esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Voided Reinforced Concrete by Numerical Modelling of Impact-Generated Rayleigh Waves
## 4 Results of Simulations and Discussion
### Time-Domain Data  
Typical waveforms collected for 5 kHz and 50 kHz are shown in Figure 9. The first positive peak was identified as the R-wave. At 5 kHz, the wave amplitude decreased slightly from Sensor 4 to Sensor 7. With a 100% void, the amplitude was amplified at Sensor 4 and decreased afterwards. Delays in the R-wave peak became more pronounced with increasing void density.  
When a wave passes through distributed voids, the voids act like obstacles, which increases the wave's travel distance inside the voided block, resulting in delays in the wave. Also, the voids absorb and reflect portions of the wave energy. With distributed voids, the reflected waves are scattered within the voided block, whereas with a 100% void,  
Figure 8: Waveforms were collected for different propagation distances, with excitation frequencies of (**a**) 5 kHz and (**b**) 50 kHz.  
Figure 9: Stacked waterfall plots with an excitation frequency of 5 kHz for (**a**) 10% void and (**b**) 100% void models. Displacement is a normalised value.  
the reflected wave travels back to the surface and converges with the propagating wave, resulting in the amplitude increase observed at Sensor 4. At Sensor 7, the wave energy is dissipated by different mechanisms, such as damping by the concrete, absorption, and scattering by the voids.  
The wavefront of an R-wave is distorted when it encounters voids in its path. This distortion occurs due to diffraction, which occurs when the wave interacts with the boundaries (edges) of the void, particularly when the void size is comparable to the R-wave's wavelength. In such cases, the wave bends around the void, rather than simply being blocked. Additionally, as the R-wave propagates through or around the void, scattering occurs, producing a complex wavefield behind it. This wavefield consists of multiple zones with varying amplitude strengths and phase relationships due to interference patterns. When multiple voids are present, dispersion takes place as the wave energy is redistributed in two ways: partial transmission through or around the empty space and reflection (which depend on the void's size, shape, material properties), and the R-wave's incidence angle.
## 3 Sensor Modules and Deployment
### PIR Sensor Node  
The PIR sensor node consists of four sensing units (PIR sensor module), a processing unit, and a communication subsystem. The sensing unit is usually composed of PIR sensor, Amplifier and Actuator. The analog original signal captured by sensor module is primarily amplified by amplifier, and then it is converted to digital signal by the Analog to Digital Converter (ADC) module. In the processing unit, the STM32 CPU has embedded 256 kB of flash memory and 48 kB of RAM for program and data. Furthermore, all the coordinating sensing and communication tasks are executed by this 32-bit CPU at 72 MHz. The memory sub-unit can store sensing data in a period of time. The communication subsystem composed of a transceiver sub-unit and a processing circuit connects the device to the network.  
The processed signal is sent to the wireless gateway by communication subsystem on the basis of Zigbee protocol in the specified interval. Moreover, the whole system is powered by a power unit that can be supported by an energy scavenging unit such as solar cells. The physical device is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline IR Receiving Electrode & 0.7 \(\times\) 2.4 mm, 4 elements \\ Sensitivity & \(\geq\)4300 V/W \\ Detectivity (D*) & 1.6 \(\times\) 108 cm (Hz)\({}^{12}\)/W \\ Supply Voltage & 3–15 V \\ Operating Temp & \(-\)30–70 \({}^{\circ}\)C \\ Offset Voltage & 0.3–1.2 V \\ FOV & 150\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: The Parameters of D205B Pyroelectric Infrared Sensor.  
Figure 2: Human thermal infrared signal processing in sensor module.
## 4 Design of Pit-Probing Control Based on Admittance Control Method
### Simulation Based on Admittance Control  
The motion simulation of the single leg based on admittance control was carried out in the Simulink simulation environment. The stiffness parameter of the contact ground was set as \(2\times 10^{4}\), the damping parameter as \(3\times 10^{4}\), and the gravitational acceleration as \(9.8\) m/s\({}^{2}\). The single leg was placed above the ground at a certain height to perform ground detection according to the preset trajectory until it made contact with the ground, and the process of pit-probing after stepping into empty space was simulated, as shown in Figure 15.  
Figure 14: Dynamic response diagrams of impedance parameters optimized by two methods: (**a**) curve of touchdown force; (**b**) curve of position.  
Figure 13: Variation curves of touchdown force and position with stiffness parameters: (**a**) curve of touchdown force; (**b**) curve of position.  
Simulations were conducted on the non-impedance control, the admittance control, the ETM, and the WMA, respectively. The control effects of the three methods on the change of normal touchdown force over time were compared, and the results are shown in Figure 16.  
As shown in Figure 14, without using impedance control, the maximum contact force at the foot end was 620 N. After optimizing the impedance parameters using the ETM, the maximum contact force dropped to 280 N. By further optimizing the parameters with the WMA, the maximum contact force decreased to 230 N, which verifies the effectiveness of the impedance control and the parameter optimization algorithm.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 3 Low Complexity Detection Algorithm
### BER Performance Comparison of the MIMO-QSM Scheme  
In this subsection, two different configurations were used in order to compare the BER performance of the proposed MIMO-QSM scheme for the ML detection under uncorrelated Rayleigh fading channels. The systems were analyzed considering the same spectral efficiency, the same number of Tx and Rx antennas, and a normalized transmission power in the transmitter. For all computer simulations, we targeted a BER of \(10^{-4}\).  
Figure 2 shows the performance comparison for the optimal ML and the proposed low complexity Near-ML algorithm for the MIMO-QSM scheme using the \(2\times 2\) and \(8\times 8\) configuration with QPSK modulator. For both cases, the proposed detector performed very near to the ML algorithm, and for this reason, we called our proposal detector Near-ML.
## 3 Colloidal Silica Transport Through Soil Stratum
### Transport Mechanisms of CS in Liquefiable Sand
#### 3.2.3 Injection Rate  
[PERSON] (2010) reported that CS has a tendency to sink downwards in a 9 m\({}^{2}\) field injection when the injection rate is less than 3700 mL/min/well [66]. [PERSON] and [PERSON] (2013) verified the sinking behavior of CS using numerical method. They believe that the maximum transport distance for CS ranges from 2.5 m to 4 m due to the higher density of CS solutions compared with pore water [67; 68]. To further investigate the sinking behavior and horizontal movement of CS grouting, they developed a 10 m\({}^{3}\) pilot-scale box model [69], as shown in Figure 13. The loose sand layer was placed in the box model by freely pouring sand onto water. Four injection wells and 4 extraction wells were set at the upstream and downstream sides, respectively. A constant head water flow was established, lasting for  
Figure 12: Relationship between flow rate of CS and viscosity [45].  
7 days, saturating the sand layers prior to grouting. In low injection rate tests, injection rates of 65 and 130 mL/min/well were adopted to transport CS into the sand layers. However, CS was mainly present at the bottom of the model upon excavation, indicating that the CS sinking behavior is significant. When the injection rate increases above 1900 mL/min/well, sufficient horizontal pressure forces the CS grouting to horizontally move to the targeted location. Therefore, the transport of CS is more likely to be successful with a high injection rate. Since it is difficult for CS to permeate through porous media when the viscosity increases up to 4 cP [45], the gel time is expected to be appropriately controlled to meet the conditions where viscosity can increase beyond 4 cP to prevent gravitational sinking once the target location is reached.
## 4 Discussions
### The Differences between the Results of the Graphic Method (GM) and Moment Method (MM)
Figure 4: Grain size distribution curves of the different skewness descriptive terms (GM results vs. MM results. Different color curves indicate grain size distribution curves for different samples. (**a,d**) indicate consistent by GM and MM, but (**b,c,e**) exist discrepancy). Figure 5: Grain size distribution curves of the different kurtosis descriptive terms (GM results vs. MM results. (**e**) indicate consistent results using GM and MM, whereas (**a–d,f,g**) are inconsistent). Comparison and Selection of Grain Size Information Extraction between Graphic and Moment Methods for Flood Sediments  
The GM and MM use calculation equations and terminology descriptors to obtain quantitative and qualitative grain size parameters. However, the different methods had differing sensitivities to changes in the sediment particle size curves, which led to differences between the GM and MM in determining effective sediment information [3]. The mean grain size serves as an indicator of the central tendency of the grain size frequency distribution and can be utilized to reflect the strength of the hydrodynamic force, thereby holding significance in identifying flood sediments [3; 14]. Previous studies reported a very strong correlation between the GM and MM when estimating mean grain sizes, and the two methods produced nearly identical outcomes [14; 16; 17; 31; 32; 33]. Thus, the differences in the GM and MM calculation methods did not affect the extraction of mean grain size information. The sorting coefficient indicates the dynamic process of separating particles with similar characteristics from complex environments. Owing to the powerful hydrodynamic forces of floods, sediment sorting tends to be poor. Previous studies have reported that if the linear correlation between the GM and MM is less than one, the GM provides greater sensitivity, whereas if it exceeds one, the MM is more sensitive [14; 33]. In this study, the regression equation slope of the sorting coefficient line between the GM and MM was 0.5555 (less than 1). These results indicate that the GM is more sensitive when the features of flood sediments in the lower Yellow River are highlighted using the sorting coefficient. Skewness can be used to describe the asymmetry of the grain size frequency curve and reflect dynamic changes during sedimentation [12; 13; 27; 30]. Kurtosis is used to describe the peak of the frequency curve and reflects the main sedimentation driving force [34; 35]. The MM considers both the head and tail grain sizes when assessing skewness and thus considers the overall grain size distribution curve information of flood sediments. Accordingly, we recommend using the MM to evaluate flood sediments in the lower Yellow River. Conversely, the GM emphasizes the peak position when assessing kurtosis, highlighting the primary dynamic characteristics of flood sediments during sedimentation. Therefore, it is recommended to use the GM to analyze the dynamic characteristics of flood sediments in the lower Yellow River.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 5 Discussion
### Task Quality  
While the primary focus of this study is on optimizing load distribution and predicting employee capability based on the Time Factor (TF), we acknowledge the critical importance of task quality in workload management systems. Task quality is a multifaceted aspect that encompasses accuracy, completeness, and adherence to standards, all of which are essential for effective service delivery in the public sector. Although this paper does not explicitly model or predict task quality, our system indirectly addresses it through a structured validation process supervised by managers.  
In the current framework, tasks are assigned to employees based on their predicted capability, as determined by the Time Factor. Once a task is completed, it undergoes a validation process where supervisors assess the output for compliance with required standards. If the task is deemed incomplete or contains errors, it is returned to the same employee for corrections, and the timer resumes until the task meets the necessary quality criteria. This iterative process ensures that tasks are completed to an acceptable standard, albeit at the cost of additional time. While this approach does not explicitly measure or predict task quality, it provides a mechanism for maintaining quality control within the system.  
The decision to focus on task completion time rather than quality was driven by the primary objective of this study: to compare machine learning algorithms for optimizingload distribution in dynamic workload management systems. Task completion time is a more straightforward and quantifiable metric, making it suitable for the comparative analysis of the algorithms in the current research. However, we recognize that task quality is equally important and can significantly impact organizational efficiency and service delivery. For instance, an employee who completes tasks quickly but with frequent errors may ultimately require more time for corrections, negating the benefits of faster task completion. Therefore, while this study does not explicitly address task quality, it lays the groundwork for future research in this area.
# A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations
## 2 Adaptive Cartesian Grid Generation Method
### Geometric Adaptation  
The dimensions of the edges of the refined sub-cells can be calculated using the integer-type parameter grid level (abbreviated as \(Lv\)):  
\[\begin{array}{l}E^{x}=L^{x}/2^{Lv}\\ E^{y}=L^{y}/2^{Lv}\\ E^{z}=L^{z}/2^{Lv}\end{array} \tag{2}\]  
Considering the center coordinates of the father cell (\(C^{x}_{\text{father}}\), \(C^{y}_{\text{father}}\), \(C^{z}_{\text{father}}\) ), and the center coordinates of the sub-cell (\(C^{x},C^{y},C^{z}\)), we have:  
\[\begin{array}{l}C^{x}=C^{x}_{\text{father}}\pm E^{x/2}\\ C^{y}=C^{x}_{\text{father}}\pm E^{y/2}\\ C^{z}=C^{x}_{\text{father}}\pm E^{z/2}\end{array} \tag{3}\]  
Firstly, an iterative refinement process is applied to intersecting grids, requiring the pre-setting of the maximum refinement level, \(Lv_{\text{max}}\), in the program. With the increase in refinement levels, the grid's depiction of the external contour becomes progressively more accurate. For Cartesian grids, the size ratio for grid transition is 2:1, necessitating that the transition speed of the grid not be too rapid. That is, a certain number of grids of the same level must be distributed in one direction. The grids in the transition area are referred to as buffer layers, denoted as \(N_{bl}\). A reasonable \(N_{bl}\) facilitates the selection of appropriate interpolation templates during the flow field solution process [30], enhancing the accuracy of the computational results. Figure 3 illustrates the process of geometric adaptive grid refinement for the entire DWP aircraft model, including the refinement of intersecting grids and the addition of buffer layers.  
Figure 3: _Cont._  
Figure 2: Domain determination and generation of initial uniform grids.
# State-of-the-Art Internet of Things in Protected Agriculture
## 3 The Crucial Technologies of IoT in Protected Agriculture
### Cloud Computing  
Cloud computing grew out of distributed computing, parallel processing and grid computing. It is an Internet-based computing system that provides hardware services, infrastructure services, platform services, software services and storage services to various IoT applications. Its essence is a system for dynamically deploying and allocating/redistributing as well as dynamic monitoring of virtualized computing and storage resource pools, thereby providing users with computing, data storage and platform services that meet QoS requirements [50; 51]. It played a strong role in promoting the development of agricultural IoT. Firstly, cloud computing can provide farmers with cheap data storage services for text, image, video and other agricultural information, which greatly reduces the storage costs of agricultural enterprises [52]. Secondly, it is difficult to make direct use of these raw data for making decisions based on the technical level of farmers. Agricultural experts can only make accurate judgments and give suggestions based on quantitative analysis. Only cloud computing can support intelligent large-scale computing systems [53; 54]. Thirdly, cloud computing could provide a secure platform for the development of various IoT applications such as crop monitoring [55].
## 1 Introduction
### Objectives of This Study  
In the paper, we propose an evaluation of the frequency and intensity of extreme droughts in current climate and future climate conditions (year 2050). Our methodology is based on a simple drought index [42] correlated to crop yield losses that can be projected into the future using a global climate model--for instance, the ARPEGE-Climat Model from Meteo-France.  
Based on this method, the objectives are to:  
* Analyze the climate and its evolution between the years 2000 and 2050;
* Detect extreme events and analyze their frequency and intensity;
* Simulate their consequences on crop yield losses.  
The model is applied to three crop categories: grasslands, soft winter wheat, and winter barley.  
This study aims to provide insight on the following issues:  
1. Do the historic records contain the most extreme events and, if not, how can we characterize them?
2. What is the probability of occurrence in the current climate conditions?
3. Will the intensity and/or frequency of these events evolve in the future?
## 4 Simulations and Results  
### Experiment Configurations  
The simulation scenarios are described as follows. There are \(N\) users, and \(K\) types of task, one base station, and the edge cloud is deployed in the base station to provide services. Each user performs one type of task. The simulation parameters are given empirical values, which are listed in Table 1[18].  
In this section, we study the effectiveness of the algorithm. We first explore the impact of the caching strategy on task execution through measuring user task completion time and energy consumption. Then, we adjust different task data transmission volume, and explore the change of task execution energy consumption with the increase of data transmission amount when using the caching strategy. We also set different cache capacities of the edge cloud, and study the impact of cache capacity on the energy consumption. Finally, by comparing three strategies: the strategy where cache-based subtasks are migrated; the strategy where non-cached tasks are all executed locally; and the strategy where non-cached tasks are all executed on the edge cloud, we evaluate the energy-saving result of the algorithm proposed in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Meaning of the Parameter** \\ \hline \(f_{1}\) & 300 MIPS & Computation rate on mobile device \\ \(f_{e}\) & 5000 MIPS & Computation rate on edge cloud \\ \(B_{s}\) & 2 Mbps & Transmission speed on mobile device \\ \(B_{r}\) & 2 Mbps & Receiving speed on mobile device \\ \(p_{l}\) & 0.80 W & Computation power on mobile device \\ \(p_{h}\) & 0.04 W & Sleep power on mobile device \\ \(p_{s}\) & 0.03 W & Transmission power on mobile device \\ \(p_{r}\) & 0.01 W & Receiving power on mobile device \\ \hline \hline \end{tabular}
\end{table}
Table 1: The value of parameters.
## 1 Introduction
The HHT spectrum scientifically and comprehensively displayed MS signal frequency characteristics and reflected the change laws of signal instantaneous energy and local abrupt change information. Compared with the conventional time-frequency analysis method, it can accurately capture the transient characteristics of signals. In this paper, HHT instantaneous frequency analysis technology was introduced. First, HHT instantaneous frequency analysis technology was applied to analyze and identify various on-site signals. Second, through rockburst analysis and prediction for two different sites, it was found that HHT instantaneous frequency analysis technology can achieve a good rockburst prediction effect, which had great significance for on-site construction and safety protection. The HHT time-frequency analysis technology by using a single-channel signal to predict rockbursts can adapt to complex tunnel construction environments.
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 1 Introduction
However, since the desired motion states are the ideal vehicle model responses according to drivers' actual input, the decoupling method can usually only be applied in the driver-in-loop system to improve the driving stability under satisfying the driver's intention. With the development of the 4 WID electrical vehicle, the supplementary control of the yaw direction could be implemented to improve the driving performance ([PERSON] et al., 2019) and guarantee the accuracy motion tracking ([PERSON] et al., 2019). The input number of 4 WID electric vehicle system is equal to the output number, which is a positive system ([PERSON] et al., 2019) and can easily be decoupled. Hence, focusing on the poor trajectory tracking accuracy problem in dynamic coupled scenarios, the dynamic model of the 4 WID vehicle was firstly built to study its coupling mechanism. To decouple the vehicle dynamic, the inverse system decoupling framework was proposed, where the back propagation neural network (BPNN) was applied to set up the inverse system, and the training dataset was simulated and collected based on stochastic inputs. The desire vehicle motion states obtained by the target lateral position and longitudinal velocity are followed by the inverse system to achieve the trajectory tracking and the dynamic decoupling. Finally, the simulation results compared with the pure-pursuit algorithm and MPC algorithm verify the effectiveness of the proposed trajectory tracking method. The paper is organized as follows: a three degrees of freedom (DOF) vehicle model is constructed and the coupling effects are analyzed in Section 2; Section 3 introduces the principle of the proposed decoupling trajectory tracking method; in Section 4, the simulation results are presented and discussed. Conclusions are given in Section 5.
## 3 Interference Situational Aware Beam Pointing Optimization  
The beam pointing optimization problem in (7) is a non-convex optimization problem, and the non-convexity is introduced by SINR in (6) [32]. Generally, the non-convex problem is challenging to be solved directly. However, as a heuristic optimization method, PSO can solve convex and non-convex optimization problems by finding the optimal solution in the search space through a collaborative particle swarm optimization and a fitness function [33]. In this section, the scheme of an interference situational aware beam pointing optimization is introduced, as shown in Figure 2. Firstly, the satellite position data are obtained. Based on the coordinate system conversion, the database of AoD and AoA is constructed. Then, the database can be utilized for PSO to optimize the beam center pointing of the UT antenna by maximizing the SINR.