esa-sceva/satcom-chunk-collection huggingface dataset:
# Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
## 7 EGS for Measuring Graph Similarity
### Inferring Parameters of Random Graph Models  
We investigate whether one can recover parameters of random graph models via the learned EGS of the graph instances. We generate a random graph with a given size and parameters and learn its entropic spectral characterisation using our EGS learner (Algorithm 2). Then, we generate another graph of the same size but learn its parameter value by minimising the symmetric-KL divergence between its entropic spectral surrogate and that of the original graph. We consider three different random graph models, i.e., Erdos-Renyi (ER) with edge probability \(p\), Watts-Strogatz (WS) with rewiring probability \(p\), and Barabasi-Albert (BA) with number of edges \(r\) attached to each new node, and different graph sizes (\(n=50,100,150\)). The results are shown in Table 1. It can be seen that, given the approximate EGS, we are able to infer the parameters of the graph producing that spectrum.  
\begin{table}
\begin{tabular}{l c c c} \hline \(n\) & **50** & **100** & **150** \\ \hline ER (\(p=0.6\)) & 0.600 & 0.598 & 0.604 \\ WS (\(p=0.4\)) & 0.468 & 0.454 & 0.414 \\ BA (\(r=0.4n\)) & 18.936 & 40.239 & 58.428 \\ \hline \end{tabular}
\end{table}
Table 1: Average parameters estimated by our MaxEnt-based method for the 3 types of network. The number of nodes in the network is denoted by \(n\).  
Figure 2: (a) EGS fit to randomly generated Erdős-Rényi graph (\(n=5000\), \(p=0.001\)). The number of moments \(m\) used increases from 3 to 100 and the number of bins used for the eigenvalue histogram is \(n_{b}=500\). (b) EGS fit to randomly generated Barabási-Albert graph (\(n=5000\)). The number of moments used for computing EGSs and the number of bins used for the eigenvalue histogram are \(m=30\), \(n_{b}=50\) (**Left**) and \(m=100\), \(n_{b}=500\) (**Right**).
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 4 FoMs Analysis
### Spectral Efficiency Analysis Results  
Considering that the S-band has a narrow bandwidth of 16.5 MHz, the use of a signal with high spectral efficiency is required. Table 5 shows the spectral efficiency of the candidates calculated using Equation (6). Among the BPSK signals, BPSK(1) had the highest spectral efficiency with 0.99, and BPSK(8) had the lowest with 0.91. Among the BOC signals, BOC\({}_{\rm s}\)(1,1) had the highest spectral efficiency with 0.97, and BOC\({}_{\rm c}\)(4,4) had the lowest with 0.75. A common feature of BPSK(8) and BOC\({}_{\rm c}\)(4,4) is that they do not have any side lobes within the allocated S-band. In the case of having the same subcarrier frequency and chip rate, the sine-BOC signal had higher spectral efficiency than the cosine-BOC signal.
###### Abstract  
Modern marine seismic reflection data have allowed for the study of the morphology of submarine channels in a 3D perspective. This study presents a quantitative analysis of continental slope channel complexes morphology within the Canterbury Basin (New Zealand). It aims to characterize the morphology and seismic facies of submarine channels to better understand their formative sedimentary processes, paleoenvironment, and petroleum prospectivity. Submarine channel morphometric parameters are measured at 150 m intervals perpendicular to the axis of channels complexes. Based on the morphology analysis of erosional surfaces and seismic facies of channel complexes filling deposits, four types of continental slope channel complexes are found in the study area. These are vertical migrating channels, lateral migrating channels, V-shaped channels, and U-shaped channels. Furthermore, our work shows that channel morphometry varies over distances of 0.1 to 14 km in this continental slope system. These changes indicate a combination of submarine gravity flow process and channel wall collapse responsible for the development of continental slope channel complexes. Regionally, the evolution of the channels indicated less significant regional plate movement. This quantitative seismic geomorphology approach of characterizing submarine channels system has broader applications for better interpretation of paleoenvironment and petroleum prospectivity within frontier basins.  
Keywords:seismic geomorphology; submarine channels; continental slope; channels complexes; morphometry; paleoenvironment; petroleum prospectivity; Canterbury; New Zealand +
Footnote †: journal: Applied
# The Influence of Chamfered and Rounded Corners on Vortex-Induced Vibration of Super-Tall Buildings
###### Abstract  
In order to study the influence of chamfered and rounded corners on vortex-induced vibration of super-tall buildings, a series of multi-degree-of-freedom (MDOF, in short) aeroelastic models were made, including chamfered-corner models with chamfer ratios of 0%, 2.5%, 5.0%, 7.5%, and 12.5%, and rounded-corner models with rounded ratios of 0%, 12.5%, 15%, and 17.5%. The crosswind displacement response of each aeroelastic model was measured by a laser displacement meter in a wind tunnel. The test results indicate that the crosswind displacement response is significantly reduced when the chamfer ratio increases to 5% or the rounded ratio increases to 12.5%; in particular, the vortex-induced vibration (VIV, in short) response can be reduced by more than 60%, which indicates that the chamfered corner and rounded corner play a good role in restraining the VIV of super high-rise buildings. When the reduced wind speed is small, the reduction of the crosswind response of chamfered and rounded models is mainly caused by the reduction of wind load; when the reduced wind speed approaches the VIV wind speed, the negative aerodynamic damping phenomenon of the model with a proper chamfer ratio and rounded ratio disappears, which shows that the VIV is suppressed not only because the cut corner reduces the wind load but also because the cut corner weakens the aeroelastic effect. Generally speaking, a chamfer ratio of about 7.5% and rounded ratio of about 15% can achieve a good reduction effect on the VIV of super high-rise buildings.  
tall buildings; vortex-induced vibration; MDOF aeroelastic model; chamfered-corner; rounded-corner 2023  
13 1049 10.3390/app13021049
## 5 Results and Analysis
### Factors Affecting Bearing Stability  
The trend of maximum airflow velocity with changes in the orifice diameter under various air supply pressures is depicted in Figure 14 for a fixed length \(a\) of the rectangular orifice array at 40 mm and an air film thickness of 20 \(\upmu\)m. With a fixed gas supply pressure, it is observed that an increase in the throttle hole diameter results in a reduction in maximum air velocity in the gas film gap, suggesting enhanced stability of the bearing.  
The relationship between maximum airflow velocity and air film thickness variations for differing supply air pressures is outlined, with the specifics provided including a 40 mm length for the rectangular orifice array and a 0.2 mm diameter for the orifice, as displayed in the data of Figure 15. Holding the supply gas pressure steady shows that a reduction in the thickness of the gas film is directly associated with a decrease in the peak airflow velocity within the gas film gap, which serves as an indication of the bearing's stability.  
As demonstrated in the data from Figure 16, variations in orifice distribution under varied supply air pressures affect the maximum air velocity when considering a constant supply orifice diameter of 0.2 mm and an air film thickness of 20 \(\upmu\)m. Keeping the supply gas pressure unchanged, it is noted that an increase in the orifice spacing, referred to as \(a\), leads to a rise in the maximum airflow velocity across the air film clearance. This pattern suggests deterioration in the stability of the bearing.  
Figure 14: Impact of throttle hole diameter on the maximum air velocity with respect to gas film clearance.  
Figure 15: Influence of the gas film thickness on the maximum air velocity with respect to gas film clearance.
# Radiation from a Cavity-Backed Circular Aperture Array Antenna Enclosed by an FSS Radome
## 1 Introduction
The obtained tangential electric fields are transformed into the equivalent magnetic current sources which in turn become rays to be used in the subsequent step. While using the rays, the ray tracing technique is employed to analyze the multi-layer FSS radome [10], and reflection and transmission properties of the FSS layer come from FEM simulations on HFSS. After obtained from the mode matching method and the ray tracing technique, electromagnetic fields on the outermost radome are transformed into the equivalent electric and magnetic surface currents using [PERSON]'s principle, and radiation fields in the far-field region are computed from the equivalent surface currents. In brief, we take an advantage of the hybrid analysis method combining the mode matching method, the ray tracing technique, and HFSS, a commercial EM simulator, based on FEM for an analysis of the aperture array antenna enclosed by the FSS radome. To verify our formulation, the FSS radome enclosing the cavity-backed circular aperture array antenna is fabricated and our computation results are compared with the measured data.
## 6 Cellular Basis of Laser-driven Radiation Therapy
### Important Factors for Determining Cellular Radiation Effect  
Two main quantities that determine the biological effects induced by radiation are dose and linear energy transfer (LET). The dose to a cell is the average amount of energy deposited per unit mass within the cell. Extensive work has been carried out to determine the cellular dose-response relationship for lethality, chromosome aberration, mutation induction, _etc._[90]. Typical survival-dose response curves are shown in Figure 18A. LET is the average amount of energy deposited along a unit length of the track forged by a radiation particle such as a proton. The relative biological effectiveness (RBE) is the comparison of the biological effectiveness of one type of ionizing radiation relative to another. As such, it is the ratio of doses (of two different types of radiation) required to achieve a given biological effect. RBE is usually plotted against LET (Figure 18B) with gamma-rays or X-rays being the standard radiation type against which other radiation types are compared. RBE is observed to slowly vary with LET over an extended range. Nonetheless, it is typically at maximum levels in the high LET range, 100-200 keV/\(\upmu\)m which is attributed to an increased \"clustering\" of lesions [91,92]. LET is the most frequently used quantity to describe the quality of a radiation type [90].  
There are significant temporal, as well as spatial, differences between high-LET and low-LET radiation. A substantial amount of energy is deposited in a cell by an individual high-LET particle track within several tens of femtoseconds. In contrast, individual low-LET radiation tracks deposit much less energy and it is distributed over the whole cell. In the low-LET case, energy is typically delivered over much longer timescales (usually seconds or minutes using a conventional radiation source) during which chemical, biochemical and biological processes occur. In general, spatially proximal ionization and excitation events and subsequent radical formation induced by a single radiation track are generally considered to be responsible for the higher RBE of high-LET radiation.  
Figure 18: Typical dose response curves of survival of cultured mammalian cells exposed to \(\upgamma\)-rays and ion particles with LET of \(\sim\)100 keV/\(\upmu\)m (**A**); Typical relative biological effectiveness—linear energy transfer (RBE-LET) relationship demonstrating that RBE peaks near 100–200 keV/\(\upmu\)m (**B**).  
There is growing evidence that closely localized DNA lesions (clustered DNA damage) are highly relevant to the ultimate biological consequences [91,92]. It is well-known that the dose-depth profile significantly differs between radiation types (Figure 1). Because of their higher LET, carbon ions have an advantage over protons for inactivating tumor cells. Furthermore, carbon ions are also superior in terms of dose localization.
## 3 Results and Discussion
### Digital Tests and Chip Configuration  
The testing procedure starts from checking the I\({}^{2}\)C communication and internal registers, which are necessary to perform all other steps. Write and read operation is performed for all registers several times with arbitrary patterns (e.g., 0x55 and 0 xAA to inspect all the bits in the register). Prior to testing more advanced functions, the communication between VLDB and chip SerDes has to be established. First, all three PLLs are turned on and their control voltages are checked (via on-line monitoring) to confirm the correct lock. Next, PLLs are connected to SerDes and the links from chip to VLDB are configured. Their configuration is based on known patterns generated by the chip (uploaded via I\({}^{2}\)C). When communication from SALT to VLDB is stable, the ASIC is switched to a loop-back mode, where the data received by the deserialiser are directly sent back, and the deserialiser is configured. Finally, the chip is ready to receive TFC commands and send data packets. Hereafter, the data processing can be studied.  
The digital processing is verified in a special test mode, where each DSP channel input sees the same constant value defined via the configuration register. Utilising NZS data packages to collect data directly from the output of the pedestal block is enough to check pedestal subtraction (as the channels are independent). The test confirms the proper operation of this block.  
In the next step (also in the test mode), pedestals controlled individually for each channel are added to the inputs to create arbitrary patterns for the next MCM block. The NZS package then collects data from the output of the MCM block. Since the NZS package includes both the average MCM value and the number of channels, the correctness of MCM calculation is thoroughly verified. The tests performed for all possible average values and many different input sets confirmed the proper operation of this block.  
The verification of the ZS block, the last DSP processing step is performed similarly to the previous test. Static inputs are generated based on the uploaded pedestal. The full range of Normal packet sizes was observed during the test, starting from packets without hits, up to the maximum number of 63 hits. For a larger number of hits, the chip creates BusyEvent packets as expected (see Table 2) confirming the correct operation of this part.  
All digital processing tests described above were conducted for several thousand data packets. Therefore, to operate continuously while avoiding memory overflow, a specific stream of TFC commands needs to be utilised. As a result, the Normal or NZS data packets (later containing a large amount of data) are interleaved with a number of HeaderOnly packets. Decreasing the number of HeaderOnly packets, memory overflow can be checked. In this case, tests verified the data consistency and packet builder packet conversion from Normal or NZS to BufferFull or BufferFullN, respectively.
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
###### Abstract  
This paper considers a general approach to fault diagnosis using a generalized Hamiltonian system representation. It can be considered that, in general, nonlinear systems still represent a problem in fault diagnosis because there are results only for a specific class of them. Therefore, fault diagnosis remains a challenging research area despite the maturity of some of the available results. In this work, a type of nonlinear system that admits a generalized Hamiltonian representation is considered; in practice, there are many systems that have this kind of representation. Thereupon, an approach for fault detection and isolation based on the Hamiltonian representation is proposed. First, following the classic approach, the original system is decoupled in different subsystems so that each subsystem is sensitive to one particular fault. Then, taking advantage of the structure, a simple way to design the residuals is presented. Finally, the proposed scheme is validated at the two-degree of freedom (DOF) helicopter of Quanser(r), where the presence of faults in sensors and actuators were considered. The results show the efficacy of the proposed scheme.  
fault detection; Hamiltonian systems; non-linear system; observers; robotic systems Article
# Modelling of Evenness of Runways as an Element of Sustainable Airport Maintenance
## 2 Methods for Measuring the Evenness of Airport Runways
### Methods for Modelling the Evenness of Airport Runways
#### 2.1.2 Inverse Distance Weighted Interpolation (IDW)  
Inverse distance weighting (IDW) is a kind of deterministic method for multivariate interpolation with a known scattered set of points. The values assigned to the unknown (interpolated) points are calculated using a weighted average of the values present at the known points. This method is accepted as the basic method in most systems that create and manage digital elevation models.  
The main feature of this method is that all points on the earth's surface are considered to be interdependent, based on distance. Therefore, the calculation of heights in an area depends on the heights of data points in the surrounding area. The elevation of an interpolated point is related to the elevations of the surrounding reference points. This relationship is a function that is inversely proportional to the distance to each reference point and is raised to a power, which is usually quadratic or cubic:  
\[\mathrm{F(r)}=\sum_{i=1}^{m}w_{i}\cdot z(r_{i})=\frac{\sum_{i=1}^{m}\frac{z(r_ {i})}{|r-r_{i}|^{p}}}{\sum_{i=1}^{m}\frac{1}{|r-r_{i}|^{p}}} \tag{5}\]  
where is:  
\(w_{i}\cdot z(r_{i})\)... the value at an unsampled location  
\(z(r_{i})\)... known values  
\(p\)... weight  
\(r...\)distances between the point and the simple  
\(z(r_{i})...\) represent the interpolation function  
\(r\)-\(r_{i,j}\)... separation distance between known and unknown point  
In general, it is a relatively fast method [57]. Weighting is assigned to sample points by a weighting coefficient, which controls how the influence of the weighting decreases with increasing distance from the new point. The larger the weighting coefficient, the lesser the influence of points that are far away from the unknown point during the interpolation process. As the coefficient increases, the value of the unknown point approaches the value of the nearest observation point.  
It is important to note that the IDW interpolation method also has some disadvantages: The quality of the interpolation result may decrease if the distribution of the sample data points is non-uniform. In addition, maximum and minimum values in the interpolated area may only occur at sample data points. This often results in small peaks and valleys around the sample data points. The interpolation method is part of many GIS applications. IDW models with p = 2 (IDW1), p = 1 (IDW2) and p = 10 (IDW3) were evaluated as reasonable.
## 4 Discussion
### Sensitivity Analysis  
To analyze parameter sensitivity, we added a 10% perturbation to each parameter of an ideal model (\(M=150\) mGal, \(z=6\) m, \(\theta=45\), \(w=12\) m, and \(x_{0}=2\) m). Then, we calculated the fitness value of this perturbed model and used the central difference method to compute its sensitivity. This is a method that assumes a locally smooth fitness surface and a symmetric response to perturbations. The expression is as follows:  
\[\frac{\partial F}{\partial p_{i}}\approx\frac{F(p_{i}+\Delta p_{i})-F(p_{i}- \Delta p_{i})}{2\Delta p_{i}} \tag{13}\]  
where \(p_{i}(i\in\{M,z,\theta,w,x_{0}\})\) represents fault parameters, \(F()\) is the objective function, and \(\Delta p_{i}=0.1\times p_{i}\) denotes the perturbation step size. Since the dimensions and magnitudes of each parameter vary, normalization is also required (using Equation (14)). After calculations, the sensitivities of the five fault parameters are obtained, as shown in Figure 6.  
\[S_{i}=\frac{\left|\frac{\partial F}{\partial p_{i}}\right|}{max\left|\frac{ \partial F}{\partial p_{i}}\right|},j\in\{M,z,\theta,w,x_{0}\} \tag{14}\]  
From Figure 6, we see that top-layer center depth z and bottom-layer center depth w of the fault have relatively high sensitivity, while fault dip angle \(\theta\) has the lowest sensitivity. This means that \(z\) and \(w\) have a significant impact on the objective function, and adjusting them is the key to optimizing the algorithm. This also indirectly indicates that it is reasonable for us to choose \(z\) and \(w\) as penalty terms to constrain the algorithm. However, in gravity inversion, there is a high chance of ambiguous solutions [4]. Even though \(z\) and \(w\) have high sensitivity, they are inherently highly correlated. One may change abruptly while the other changes at the same time. This is the parameter coupling issue mentioned in Section 3.1.
# Study on Path Planning Method for Imitating the Lane-Changing Operation of Excellent Drivers
## 1 Introduction
In previous studies, by purposely propagating output-layer errors into hidden-layers, and deriving the optimal weights with gradient descent optimization [24], Back Propagation (BP) neural network is widely used to minimize errors. However, the further development and application of BP neural network is limited by the drawback that it easily falls into local optimal solutions. Many researchers have attempted to use different types of evolutionary algorithms, such as Genetic Algorithm [25], Particle Swarm Optimization (PSO) [26], and Simulated Annealing (SA) [27], to optimize the weight and threshold of the BP neural network in training process. [PERSON] and [PERSON] [28] presented a short-term load forecasting model of natural gas based on Genetic Algorithm and Back Propagation (GA-BP) neural network. [PERSON] et al. [29] proposed a wind speed forecasting model based on GA-BP neural network. They found that the accuracy and the learning speed of the BP neural network can be improved significantly with optimization through the genetic algorithm. To solve the problem that the traditional path planning methods for lane-changing do not consider the actual driving characteristic of lane-changing, five excellent drivers were invited to do lane-changing tests. The trajectories under different modes were recorded. The paths were fitted by polynomial curve by comparing different curves. The routes of lane-changing considering the feature of the intermediate state and the final position were obtained. The excellent driver lane-changing model was established based on GA-BP neural networks trained by the optimal trajectory database obtained by the experiment. The path planning method for lane-changing based on the excellent driver lane-changing model was proposed. It can meet the requirements of different types of passengers for riding comfort and reduce the probability of motion sickness. The structure of the paper is as follows. Section 2 presents the lane-changing test, and gives an approach to transform the data of Global Position System (GPS) into geographic coordinate system. The fitting of driver's lane-changing path based on six-order polynomial is shown in Section 3. In Section 4, the excellent driver model based on GA-BP neural networks is highlighted. Simulation results are discussed in Section 5. Finally, Section 6 presents some concluding remarks.