esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seismic Resilience Evaluation Framework
### Generation of Fragility Functions  
As illustrated in Equation (1), most fragility functions could be expressed using a cumulative lognormal probability distribution function [15; 16; 17]:  
\[P(ds\geq ds_{j}\left|IM\right)=\Phi\left[\frac{1}{\beta_{tot}}\cdot\ln\left( \frac{IM}{IM_{mj}}\right)\right], \tag{1}\]  
where \(P(ds\geq ds_{j}\left|IM\right)\) represents the exceedance probability of a specific damage state, i.e., \(DS\) or \(ds\), under an earthquake intensity level, the latter being described through the earthquake intensity measure \(IM\), i.e., \(PGA\) or \(PGV\) in this work; \(\Phi(\bullet)\) represents the cumulative distribution function; \(IM_{mj}\) stands for the mean value of \(IM\) needed to induce the \(j_{th}\)\(ds\); and \(\beta_{tot}\) stands for the total standard deviation, which can be calculated through the combination of three main sources of uncertainty, as illustrated in Equation (2):  
\[\beta_{tot}=\sqrt{\beta_{ds}^{2}+\beta_{C}^{2}+\beta_{D}^{2}}, \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Earthquake** & **Station Name** & **Year** & **Mag. (\(M_{w}\))** & \(R_{k}\) (km)** & \(PGA\) (g)** \\ \hline
1 & Northridge-01 & LA-Hollywood Stor FF & 1994 & 6.69 & 19.73 & 0.23 \\
2 & Parkfield & Cholame-Shandon Array & 1966 & 6.19 & 12.9 & 0.24 \\
3 & Loma Prieta & Treasure Island & 1989 & 6.93 & 77.32 & 0.16 \\
4 & Kern County & Taft Lincoln School & 1952 & 7.36 & 38.42 & 0.15 \\
5 & San Fernando & Castaic-Old Ridge Route & 1971 & 6.61 & 19.33 & 0.34 \\
6 & Imperial Valley-02 & El Centro Array \#9 & 1940 & 6.95 & 6.09 & 0.28 \\
7 & Superstition Hills-01 & Imperial Valley W.L. Array & 1987 & 6.22 & 17.59 & 0.13 \\
8 & Parkfield-02\_CA & Parkfield-Cholame 2 WA & 2004 & 6.00 & 1.63 & 0.62 \\
9 & Imperial Valley-07 & El Centro Array \#11 & 1979 & 5.01 & 13.61 & 0.19 \\
10 & Tottor\_Japan & TTR008 & 2000 & 6.61 & 6.86 & 0.39 \\
11 & Kobe\_Japan & Port Island & 1995 & 6.9 & 3.31 & 0.32 \\
12 & Borrego Mtn & El Centro Array \#9 & 1968 & 6.63 & 45.12 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected ground motions [23].  
Figure 5: Soil–tunnel interaction model.  
where \(\beta_{ds}\) is the uncertainty involved in defining various damage states; \(\beta_{C}\) is used to quantify the uncertainty associated with the response of the structure (seismic capacity); and \(\beta_{D}\) is utilized to describe the uncertainty originating from the ground motions (seismic demand). The parameters of \(\beta_{ds}\) and \(\beta_{C}\) are assumed to be 0.4 and 0.3, respectively [26].  
A damage index (_DI_) is utilized to appropriately characterize various damage states and is defined using the ratio of the actual (_M_) over the capacity (\(M_{Rd}\)) bending moment of the tunnel lining cross-section [26]. On this basis, the capacity (\(M_{Rd}\)) bending moment of the tunnel lining is assessed by a section analysis [26] according to the material and geometric characteristics of the lining, taking into account the actual axial forces (_N_) and bending moments (_M_). Five damage states are introduced in Table 2.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 3 Research Method
### Geometric Accuracy Evaluation Index
#### 3.4.2 Structural Column Accuracy Evaluation  
The accuracy of the generated structural column planes is quantitatively assessed using two metrics: the structural column discrepancy metric (\(SCDM\)) and the angular distance deviation (\(ADD\)). Similar to other studies [52; 57], the \(SCDM\) is used to calculate the differences in the relative positions of the structural columns and the Euclidean distances of all point pairs for the structural columns, where \(P_{m}\) is the true coordinates of the structural column corner points and \(P_{a}\) is the measured coordinates of the same corner point after registration. The \(ADD\) is used to reflect the deviation of the angle between the corner points of the structure columns, defined as the angular distance between the corner points of the wall and the referenced column in BIM. As shown in Figure 7, the result of coarse registration is represented by the superposition of two boxes; the blue box and red box represent BIM and point cloud, respectively.  
\[SCDM=dis(P_{m}-P_{a}) \tag{5}\]  
\[ADD=\arccos(\overrightarrow{a},\overrightarrow{b})=\arccos\frac{ \overrightarrow{a}\cdot\overrightarrow{b}}{\left|\overrightarrow{a}\right| \cdot\left|\overrightarrow{b}\right|} \tag{6}\]
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 5 Performance Tests--Results and Analysis
### Select Operation  
We further analyzed several select operations in order to better observe the result of the proposed optimization methods. The selection operations performed are a simple select operation, based on the primary key of the table; a select operation that uses a join based on the name field in the business table; a select operation that uses two joins also based on the business name; a select operation with multiple joins and different functions based on the _business_id_ field in the user table; and a select operation with two left joins and different functions. All selections exclude deleted items from all tables.
## 2 Structures of Basic Spoke Type IPM Model and Proposed Flared Type IPM Model
### Basic Spoke Type IPM Model  
Figure 2 shows the shape of the basic spoke type IPM model in which a permanent magnet is magnetized in a concentrated flux structure (white mark). As shown in Figure 2, the basic spoke type IPM model is applied with 8 poles and concentrated winding. The multi-polar structure of the IPM model and the concentrated winding have a high output power density per unit volume and high efficiency, so this model has been widely used in fields such as home appliances and electric vehicles. Additionally, the spoke type motor is better at concentrating magnetic flux than other IPM motors. Therefore, the magnetic flux can be utilized more efficiently and the output power density can be increased. In order to prevent the problem of scattering during high-speed rotation, the rotor structure is designed with an interior permanent magnet and composed of plastic resin, which is effective even in high-speed rotation. Moreover, the basic spoke type IPM model uses ferrite magnets of grade 9 BE, which cost less than NdFeB magnets. Therefore, this model has a price advantage, and can be applied to electric vehicles and household appliances. Table 1 shows the specification of the basic spoke type IPM model.  
Figure 1: Electric water pump system: (**a**) configuration and major components; (**b**) basic motor unit.  
Figure 2: Structure of basic spoke type interior permanent magnet (IPM) model: (**a**) full model; (**b**) 1/4 model.
# Energy Efficient Secure Communication Model against Cooperative Eavesdropper
## 1 Introduction
In [17], the eavesdroppers cooperate with each other to detect the information transmission between the transmitter and relays and do not consider detecting jamming signal to remove interference. In [18], the research work is focused on a scenario where multiple eavesdroppers decode information from the base station and the legitimate network with the help of multiple friendly jammers to degrade the SINR of the cooperative eavesdroppers. However, in our work, we consider that the MIMO eavesdropper is closer to the jammer and detects the jamming signal and cooperates with the eavesdropper nearer to the transmitter to estimate information. Thus, under these special circumstances, the research work is focused on hardware configuration and optimum power allocation for the jammer node. We consider that the eavesdropper employs MIMO IBDFE. IBDFE is an efficient low complex receiver, as compared to the non-iterative decoder [19; 20] and it can be effectively used with single carrier frequency-division multiple access (SC-FDMA) transmission techniques [21; 22]. In this article, we present an unique scenario in which one eavesdropper detects the jamming signal and another eavesdropper estimates the information signal, and then cooperate with each other to improve both jamming signal and information signal estimate. The SINR of improved jamming signal estimate and information signal estimate at the eavesdroppers is derived. The performance of the MIMO IBDFE receiver with the change in antenna configuration and the impact of change in the SNR of the jamming signal is analysed with the simulated results. The ratio between jamming signal power and information signal power for the given operating SNR is optimized with the support of simulated results. Furthermore, we make the system model more energy efficient by optimizing the power of the jamming signal. The structure of the paper is as follows. The system model is explained in Section 2, the MIMO IBDFE equation is derived in Section 2.2 and the SINR equation for the cooperative eavesdropper is given in Section 2.3. The performances of the eavesdroppers are analyzed and the optimum jamming signal power for a given operating SNR is studied in Section 3. The conclusion is presented in Section 4. Throughout this article, matrices or vectors are denoted by bold letters and scalar variables are denoted by italic letters. The variables associated to frequency domain and time domain are denoted by capital letters and small letters, respectively. \((.)^{*},(.)^{T},(. )^{H},||.||\) and \(\mathbb{E}[. ]\) denote the conjugate, transpose, Hermitian, trace and expectation operations, respectively. \(\bar{X}\), \(\bar{X}\) and \(\bar{X}\) denote sample, hard decision and soft decision, respectively, and the appropriate Identity matrix of \(X\) is denoted as \(I_{N}\).
# Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows
###### Abstract  
This paper presents a real-time path planning approach for controlling the motion of space-based robots. The algorithm can plan three-dimensional trajectories for agents in a complex environment which includes numerous static and dynamic obstacles, path constraints, and/or performance constraints. This approach is extended based on the dynamic window approach (DWA). As the classic reactive method for obstacle avoidance, DWA uses an optimized function to select the best motion command. The original DWA optimization function consists of three weight terms. Changing the weights of these terms will change the behavior of the algorithm. In this paper, to improve the evaluation ability of the optimization function and the robot's ability to adapt to the environment, a new optimization function is designed and combined with fuzzy logic to adjust the weights of each parameter of the optimization function. Given that DWA has the defect of local minima, which makes the robot hard to escape U-shaped obstacles, a dual dynamic window method and local goals are adopted in this article to help the robot escape local minima. By comparison, the proposed method is superior to traditional DWA and fuzzy DWA (F_DWA) in terms of computational efficiency, smoothness and security.  
obstacle avoidance; space-based robots; dynamic window approach; fuzzy logic; path planning  
doi:10.3390/app11020504
# On Cross-Layer Interactions for Congestion Control in the Internet
## 4 AQM Algorithms  
Packet buffers are essential elements of a network router. They compensate for the traffic bursts, which are a natural consequence of the best-effort nature of the network. However, the unwelcome consequence of buffering is an additional packet delay, which affects the performance of interactive and transaction-based applications. High and variable latency may also impair the congestion control mechanism of TCP. The main problems with buffers are buffer overflows and so-called bufferbloat, i.e., \"persistently full buffer\" [25]. Buffer overflows are caused by bottlenecks that result from a decrease in the transmission rate or at the convergence point of many links.  
Throughout the last 20 years, despite the awareness and dramatic increase in the buffer memory size and memory access speed up, the problem still affects network performance. Actually, the increase of memory size and availability is the main cause of bufferbloat, and the solution for this is AQM. With the increased usage of the Internet as well as the change in traffic characteristics due to the growth of video streaming and time-critical traffic (video conferences, VoIP, and gaming), the urge for proper buffer management is even more critical.  
The main limitation of most AQM algorithms is that they are vulnerable to parameters' tuning and require adapting of parameters when the network condition changes. This is probably the most important reason why AQMs (as RED, for example), although thoroughly researched, are not commonly deployed. Drop Tail is still the most popularbuffer management strategy applied at routers, resulting in packet drops only when the buffer is full.  
The purpose of any Active Queue Management algorithm is to control the queue size to reduce its variability and queuing delay and preserve the possibility of absorbing packet bursts at the same time. An extensive survey of Active Queue Management schemes, developed and evaluated in the last decades, has been published by [PERSON] [26]. Two state-of-the-art AQM algorithms that address the problem of bufferboat along and provide a parameterless configuration are presented below.
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 4 Simulation and Experimental Results
### Dynamical Analysis  
#### 4.2.1 Modal Analysis  
The first step in performing a dynamic analysis is determining the structure's natural frequencies and mode shapes, taking zero damping into account. The results of this analysis define the structure's dynamic behavior and can be used to predict how the structure will respond to dynamic loads [33]. Before the vibration test, the fundamental frequency of the structure was obtained through a 0.5 g sinusoidal characteristic sweep frequency test within the frequency range of 10-2000 Hz. The first four order natural frequencies and vibration modes of the novel GRM are shown in Figure 9. The sinusoidal sweep frequency test curves in X, Y and Z directions before the vibration test of each measuring point are shown in Section 4.2.2. The first order main frequencies in X, Y and Z directions obtained by the simulation analysis and test results are shown in Table 6.  
The first order main vibration mode frequency of modal analysis is 420.25 Hz, which is greater than the 100 Hz specified by the spacecraft [34], indicating that the stiffness of the main structure of the designed model meets the design requirements. From the test sinusoidal characteristic sweep frequency curve in Figure 10, it can be seen that the first order frequency of the novel GRM is 771.19 Hz in X direction, 436.25 Hz in Y direction, and 586.07 Hz in Z direction. Combined with the above modal analysis results, the relative errors are obtained, as shown in Table 6. As can also be seen from Table 6, the results of  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Direction** & **Modal Analysis (Hz)** & **Modal Test (Hz)** & **Relative Error** \\ \hline X & 732.34 & 771.19 & 5.04\% \\ Y & 420.25 & 436.25 & 3.67\% \\ Z & 615.66 & 586.07 & \(-4.80\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Modal data.  
Figure 9: First 4 orders of vibration and frequency: (**a**) First order Y-directional vibration frequency is 420.25 Hz. (**b**) Second order Z-directional vibration frequency is 615.66 Hz. (**c**) Third order local vibration frequency is 618.68 Hz. (**d**) Fourth order X-directional vibration frequency is 732.34 Hz.  
the three directions obtained by the modal analysis and the test results are all within the allowable error range of 10% in the engineering analysis, indicating that the established finite element model is effective and has a sufficient safety margin.  
Figure 10: _Cont._
## 2 Synthesis of Yttrium
### Green Synthesis  
Despite the synthesis of Y\({}_{2}\)O\({}_{3}\) NPs using several sources and methods, the Green method is suitable and is targeted by many researchers. Several studies have reported the plant-based nanoparticles using various parts of the plant (leaf, fruits, and pods), and as-prepared nanoparticles were tested and confirmed by characterization measures, such as UV-vis, XRD, Fourier-transform infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Transmission electron microscopy (TEM). Y\({}_{2}\)O\({}_{3}\) nanoparticles were green synthesized using _Acalypha indica_ leaf extract, with a particle size ranging (23 to 66) nm. The presence of Y-O-Y confirmed the formation of the synthesized nanoparticles, and O-Y-O stretching vibration using FTIR spectroscopy, changes in thermal behavior via thermogravimetric, and differential scanning calorimetry analyses [63]. [PERSON] et al. [83] also demonstrated green synthesized Y\({}_{2}\)O\({}_{3}\) NPs using _Liriope platy phyla_ rhizome extract. It was reported that the leaf extract acts as the reducing agent when added to the solution of yttrium nitrate hexahydrate without any additives for the synthesis of nanoparticles, and the prepared nanoparticles were used as a heterogeneous green catalyst for the construction of biologically interesting 1,3-thiazol-4-ones in good-to-excellent yields  
[PERSON] and [PERSON] [17] synthesized Y\({}_{2}\)O\({}_{3}\) NPs using an aqueous extract of _Acalypha indica_ as a capping agent. Ultraviolet-visible (UV-Vis) spectrogram was measured for all calcined NPs, and Y\({}_{2}\)O\({}_{3}\) was found at 284.0 nm. XRD for the yttrium complex showed an amorphous structure with a broad peak at the calcined Y\({}_{2}\)O\({}_{3}\) NPs, and high-intensity peaks that belong to the cubic structure with Ia\({}_{3}\) group. The lattice parameters (ao) are (10.37 + 0.01) A and at 90.00\({}^{\circ}\). The FT-IR spectrum of Y\({}_{2}\)O\({}_{3}\) showed a sharp band that appeared at 565 cm\({}^{-1}\) that is assigned to (Y-O) stretching vibration and Y\({}_{2}\)O\({}_{3}\) formation, while the intense band at 588 cm\({}^{-1}\) indicates the presence of Y-O-Y asymmetric stretching mode of vibration corresponding to the surface-bridging oxide. The bands at (873, 1216, 1085, and 1026) cm\({}^{-1}\) represent the Y-OH and Y-O-Y groups present in the nanostructure. Another study has also emphasized that a green step approach was employed using _Forsythiae fructus_ aqueous fruit extract. Y\({}_{2}\)O\({}_{3}\) NPs were successfully developed recently [37], and the obtained nanoparticles were reported to exhibit anticancer activity against harmful pathogens. The nanoparticles' size was 11 nm. The green synthetic method showed environmentally friendly, cheap, and reduced toxicity of chemical and less harmful side effects. This method can be widely advocated as an alternative to expensive physical and chemical Y\({}_{2}\)O\({}_{3}\) NPs preparation and used as a promising candidate for clinical applications.
# A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory
## 2 The Nonlinear PMSM Electromagnetic Model Based on Differential Inductance  
To address the limitations inherent in conventional linear models--particularly the assumptions of constant inductance, negligible magnetic saturation, and sinusoidal flux linkage distributions--this study develops a nonlinear modeling framework grounded in differential inductance theory. In saturated electromagnetic systems, the differential inductance physically represents the local sensitivity of the flux linkage-current relationship under nonlinear operating conditions. For polyphase systems, this concept generalizes to the Jacobian matrix of the flux linkage vector with respect to the current vector, expressed as:  
\[\mathbf{L}^{\text{DIF}}=\mathbf{J}_{\mathbf{i}}^{\text{b}}=\frac{\partial \mathbf{\psi}}{\partial\mathbf{i}} \tag{1}\]  
Since the differential relations required for the torque model are provided only by differential inductance, the inductances appearing below are differential without explanation.
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 5 Numerical Simulation and Analysis
### Design Parameters Analysis  
There are many design parameters involved in the design process of the opportunity, such as the transfer duration, altitude of the perilune, and so on, while the location of the landing field also affects the design results. Different design parameters have distinct degrees of influence on the distribution of opportunities, the cost of transfer, etc. The analysis of the influence of several design parameters on the distribution law of opportunity and the performance of precise Moon-to-Earth transfer trajectory is as follows.
# Radiation Hardness of Oxide Thin Films Prepared by Magnetron Sputtering Deposition
## 3 Experiment Results and Discussion  
### Elastic Recoil Detection Analysis of Magnetron-Sputtering-Deposited Oxides  
The ToF-ERDA measurements provided information about all elements present in the investigated samples simultaneously. In the maps shown in Figure 1, elements can be identified and distinguished by measuring the energy and time of flight of recoils coming out of the sample. Sometimes, as shown for example in Figure 1c, scattered iodine ions from the probing ion beam can also be detected. Therefore, by using this technique, it is possible to determine the elemental composition of thin films with very good mass and depth resolution. The results of the ToF-ERDA measurements we have obtained in this work are presented in Table 3.  
Figure 1: ToF-ERDA maps obtained from the analysis of (**a**) a-SiO\({}_{2}\), (**b**) a-Al\({}_{2}\)O\({}_{3}\), and (**c**) a-MgO.  
The results presented in Table 3 indicate that the produced oxide films are most likely stoichiometric, as expected from used oxide targets. Assuming that hydrogen is bonded within water molecules, it appears that the measured amount of oxygen atoms is only slightly higher than expected, although expected values are still within the error bars. These values are constant throughout the whole film thickness. Very small amounts of elements such as carbon, argon, iron, and chromium (identification of the last two is uncertain) that were found are common for the magnetron sputtering deposition process. Such small amount of impurities, well known residues of the magnetron sputtering process, are not expected to influence the radiation hardness of studied oxide materials [31].
# On Beamforming with the Single-Sideband Transform
## 2 Frequency and Time-Frequency Transforms
### Convolution  
Given an LTI system with an impulse response \(h(t)\), the convolution theorem for the Fourier transform states that  
\[h(t)*x(t)\overset{\mathcal{F}}{\rightleftharpoons}H_{\mathcal{F}}(f)X_{ \mathcal{F}}(f) \tag{6}\]  
where \(\overset{\mathcal{F}}{\rightleftharpoons}\) indicates a Fourier transform pair. This theorem is not strictly valid for the RFT (see Property A3 in Appendix A). However, it is possible to prove that there is an equivalent of the convolution theorem for the RFT (see Property A4 in Appendix A), with  
\[h(t)*x(t)\overset{\mathcal{R}}{\rightleftharpoons}X_{\mathcal{R}}(f)H_{ \mathcal{F}}^{\text{R}}(f)+X_{\mathcal{R}}(-f)H_{\mathcal{F}}^{\text{I}}(f) \tag{7}\]  
where \(\overset{\mathcal{R}}{\rightleftharpoons}\) indicates an RFT pair. For a given frequency \(f\), the convolution's output on the RFT domain depends on both it and its dual frequency \(\
egmedspace f\). This makes intuitive sense, as the real-valued spectrum of the RFT impedes a correct phase representation using only the given frequency, making its conjugate frequency also necessary.