esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 1 Introduction
This study collects 4010 strong motion records from 134 seismic events in the Loess Plateau region, which are analyzed and screened, ultimately constructing the dataset required for the prediction equations. Each strong motion record is processed with baseline correction and high-pass filtering and the peak parameters and epicentral distances are extracted. Subsequently, two attenuation models are developed for nonlinear data fitting, assisting the development of prediction equations for horizontal ground motion parameters at the soil layers, including peak ground acceleration (PGA), effective peak acceleration (EPA), and peak ground velocity (PGV). These equations can estimate the ground motion parameters with magnitudes ranging from \(M_{\text{S}}\) 3.0 to \(M_{\text{S}}\) 6.5 and epicentral distances between 0 and 100 km. PGA refers to the maximum absolute amplitude of the ground motion acceleration time history, measured in cm/s\({}^{2}\) or gal. It characterizes the intensity of the inertial effects of ground motion and reflects its high-frequency characteristics. EPA is the peak acceleration with an averaged significance based on the response spectrum. EPA is formulated as follows:  
\[\text{EPA}=S_{a}/2.5 \tag{1}\]  
where \(S_{a}\) represents the smoothed average value of the acceleration response spectrum with a damping ratio of 5% in the frequency range of 2 to 10 Hz. \(Sa\) is then divided by 2.5 to obtain the EPA [13]. This study introduces the EPA parameter and analyzes its attenuation relationship to investigate further the impact of ground motion attenuation on structural damage in the Loess Plateau region. PGV refers to the maximum absolute value of the ground motion velocity time history, measured in cm/s. It is related to the kinetic energy of particle vibrations and is a significant physical quantity for measuring ground motion energy. The velocity peak reflects the intensity of the medium-frequency components of the ground motion. Studies, such as blasting experiments, have revealed that PGV correlates well with the degree of structural damage. Given the importance, intuitiveness, and clear physical significance of the peak parameters, PGA, EPA, and PGV, this study adopts a magnitude grouping approach. Specifically, it uses two-step fitting methods to establish the prediction equations for PGA, EPA, and PGV in the Loess Plateau region. Based on these equations, the corresponding attenuation curves and fitting residual distribution plots are generated, and the fitting performance of the two attenuation models is analyzed. Finally, based on the analysis results, a suggested selection scheme for the attenuation relationships of peak parameters in the Loess Plateau region is proposed.
## 2 The FNA Method
### The Prediction of Machining Trajectory  
The position sequence \(\mathbf{P}_{i}\) shown in Equation (1) has been achieved according to the previous section. However, due to the trajectory characteristics of the small line segments and the position error [30], \(\mathbf{P}_{i}\) will inevitably contain a random position error \(\Delta p\), which, in turn, causes fluctuations in the velocity and acceleration as  
\[\left\{\begin{array}{l}\Delta f=\frac{\Delta p}{T_{\mathrm{s}}}\\ \Delta a=\frac{\Delta f}{T_{\mathrm{s}}}=\frac{\Delta p}{T_{\mathrm{s}}^{2}} \end{array}\right. \tag{2}\]  
where \(T_{\mathrm{s}}\) is the sampling period. If \(\Delta p\) and \(T_{\mathrm{s}}\) are taken to be 1 \(\upmu\)m and 1 ms, respectively, the acceleration fluctuation \(\Delta a\) will be as high as 1000 mm/s\({}^{2}\).  
On the other hand, in the actual CNC machining process, \(\mathbf{P}_{i}\) will be transmitted to the servo system to control the motion of the machine tool. Since the output of the servo system will lag behind the input [31], there will be a difference between the actual machining trajectory and the command trajectory, which, in turn, will lead to a change in the corresponding speed and acceleration.  
For the above two reasons, this section will use the analysis method of the discrete system to predict the \"actual machining trajectory\" \(\mathbf{Q}_{i}\) corresponding to \(\mathbf{P}_{i}\), and the solving of the nominal acceleration in a later section will be based on \(\mathbf{Q}_{i}\) to make the final feedrate-constraint value be more reasonable.  
Figure 1: Flowchart of the FNA method.  
The function and control structure of the servo system of a typical three-axis CNC machine tool is shown in Figure 2, in which \(H_{x}(s),H_{y}(s)\), and \(H_{z}(s)\) are the transfer functions of each axis. In order to solve the position sequence \(\mathbf{Q}_{i}\) of the servo system output, the difference equation corresponding to the transfer function of each axis should be obtained first, and then the command position sequence \(\mathbf{P}_{i}\) can be substituted into it to calculate \(\mathbf{Q}_{i}\). Since the servo system of each axis usually has the same control structure, take the X-axis as an example and let its input and output position sequences be \(p_{x}(k)\) and \(q_{x}(k)\), respectively. Then, when \(H_{x}(s)\) is approximated as a second-order system [32,33], its corresponding difference equation is  
\[q_{x}(k)=a_{0}\cdot p_{x}(k)+a_{1}\cdot p_{x}(k-1)+a_{2}\cdot p_{x}(k-2)-b_{0} \cdot q_{x}(k-1)-b_{1}\cdot q_{x}(k-2),k=0,1,2,\cdots \tag{3}\]  
The solution of the coefficients and initial conditions of this difference equation will be detailed below.
# 3D-Printed Concrete Bridges: Material, Design, Construction, and Reinforcement
## 4 Construction Techniques for 3D-Printed Concrete Bridges  
This section reviews the printing systems and construction techniques used in 3D-printed concrete bridges, highlighting their applications, advantages, and challenges.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Additive/Admixture** & **Purpose** & **Property Enhanced** \\ \hline
[16] & Water retention agent & Improves water retention, provides & \\  & Water retention agent & thixotropic behavior, and prevents & Thixotropy \\  & & pressurized bleeding & \\ \hline
[35] & Superplasticizer & Enhances flowability and reduced & \\  & & water demand & Workability, extrudability \\ \hline
[36] & Superplasticizer & Enhances flowability and reduces & \\  & Deforming agent & Improves workability and prevents & \\  & Retarder & legregation & Consistency, stability \\  & Viscous agent & Delays setting time to extend the & \\  & & workability window & Open time, buildability \\ \hline
[21] & Polypropylene (PP) fibers & Improves tensile strength and & \\  & & crack resistance & \\ \hline  & Limestone powder & Improves workability and & \\  & Polycarboxylate ether (PCE) & Enhances flowability and reduces & \\
[17, 37] & Hydroxypropyl methylcellulose & water demand & \\  & (HPDC) & Enhances viscosity and thixotropy & \\  & Alkali-free shotcrete accelerator & Controls setting time for rapid & \\  & (ACC) & layer stacking & \\ \hline  & Water-reducing agent & Enhances flowability and reduces & \\  & & water content & \\
[38] & Silica ash and fly ash & Improves workability and durability & \\  & Polypropylene (PP) fibers & Enhances interlayer bonding and & \\  & reduces cracking & & \\ \hline  & Silica fume & Enhances strength and durability & \\  & Metakaolin & Improves thixotropy and & \\  & & extrusion properties & \\
[25] & Superplasticizer & Improves flowability and reduces & \\  & & water demand & \\  & PVA fiber & Enhances tensile strength and & \\  & & crack resistance & \\ \hline \end{tabular}
\end{table}
Table 1: Materials used in 3D-printed concrete bridges.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 2 Related Work
### Image Processing-Based Approaches  
[PERSON] et al. [26] developed an overexposure method correction based on lightness correction as well as chrominance restoration. For the lightness correction, the authors used a two-dimensional Gaussian function to model and correct the lightness of overexposed regions. Then, they restored color using a stretching technique and propagated the corrected color from neighboring non-overexposed regions.  
[PERSON] et al. [27] generated different segmentation masks using three binarization techniques. The first mask was obtained from [PERSON]'s local thresholding algorithm which calculated a local threshold for each pixel based on the intensity values of the pixels in a defined neighborhood around it. The second mask represented the highest intensities in the image. Finally, the third mask was generated by applying the thresholding technique on each RGB channel of the original image. Finally, the approach obtained a 0.7361 in precision, a 0.9207 in Recall, and a 0.8184 in F-measure.  
[PERSON] and [PERSON] [28] proposed monocular Visual Odometry (VO) that combined intensity-based and gradient-based costs with adaptive weight. The authors evaluated the system in terms of robustness, runtime, and tracking precision using different synthetic and real datasets such as vKITTI, real-world DEVON Island, and Symphony Lake under local and global lighting changes. They also briefly evaluated the mapping effectiveness using images influenced by sunlight glare. This evaluation revealed that sun glare can be represented as local illumination variations, and its detrimental impact on motion estimation can be mitigated by employing an algorithm designed to handle such variations. However, the system has higher computational costs compared to other methods, largely due to the increased residual and Jacobian computational demands associated with our combined cost.  
[PERSON] et al. [29] generated colorimetric, photometric, and GPS features to detect both the extent and location of sun glare from a single frame. The authors combined three important components: (1) The brightness, saturation, and local contrast of the input frame, (2) the solar azimuth and elevation, determined from GPS location and heading information (applied during daylight conditions), and (3) the color variation from the expected hue of the sun, based on solar elevation data. The proposed algorithm effectively identifiedthe positions and spatial extents of glare sources in multiple images, using both subjective ratings and established quantitative metrics.
_Algorithms_**2015**, \(8\), 321-335; doi:10.3390/a8020321  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
Time Domain Simulation of Sound Waves Using Smoothed Particle Hydrodynamics Algorithm with Artificial Viscosity  
**[PERSON] 1, [PERSON] 1,2,* and [PERSON] 1**  
1 School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; E-Mails: [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (Y.O.Z.)  
2 Hubei Key Laboratory of Naval Architecture and Ocean Engineering Hydrodynamics, Huazhong University of Science and Technology, Wuhan 430074, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-139-9555-9242.  
Academic Editor: [PERSON]  
_Received: 24 March 2015 / Accepted: 9 June 2015 / Published: 17 June 2015_  
**Abstract:** Smoothed particle hydrodynamics (SPH), as a Lagrangian, meshfree method, is supposed to be useful in solving acoustic problems, such as combustion noise, bubble acoustics, _etc._, and has been gradually used in sound wave computation. However, unphysical oscillations in the sound wave simulation cannot be ignored. In this paper, an artificial viscosity term is added into the standard SPH algorithm used for solving linearized acoustic wave equations. SPH algorithms with or without artificial viscosity are both built to compute sound propagation and interference in the time domain. Then, the effects of the smoothing kernel function, particle spacing and Courant number on the SPH algorithms of sound waves are discussed. After comparing SPH simulation results with theoretical solutions, it is shown that the result of the SPH algorithm with the artificial viscosity term added attains good agreement with the theoretical solution by effectively reducing unphysical oscillations. In addition, suitable computational parameters of SPH algorithms are proposed through analyzing the sound pressure errors for simulating sound waves.  
**Keywords:** SPH; sound waves; unphysical oscillations; artificial viscosity; linearized acoustic wave equations
## 2 Research Methodology
### Measurement Noises and Computational Complexity
To address this challenge, researchers have proposed various dimensionality- and model-order-reduction methods (e.g., SEREP, ISOMAP, POD, etc.). Particularly, the computational complexity of the methods based on ISOMAP mainly depends on the number of DOFs. The computational complexity of the methods based on Krylov subspace, SEREP, Guyan reduction, modal truncation, IES, and dynamic condensation mainly depends on the number of data samples or time steps. However, while using POD, complexity depends on both parameters.
## 2 Geometry and Signal Model
### Geometry Model
Among various array models, the L-shaped array not only has the advantages of simple structure and low system design difficulty, but also it can offer a larger array aperture when the number of channels and the distance between channels is constant, which makes it have a higher DOA estimation accuracy [14; 18]. Moreover, when processing the signal of the L-shaped array, we can simplify the two-dimensional DOA estimation problem into two independent one-dimensional DOA problems to solve [14; 15], which will reduce the computational burden. Hence, considering the requirement of DOA estimation and the advantages of the L-shaped array, this paper proposes to arrange the multiple channels of the SAR system into an L-shaped to receive signals. Besides, in order to achieve DOA estimation, the channels in the array should meet:  
1. All the channels are independent and isotropic. 2. The distance between adjacent channels is not more than half the wavelength of the RFI signal.
###### Abstract  
Wind tunnel test results can be influenced by various factors such as the blockage ratio and scaling ratio. These factors may introduce errors in the experimental outcomes, impacting the accuracy and reliability of the data obtained. This study quantitatively assesses consistency and identifies uncertainty sources to enhance result uniformity across various wind tunnel laboratories. This study conducted a systematic comparison between different wind tunnels in terms of rigid model pressure measurement wind tunnel experiments on the same Commonwealth Advisory Aeronautical Research Council (CAARC) standard tall building model. The study analyzes and discusses the results of mean and root-mean-square (RMS) wind pressure coefficients, peak factors, extreme wind pressure coefficients, probability density distributions, and base overturning force coefficients. The results indicated that in the open-circuit wind tunnel laboratory, the mean wind pressure coefficient is underestimated in the positive pressure region and overestimated in the negative pressure region. This is due to the static pressure which significantly decreases the streamwise direction within the test section, and the difference in static pressure is logarithmically proportional to the mean wind speed. Additionally, dynamic pressure is uniformly distributed along the test section axis. The inaccurate measurement of static pressure leads to these results. To address this issue, an indirect measurement method was employed to correct the static pressure results and reduce the error in the mean wind pressure coefficient to within 10%. Furthermore, differences in turbulence integral scale result in an error of up to 16% in the RMS wind pressure coefficient. Therefore, when conducting rigid model pressure measurement wind tunnel experiments, especially in open-circuit wind tunnel laboratories, careful consideration should be given to the influence of static pressure drop and integral length scale of turbulence.  
CAARC; wind tunnel experiment; pressure measurement experiment; turbulence integral scale +
Footnote †: journal: Applied Sciences
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Voltage and Reactive Power Control in Operation
#### 3.6.1 Methods for Maintaining Optimal Voltage Levels in the Grid  
For maintaining optimal voltage levels, Ref. [125] explores the integration of on-load tap changers (OLTC), voltage regulators (VR), capacitor banks (CB), and DGs through a mixed integer second-order cone programming (MISOCP) model with model predictive control (MPC). This method aims to minimize operational losses and sustain optimal voltage, utilizing linear programming and machine learning for better stability and response.  
Furthermore, Ref. [194] details a centralized control strategy for ADNs that merges proportional integral and corrective control via MPC. This strategy benefits from real-time data and forecasts to enhance system adjustments, demonstrating MPC's role in digital grid management to address voltage and quality issues proactively, thus ensuring stability and efficiency.
# Mobile Personal Multi-Access Edge Computing Architecture Composed of Individual User Devices
## 3 Performance Evaluation  
To evaluate the performance of the proposed scheme, we conducted experiments on various assumptions in which mobile users transmit computing tasks to access MECS and move to other access MECS. To do this, we implemented the network topology and MEC environments using CloudSim and Edge CloudSim respectively [35; 36]. The mobile users moved according to a nomadic mobility model in which they move to other access MECS after a random amount of time has passed [37; 38]. In our simulation model, there were three mobility types with different preference levels [39; 40]. The mobile user was likely to spend more time in that access MECS in the case of low preference level.  
In other words, the mobile user preference level of the location directly affected the dwelling time that the mobile user spent in the related access MECS. Million instructions per second (MIPS) was used to describe the ability to perform one million commands per second for the complexity of the computing operation. In addition, this paper handled the computation-intensive task and task offloading of mobile users using containerization technology, such as Docker, to handle tasks performed in simulation environments [41; 42; 43]. The percentages in the figures in this paper represent the usage of MEC resources. In Table 2, the important simulation parameters and their values are listed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Description** \\ \hline User Mobility Model & Nomadic Mobility Model \\ Number of Mobile User & 50–200 \\ Number of Mobile Preference Level & 3 \\ Probability of Selecting a Mobile Preference Level & Equal (1/3) \\ Number of access MECS per Place & 1 \\ VM Processor Speed (MIPS) per access MECS & 955 \\ Processor Speed (MIPS) per mMECS & 19 \\ WLAN Bandwidth (Mbps) & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 9: Flow Chart of the Mobile Personal Multi-Access Edge Computing Architecture.
## 2 Fundamental Principles  
Spectral domain LCI investigates the optical path length and magnitude of backreflected light from a sample to determine the samples' distance offset. This involves analysing the achieved interferometric signal frequency modulation which is dependent on the optical path difference between a stable reference signal and the signal returned from the measured sample; this is commonly achieved through conversion of the signal into the Fourier domain via a Fast Fourier Transform (FFT). This behaviour is well outlined in literature especially for measurement of biological systems with OCT [45; 46]. This section will clarify the step by step underlying theory of how to achieve a spatial distance measurement from a simple LCI system.  
A Michelson interferometer configuration is commonly used for the interferometric portion of the LCI sensor which is outlined in Figure 1a, the mathematical description of the light signal is also shown at each stage of progress. Figure 1b,c shows optical components can also be added at the exit of the fibre, allowing for the control of specific beam parameters (shape, depth of focus, focal length, and intensity distribution). Additionally, scanning methods can be added to perform profilometry as shown in Figure 1b. However, when applying such a sensor to operate within the spatially constrained non-ideal environment present in many manufacturing procedures scanning systems may not allow for the required robustness and spatial tolerances imposed within the measurement area, thus scanning procedures such as shown in Figure 1c may be more appropriate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**First Seen in Literature** & **Application Domain** & **Publications** \\ \hline
1984 & Absolute ranging for detecting faults in fibre optic cables & [15; 16; 17; 18] \\
2004 & Art and Archaeology inspection & [19; 20; 21] \\
2005 & Measurement during laser machining & [21; 22; 23; 24; 25] \\
2005 & Material investigation & [26; 27; 28] \\
2007 & Investigation of paper structure & [29; 30] \\
2008 & Electronic board and micro-electronics & [31; 32] \\
2008 & Profiling measurements & [33; 34; 35; 36; 37; 38; 39; 38] \\
2009 & Nuclear research reactor & [39] \\
2009 & Surface roughness measurements & [40; 41] \\
2011 & Measurement of tablet coatings offline and online & [11; 42] \\
2013 & Measurement of optical components such as GRIN material & [43] \\
2013 & Tracking film growth & [44] \\
2020 & Measurement in liquid jets & [13] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Publication breakdown by application domain, table ordered by first appearance of application in literature.  
Figure 1: (**a**) schematic of Michelson interferometer for LCI sensor, (**b**) scanning by galvanometer mirror, (**c**) scanning via moving probe or moving sample, (**d**) signal term for a singular sample reflector and corresponding equations relating to signal features. For multiple reflectors, the cross-correlation component is a superposition of sinusoids.
## 3 Interference Mitigation Schemes
### Power Control Approach
#### 3.1.4 Fast Converging Fuzzy Power Controller  
In [20], the fast converging fuzzy power controller (FPC) system model is described as a fuzzy system with a feedback channel to minimize transmission power and maximize link throughput. Genetic algorithm (GA) is applied to determine the optimal fuzzy controller parameters.  
The system model contains the inputs, output, and fuzzy power controller. The input signals are SINR, current interference power, and a feedback signal. The output is the current transmission power, which works as the feedback channel to the input of the system. The fuzzy power controller decides on the level of transmission power at the output based on its inputs. The fuzzy power controller consists of a fuzzy knowledge base, crisp input, crisp output, fuzzifier, fuzzy interference engine, and defuzzifier.  
GA is employed at the fuzzy knowledge base to control the fuzzy interference engine. GA learning mechanism is used to describe the fuzzy rules and the output variables to create suitable parameters for the design criteria. GA is divided into four stages: The first stage is called the chromosomes where parametric genes and rule genes represent the fuzzy set function of inputs and fuzzy logic rule, respectively. The second stage uses crossover and mutation operators to obtain new individuals. The third stage is called the learning strategy which chooses the best individuals. The last stage tries to maximize the fitness function that is represented to the link capacity, transmission power, and number of iterations needed to converge. More detailed information on the genetic-fuzzy system can be found in [20].