSON] and [PERSON] [13] performed a detailed FE analysis considering welded and bolted joint connections to investigate the performance of collar rigid connections by using the commercial FE software ABAQUS 2021. Their numerical results were validated by detailed comparative studies with experimental results for the joints. A discussion of the cyclic behavior of the rigid collar joint indicated that the large collar joint will exhibit appropriate seismic behavior, have no plasticity, and not lead to local buckling. [PERSON] et al. [14] developed a local FE model to account for local strain and stress at rivet joints. Their FE model was combined with a global FE model and utilized to evaluate the internal stress field and fatigue life of an old steel bridge in Egypt for which the structural documents and drawings had been lost. Detailed modeling techniques for their FE model were found in the literature [15]. Given the high costs of FE-riveted joint models, they assumed that the backing plates below the angles should be ignored. Modeling the interaction between steel components is crucial in the finite element modeling of steel rigid collar connections. Extensive research has utilized surface-to-surface interactions to depict the interactions between steel components. To specify the tangential and normal characteristics of the contact between surfaces, the penalty method and hard contact are assumed, respectively. The Coulomb friction coefficient for steel-to-steel interactions has shown variations in the literature [16; 17; 18; 19; 20]. In this study, the structural details of a hinge connection for steel girder segments were developed to improve the fundamental challenges related to the welded and bolted field joints of steel girder segments while also ensuring applicability to rapid construction sitessuch as emergency bridges (referred to as the \"speedBridge\" hereafter). In the speedBridge, the load acting on the compression flange of the segment girder is resisted by two bearing blocks, whereas that on the tension flange is resisted by hinge structures installed at both ends of the lower flange of the steel girder. The performance of the hinge connection structure details of the speedBridge steel girder segment proposed in this study was tested experimentally. In addition, an inelastic finite element (FE) analysis was conducted to determine the stress distribution of the connection.
# Passive UHF RFID Tag for Multispectral Assessment
## 3 Results and Discussion
### Validation and Application in a Real Situation  
Apart from the possibility of identifying different illuminants, this passive tag has been used to conduct reflective colour-sensing measurements. We first carried out a validation of this type of measurement using a colour chart as the target. Figure 7 shows the results for seven plain-coloured cardboards: the three primary colours (red, blue, and green), white, yellow, orange, and brown. The illuminant is a cool white LED and the setup is as depicted in Figure 4. It can be observed that the spectrum of the incident light measured with the spectrometer has been modified in accordance with the cardboard surface colour. The spectral fingerprints obtained with the RFID tag also reflect the colour change, and the relative heights of its five coordinates allow the identification of the cardboard colour as well.  
Along the same line, we propose an application for our RFID tag in a real situation: identification of the ripening stage of fruit. To this end, we consider a banana as the target and monitor the progress of its ripeness from immature to overripe (see Figure 8a). The banana can be classified as immature, ripe, or overripe according to the relative positions of the five components in its spectral fingerprint. Figure 8b shows photographs of the banana at each ripening stage. Given the colour shift from green to dark brown, an illuminant with a high green component, such as the one used in this study, could be the most suitable one to measure the ripening process.  
Table 1 presents the spectral fingerprint values obtained with our RFID tag for the different targets considered: on one hand, the plain-coloured cardboards used for the validation process, and on the other hand, the banana used for its ripening stage identification. In these cases, most of the information is contained in the blue, green, and red coordinates. This is due to the spectral characteristics of the light source used as an illuminant. Nevertheless, this information is enough to identify the colour in the case of the cardboards and the ripening stage in the case of the banana. Other radiation sources with significant spectral information in the ultraviolet or infrared regions could also be used as illuminants.  
Figure 7: Comparison of the normalized spectral responses measured with the spectrometer and the spectral fingerprints obtained with the RFID tag for reflective colour-sensing experiments with: (**a**) red, green, and blue cardboards; (**b**) white, yellow, orange, and brown cardboards.
## 6 Experimental Results
### Performance Evaluation of LAC-KHM  
In what follows, LAC-KHM's efficiency is evaluated via comparisons with k-medians, k-medoids, k-means++, k-means, and standard LAC on seven different datasets, the features of which are displayed in Table 1. In addition, the input parameters of the functions in the LAC-KHM algorithm are also explained. Details of the dataset features and their initial values can be seen in this table as well. Additionally, the parameters of the LA are set according to those of [12].  
Similarly, the results of checking the accuracy of the given algorithm compared to the rest are shown in Table 2. As can be seen in this table, LAC-KHM achieves the best results.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Instances** & **Dimensions** & **Clusters** & **Membership\_Cluster** & **LA Initial Prob** \\ \hline BCW & 683 & 10 & 2 & 2 & 1/2 \\ Sonar & 208 & 60 & 2 & 2 & 1/2 \\ CMC & 1473 & 9 & 3 & 3 & 1/3 \\ Hayes-Roth & 132 & 5 & 3 & 3 & 1/3 \\ Ionosphere & 531 & 34 & 2 & 2 & 1/2 \\ Sonar & 208 & 60 & 2 & 2 & 1/2 \\ Pima & 768 & 8 & 2 & 2 & 1/2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of the datasets used in this research.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 4 Analysis of ACF and PSD
### Modulation Parameter Analysis  
In mathematical model Equation (2) of the OFDM navigation signal, listed are the main parameters in the OFDM modulation process: \(K\) is the number of subcarriers, \(\Delta f\) is the subcarrier interval, and \(T_{g}\) is the length of the protection interval. A unique OFDM modulation method can be determined by setting these four parameters. In this summary, we discuss the impact of the OFDM parameters on the ACF and PSD, which helps signal designers choose the appropriate OFDM parameter.
# Observing APOD with the AuScope VLBI Array
## 1 Introduction
Only actual tests are capable of revealing certain deficiencies on the data acquisition level, such as tracking issues, and allow for the experience required to adopt existing VLBI infrastructure components--hardware as well as software--according to the needs of satellite observations. Hence, today's experiments--although they may not deliver highly accurate results--provide a great test bed for future missions with potentially a high impact on geodesy. In this paper, we discuss VLBI observations of the APOD-A nano satellite (hereinafter abbreviated as APOD). APOD can be considered as the first realization of a co-location LEO satellite combining the techniques SLR, GNSS, and VLBI on one platform (details are provided in Section 2.2). We describe a set of intensive tracking sessions to APOD using the AuScope VLBI array in Australia [26]. We focus on the data acquisition scheme for observations of a fast moving LEO satellite on regional baselines and discuss the resulting challenges for the tracking procedures and the data processing. The established process chain for our experiments is outlined in Figure 1. All tasks and processing steps are described in detail in the following sections. Section 2 provides an overview of all experiments discussed in this paper, and introduces the station network and the observation target. The observations, including all steps from scheduling with the Vienna VLBI and Satellite Software (VieVS [27]) to the actual data acquisition, are discussed in Section 3. In Section 4, the correlation and post-processing steps, based on the software correlator DiFX [28] and the Haystack Observatory Post-processing System (HOPS, [[http://www.haystack.mit.edu/tech/vlbi/hops.html](http://www.haystack.mit.edu/tech/vlbi/hops.html)]([http://www.haystack.mit.edu/tech/vlbi/hops.html](http://www.haystack.mit.edu/tech/vlbi/hops.html))), are described. The analysis of the derived delay observables using VieVS is presented in Section 5. The paper is concluded with a discussion and future prospects in Section 6.
## 2 The TL-ERT Method: Data Processing and Inversion
### Novel Algorithms for Data Processing and Inversion
As it concerns the TL-ERT data, [PERSON] et al., 2017 [67] used open-source codes to simulate spatio-temporal changes of subsurface resistivity. The module PFLOTRAN-E4D operating in parallel modality was applied to reconstruct the bulk electrical conductivity from a subsurface flow and transport simulation code. At each step of the iterative process, a tomographic image is simulated using the bulk electrical conductivity model. This multi-physics approach appears of great interest for the availability of open-source codes. [PERSON] et al., 2017 [68] introduced an open-source framework for the inversion of a hydro-geophysical dataset. A pyGIMLi (Python Library for Inversion and Modelling in Geophysics) was implemented for solving the minimization problem with a Gauss-Newton algorithm for any forward operator. The method was used for incorporating hydrological and petrophysical constraints in the ERT data inversion. [PERSON] et al., 2020 [69] introduced the open-source code RESIPy for the inversion of resistivity and induced polarization data. The code can also be used for the analysis of TL-ERT data. It is formed by three different layers: (i) the first one is composed of inversion codes R2, R3t and CR2 and a software to generate triangular meshes; (ii) the second layer is composed of Python API, containing processing routines for filtering and error modeling; (iii) the last is composed of routines for graphical application mainly devoted to data visualization. [PERSON] et al., 2020 [70] proposed a method for the fast inversion of the 4D ERT data that considers the problems related to the rapid changes in the resistivity subsurface model. Generally, the ERT inversion is time consuming, and during the period necessary for the data inversion a resistivity change in the subsurface could occur. The algorithm subdivided an entire data set into a multiple subset of data, that were dynamically substituted in the equations for data inversion. In this process, the regularization coefficients are adjusted; furthermore, a GPU parallel algorithm is used for the TL-ERT data inversion. Finally, in Table 1 we summarize the different methods adopted for TL-ERT data inversion. At each class is associated a list of the selected works previously discussed. Of course, the list is not exhaustive, and does not include all of the published papers.
## 3 Results
### Runoff Analysis
#### 3.1.3 Theoretical Runoff Estimation  
The years with observed runoff data and the years with runoff turning points (where the runoff changed from increasing to decreasing or from decreasing to increasing) were chosen in the theoretical runoff as typical years, and the results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**1-Day Rainfall**} & \multicolumn{4}{c}{**3-Day Rainfall**} \\ \cline{2-9}
**Year** & **20-Year** & **10-Year** & **5-Year** & **3-Year** & **20-Year** & **10-Year** & **5-Year** & **3-Year** \\ \hline
1991 & 39.26 & 30.50 & 17.60 & 13.01 & 55.94 & 44.85 & 32.82 & 22.81 \\
1995 & 40.33 & 31.33 & 18.09 & 13.37 & 57.40 & 46.01 & 33.65 & 23.38 \\
2009 & 41.10 & 32.07 & 18.80 & 14.07 & 55.87 & 44.47 & 32.07 & 21.78 \\
2010 & 41.12 & 32.10 & 18.82 & 14.09 & 55.82 & 44.42 & 32.02 & 21.72 \\
2014 & 41.93 & 32.87 & 19.54 & 14.80 & 54.39 & 42.95 & 30.50 & 20.16 \\
2015 & 42.13 & 33.06 & 19.72 & 14.97 & 54.03 & 42.58 & 30.12 & 19.77 \\
2016 & 43.30 & 34.03 & 20.41 & 15.56 & 54.32 & 42.63 & 29.91 & 19.35 \\
2018 & 46.16 & 36.46 & 22.18 & 17.10 & 55.58 & 43.34 & 30.00 & 18.93 \\ Annual change rate & 25.56\% & 22.07\% & 16.96\% & 15.15\% & \(-1.33\%\) & \(-5.59\%\) & \(-10.44\%\) & \(-14.37\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Theoretical runoff under 1-day and 3-day rainfall with different return periods (mm).  
Figure 2: Change of land use area in the study area in recent 30 years.  
The runoff increased when the return period was longer under the same rainfall duration or the rainfall duration was longer under the same return period in Table 2. There was a positive correlation between the return period and the runoff and between the rainfall duration and the runoff.  
The annual runoff change rate of 1-day rainfall increased with the increase in the return period. The runoff of 3-day rainfall decreased slowly with time, so the annual runoff change rates were negative values. According to the absolute value of the runoff change rate under 3-day rainfall, the annual runoff change trend decreased with the increase in the return period, and the annual runoff change trend of 1-day rainfall was greater than that of 3-day rainfall.
## 3 Discrete Formulation of Topology Optimization Problem Statements and Sensitivities
### Topology Optimization Problem  
The discrete version of Problem (1) results in:  
\[\min_{\rho}:J(\rho,u(\rho)) \tag{31}\]  
subject to:  
\[\rho_{min}\leq\rho_{e}\leq 1\ \forall\ e=1,\ldots,N \tag{32}\]  
\[V(\rho)=\sum_{\rho}\rho_{e}v_{e}\leq V^{*} \tag{33}\]  
where, as previously defined, \(J(\rho)\) is the cost function, the density \(\rho\) represents the design variables, which can vary within the interval \([\rho_{min},1]\), \(V(\rho)\) is the total volume, with \(v_{e}\) representing the volume of element \(e\) when the design density \(\rho=1\), \(V^{*}\) is the maximum value of the volume constraint, and \(u(\rho)\) is the solution displacement vector of the state problem:  
\[F=K(\rho)u. \tag{34}\]  
The stiffness matrix \(K(\rho)\) and force vector \(F\) are defined as, respectively:  
\[K(\rho)=\int_{\Omega}B_{a}^{T}\mathbb{C}B_{a}dx \tag{35}\] \[F=\int_{\Omega}N\ f\ dx+\int_{\partial\Omega}N\ t\cdot n\ ds \tag{36}\]  
where \(B_{a}\) is the strain-displacement matrix in the evaluation point \(a\), \(N\) a linear shape function, and \(f\) a generic load function, while \(t\) indicates the applied boundary forces, if they exist, and \(n\) is the normal direction pointing outwards of the boundary \(\partial\Omega\).  
When considering a maximum stress constraint, the problem statement is also subject to:  
\[\sigma^{PN}(\rho)\leq\sigma^{*} \tag{37}\]  
where \(\sigma^{PN}(\rho)\) represents the maximum stress and \(\sigma^{*}\) its the maximum allowable value.  
Note that the penalization factors and regularization process described in Section 2.2 are not changed.
# Robust Algorithm Software for NACA 4-Digit Airfoil Shape Optimization Using the Adjoint Method
## 3 Methodology
### Geometry Generation of the NACA Four-Digit Airfoil  
The NACA airfoil was initially developed for aircraft, but it has also found applications in wind turbines. In the literature, the four- and five-digit types of NACA (National Advisory Committee for Aeronautics) have been extensively investigated. The coordinates of the airfoil \((x,y)\) in a plane \((xy)\) are not dimensionless with respect to the chord. Airfoils with NACA four-digit designations have piecewise parabolic mean camber lines \(y_{c}\) and thickness distributions \(y_{t}\) that are consistent across designs. The thickness distribution is given by  
\[y_{t}=\pm 5 tc\Big{(}0.2969\sqrt{x}-0.1260x-0.3516x^{2}+0.2843x^{3}-0.1015x^{4} \Big{)} \tag{1}\]  
where \(t\) is the thickness ratio and \(c\) is the chord length of the airfoil. The mean camber line \(y_{c}\) comprises two sections, which are defined by different parabolic curves, as in \[y_{c}=\left\{\begin{array}{ll}\frac{m}{p^{2}}(2 px-x^{2})&0\leq x<p\\ \frac{m}{(1-p)^{2}}((1-2p)+2 px-x^{2})&p\leq x\leq 1,\end{array}\right. \tag{2}\]  
where \(p\) is the maximum camber position along the chord length and \(m\) is the camber ratio. In other words, the complete set of the coordinates \((x,y)\) of the NACA airfoil can be described by two curves, i.e., the upper and lower surfaces, which are defined by  
\[\begin{array}{ll}y_{\text{upper}}&=y_{c}+y_{t}\cos(\theta)\\ x_{\text{upper}}&=x-y_{t}\sin(\theta)\\ y_{\text{lower}}&=y_{c}-y_{t}\cos(\theta)\\ x_{\text{lower}}&=x+y_{t}\sin(\theta),\end{array} \tag{3}\]  
where,  
\[\theta=tan^{-1}\frac{dy_{c}}{dx}. \tag{4}\]  
To summarize, the first digit of a NACA airfoil indicates the maximum mean camber, the second digit indicates the position of the maximum camber, and the last two digits indicate the maximum thickness. The NACA4418 airfoil selected as an initial condition for the optimization process described in the following is an asymmetric airfoil with a maximum mean camber of 4% located at a distance of \(0.4c\) and a maximum thickness of 18%, which is \(t=0.18\).