esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sample Preparation  
To examine heavy metal contamination in soil, we gathered soil samples from areas near industrial sites, mining operations, transportation routes, waste disposal facilities, parks, and residential areas. Our prominent sampling locations included zip codes 35207 and 35217 (Superfund sites) and 35214 (control area), known for significant metal pollution from metallurgical activities, coal power plants, and mining operations in North Birmingham's Superfund sites.  
To ensure accurate soil testing, we carefully collected samples from the topsoil layer (5-10 cm depth) using appropriate tools and protective gear, such as gloves, trowels, containers, tags, documentation forms, and GPS devices, ensuring they remained uncontaminated. Before processing, each sample was stored in individually labeled containers with essential information, including the location name, GPS coordinates, and date.  
For sample preparation, we manually removed organic matter, gravel, and rocks, followed by drying the samples at 130-140 \({}^{\circ}\)C for 1-4 h, depending on the soil moisture content. The samples were then ground using a stainless-steel grinder, and the resulting powder was sifted through a 100 \(\upmu\)m plastic sieve into a clean, uncontaminated plastic container. This sieving process was repeated until all fine particles passed through the sieve, with larger particles reintroduced into the grinder for reprocessing as needed. The grinder was cleaned with methanol after processing each sample to maintain accuracy.  
Subsequently, 2.5 g of the sifted soil sample was used to create compressed pellets using a hydraulic press, applying 8 metric tons of pressure for 3 min, followed by a 30 s resting period. These compressed pellets were then analyzed using LIBS to assess soil properties comprehensively.  
The calibration samples were prepared using actual soil samples that contained lower initial concentrations of concomitants, according to ICP-MS measurements. For arsenic (As) and lead (Pb), we selected samples with initial concentrations of 12 mg/kg and 14 mg/kg, respectively. Here and further, the concentrations of analytes refer to dry weight. For manganese (Mn), we used a soil sample with an initial concentration of 63 mg/kg. We added the appropriate amounts of standard solutions for atomic absorption spectrometry (AAS), obtained from Fisher Scientific, to each sample to achieve the desired concentrations for the calibration curves. The samples were then dried and pressed into pellets as described  
Figure 2: Experimental setup.  
above. We verified the accuracy of the calibration sample preparation using Mn samples with the ICP-MS method and obtained a good match with the understanding that ICP-MS spectrometry, requiring the conversion of dry samples to liquid, is prone to leaching and the possible loss of analytes. Assuming the correctness of our sample preparation procedure, we proceeded in the same manner with As and Pb.
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 6 Conclusions  
The issues related to the allocation of resources over LTE networks are regarded as a major challenge that can provide satisfactory QoS to the active users for every transmission time interval (TTI). The resources that are available on the network are distributed amongst all the users in such a way that they can fulfil their needs. The researchers investigated the different scheduling algorithms (such as PF, MLDWF, EXP-PF, and FLS) based on the LTE fairness index. For this purpose, they proposed a novel scheduling algorithm for improving the low-performance deterioration of the network by applying the ANN algorithm technique, i.e., the approximate radial basis function neural network (RBFNN), the exact RBFNN, and the generalized regression neural network (GRNN). The exact Gaussian fields are approximated using the appropriate radial function (RF) for different radio services. This study investigated the performance of a popular resource allocation algorithm used in the LTE downlink stream to satisfy the QoS requirements. The results indicated that the proposed method improved performance over the other algorithms based on all calculated metrics. The algorithm indicated an improved percentage value and a higher fairness factor. Moreover, to overcome fundamental challenges like high data rate real-time traffic, high QoS, reliable and robust communication links, and an ever-increasing demand for network services, future wireless networks will depend on the new technological advancement known as ANNs. Today's mobile or cellular consumers are increasingly utilizing multimedia services. With the increased data rate based on ANNs and machine learning, seamless multimedia services are now accessible.  
Conceptualization, A.A.M., F.Y.H.A. and B.K.; methodology, A.A.M., F.Y.H.A., B.K., D.A.Z. and J.H.Y.; software, A.A.M. and F.Y.H.A.; formal analysis, A.A.M., F.Y.H.A., B.K., D.A.Z. and J.H.Y.; investigation, A.A.M. and F.Y.H.A  
**Funding:** The research leading to these results has received a research grant from the Ministry of Higher Education, Research and Innovation (MoHERI) of the Sultanate of Oman under the Block Funding Program. MoHERI Block Funding Agreement No: MoHERI/BFP/SU/01/2021.  
**Data Availability Statement:** Data is available on request due to restrictions on privacy.  
**Acknowledgments:** The corresponding author would like to thank all other contributors for their guidance in an advisory position and for helping to develop an attitude helpful to scholarly research and development. The authors do not have competing interests.  
**Conflicts of Interest:** All authors have stated that they have no competing interests. The sponsors had no input into the study's conception or execution, data acquisition, analysis, interpretation, article preparation, or final publication decision.
## 3 Creep Controller Model
### Features of Reward Function  
The appropriate definition of the reward function can directly determine whether the learning process of RL algorithms can efficiently and quickly achieve the desired training objectives. The main task of RL is to maximize rewards by selecting the best action at each sampling time. The ultimate goal of control algorithm for the electric locomotive is to achieve the optimal creep rate utilization in uncertain and harsh roadway environments, reduce the occurrence of slip and idling phenomena, and achieve the best operating efficiency under the premise of safe operation. Therefore, the reward function is directly related to the control effect of the mining electric locomotive's driving speed and creep rate under different states.  
Whether the mining electric locomotive has encountered obstacles or reached the maximum speed of the corresponding road section is classified during the state setting. When setting the reward function, whether the vehicle is accelerating, decelerating, or running at the uniform speed in the current state should be considered. The vehicle changing at a faster speed to achieve the desired control purpose will present a better training effect. The following speed reward conditions is defined based on repeated experiments. The main way to measure the size of the reward value is to calculate the speed change that occurs during the unit sampling interval for the vehicle to achieve control objectives.  
\[reward_{\text{speed}}=c_{\text{speed}}\times(v_{\text{record}}-v_{\text{ current}}) \tag{11}\]  
where, \(c_{\text{creep}}\) represents the speed reward coefficient, which can be obtained through multiple experiments.  
According to the control goal proposed in Section 2.4 of the paper to control the creep rate within the range of \(\pm\)10% to \(\pm\)30%, the creep control reward conditions are set. We have comprehensively considered the creep rate of four wheels, and the evaluation criteria for the creep rate reward are set as follows.  
When the absolute values of the creep rate of the left front wheel \(|\xi_{\text{FL}}|\), right front wheel \(|\xi_{\text{FR}}|\), left rear wheel \(|\xi_{\text{BL}}|\), and right rear wheel \(|\xi_{\text{BR}}|\) are all approximately equal to the optimal creep rate of 0.2 when taking two decimal places, the reward value can be set to:  
\[reward_{\text{creep}}=c_{\text{creep\_max}} \tag{12}\]  
In other cases, the reward value is set to:  
\[reward_{\text{creep}}=\frac{c_{\text{creep\_max}}}{\sqrt{(|\xi_{\text{FL}}|- 0.2)^{2}+(|\xi_{\text{FR}}|-0.2)^{2}+(|\xi_{\text{BL}}|-0.2)^{2}+(|\xi_{\text{ BR}}|-0.2)^{2}}} \tag{13}\]  
where, \(c_{\text{creep\_max}}\) and \(c_{\text{creep}}\) represent the reward coefficient for optimal creep rate and the reward coefficient for creep rate respectively, which can be obtained through multiple experiments.  
We add the evaluation results of speed and creep with ratio of 1:1 to obtain the final reward function formula:  
\[reward=reward_{\text{speed}}+reward_{\text{creep}} \tag{14}\]
## 3 Results
### Stage C
#### 3.3.1 Analysis  
The Friedman test was applied to check whether the differences found between the two design stages were statistically significant since those were ordinal data. The results indicate that the Stage B scores were higher. They are shown in Table 4.  
In Stage C, the results of Stage A, which was based on the traditional design process, were compared with the results from Stage B, the hierarchy-based design process, which are shown in Figure 17.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**Stage A**} & \multicolumn{2}{c}{**Stage B**} & \multirow{2}{*}{**[PERSON]**} \\ \cline{2-2} \cline{5-6}  & **Mean** & **s. d.** & **Mean** & **s. d.** & **Test** \\ \hline Safe & 3.81 & 1.05 & 4.27 & 1.00 & 52.25 *** \\ Convenient & 3.74 & 1.09 & 4.02 & 1.05 & 22.37 *** \\ Efficient & 3.73 & 1.14 & 4.07 & 1.02 & 17.75 *** \\ Attractive shape & 3.77 & 1.14 & 4.23 & 1.02 & 48.13 *** \\ Color & 3.74 & 1.22 & 4.32 & 0.98 & 70.00 *** \\ Adaptable & 3.78 & 1.14 & 4.17 & 0.99 & 23.22 *** \\ Technology easy to use & 3.87 & 1.12 & 4.11 & 0.96 & 8.73 *** \\ Suitable material & 3.89 & 1.11 & 4.13 & 0.94 & 15.02 *** \\ Comfort & 3.65 & 1.21 & 4.27 & 1.11 & 43.84 *** \\ Consideration of needs & 3.77 & 1.26 & 4.1 & 0.98 & 34.59 *** \\ Easy to use & 3.74 & 1.19 & 4.23 & 0.99 & 32.00 *** \\ I would agree to use it & 3.68 & 1.23 & 4.09 & 1.1 & 33.53 *** \\ Consider my special needs & 3.54 & 1.24 & 3.92 & 1.15 & 28.45 *** \\ \hline \hline \end{tabular}  
* * \(p<0.01\); *** \(p<0.001\).  
\end{table}
Table 4: Comparison between the characteristics of the designs of birthing beds (\(n=453\)).  
Figure 17: Differences in the evaluation of the design characteristics between Stage A and Stage B.
# Particle-Velocity-Based Mixed-Source Sound Field Translation for Binaural Reproduction
## 3 Particle-Velocity-Based Mixed-Source Expansion
### Mixed-Source Model  
Different from the plane wave model [20], which represents a sound field with an equivalent superposition of virtual plane wave sources, the mixed-source model expands the sound field in terms of a mixture of near-field and far-field virtual sources [39]. This mixed-source model can overcome the difficulties of the plane wave expansion in synthesizing the near-field sources.  
To blend the model of a near- and far-field virtual source together, we define a normalized point-source to be the mixed-source, defined as  
\[P(\mathbf{x},k;\mathbf{y})=|\mathbf{y}|e^{-ik|\mathbf{y}|}\frac{e^{ik|\mathbf{y}-\mathbf{x}|}}{4\pi| \mathbf{y}-\mathbf{x}|}, \tag{14}\]  
where \(\mathbf{y}\) denotes the position of the virtual mixed source. In the spherical harmonic domain, it can be expressed as  
\[P(\mathbf{x},k;\mathbf{y})=\sum_{n=0}^{\infty}\sum_{m=-n}^{n}\underbrace{ik[|\mathbf{y}|e ^{-ik|\mathbf{y}|}h_{n}(k|\mathbf{y}|)Y_{nm}^{*}(\hat{\mathbf{y}})}_{\beta_{nm}(k)}j_{n}( k|\mathbf{x}|)Y_{nm}(\hat{\mathbf{x}}), \tag{15}\]where \(h_{n}(\cdot)\) is the spherical Hankel function of the first kind, and \(\beta_{nm}(k)\) are the pressure coefficients of the mixed source. We note that the mixed source has the property of [26]  
\[\lim_{|y|\to+\infty}P(\mathbf{x},k;\mathbf{y})=\frac{e^{-ik\hat{y}\cdot\mathbf{x}}}{4\pi}, \tag{16}\]  
which shows that the plane-wave expansion can be modeled using the mixed-source expansion placed in the far field.  
We construct a virtual equivalent sound field using two concentric spheres of virtual mixed sources. Therefore, the sound field can be expressed, using the mixed-source model, as [39]  
\[\begin{split} P(\mathbf{x},k)&=\int\psi(k,R_{N}\hat{y}; \mathbf{o})\underbrace{R_{N}e^{-ikR_{N}}\frac{e^{ik||R_{N}\hat{y}-\mathbf{x}||}}{4\pi|| R_{N}\hat{y}-\mathbf{x}||}}_{P(\mathbf{x};R_{N}\hat{y})}d\hat{y}\\ &+\int\psi(k,R_{F}\hat{y};\mathbf{o})\underbrace{R_{F}e^{-ikR_{F}} \frac{e^{ik||R_{F}\hat{y}-\mathbf{x}||}}{4\pi||R_{F}\hat{y}-\mathbf{x}||}}_{P(\mathbf{x}; R_{F}\hat{y})}d\hat{y},\end{split} \tag{17}\]  
where \(R_{N}\) is the radius of the virtual sphere placed in the near field, \(R_{F}\) is the radius of the virtual sphere placed in the far field, \(\psi(k,R_{N}\hat{y};\mathbf{o})\) and \(\psi(k,R_{F}\hat{y};\mathbf{o})\) are the driving functions of the mixed-source distributions as observed at \(\mathbf{o}\), and \(P(\mathbf{x},k;R_{N}\hat{y})\) and \(P(\mathbf{x},k;R_{F}\hat{y})\) are the mixed sources on the two spheres. Note that the mixed-source model expands the sound field with respect to \(\mathbf{o}\), which allows us to observe the source distribution at \(\mathbf{o}\) and estimate the sound at any translated position \(\mathbf{x}\).
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CAF & Cross Ambiguity Function \\ CEP & Circular Error Probable \\ CRB & Cramer-Rao Bound \\ CTN & Coarse Time Navigation \\ DD & Double Difference \\ DOP & Dilution Of Precision \\ GNSS & Global Navigation Satellite System \\ IAR & Integer Ambiguity Resolution \\ IF & Intermediate Frequency \\ IoT & Internet of Things \\ LAMBDA & Least-squares Ambiguity Decorrelation Adjustment \\ LBS & Location Based Service \\ LRF & LAMBDA Ratio Factor \\ PRN & Pseudo Random Noise \\ PVT & Position Velocity Time \\ RAIM & Receiver Autonomous Integrity Monitoring \\ RMS & Root Mean Square \\ RTK & Real Time Kinematics \\ SD & Single Difference \\ SNR & Signal to Noise Ratio \\ TEC & Total Electron Content \\ TOW & Time Of Week \\ VRS & Virtual Reference Station \\ ZTD & Zenith Total Delay \\ \end{tabular}
## 3 PIC Simulation Results
The blue dashed curve is the laser pulse intensity profile as a function of time, for reference; **(b)** Relativistic critical density velocity along the Z = 0 axis as a function of time (dot-dashed blue line) from the 2D PIC simulation results. This velocity is calculated as the gradient of the black dashed line in **(a)**, which delineates the relativistic critical density surface at each time; The solid green line is the time-dependent hole-boring (HB) velocity from Equation (1); The time-dependent LS velocity using a top-hat and a Gaussian laser pulse temporal profiles are shown as the dot-dash orange and solid red lines, respectively, as calculated using Equation (4). All results correspond to \(l\) = 10 nm. Corresponding plots for: (**c**,**d**) \(l\) = 40 nm; (**e**,**f**) \(l\) = 100 nm; and (**g**,**h**) \(l\) = 200 nm.
###### Abstract  
The goal of this study was to analyze instability during stepping at different speeds in young and older adults. To this aim, the anteroposterior and the mediolateral distances between the body center of mass (COM) and the minimum moment axis (MMA) were computed. A total of 15 young adults (25 y.o. [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]) and 15 older adults (68.7 y.o. [63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77]) volunteered for this study. For the computation of the distances, a complete biomechanical protocol combining two force platforms and a 3D motion capture analysis system was setup. The subjects were equipped with 47 reflective markers and were modeled as a frictionless multibody system with 19 segments, 18 joints and 42 degrees of freedom. They were asked to perform a series of stepping tasks at fast and spontaneous speeds. The stepping was divided into five phases, with successive swing and double-stance phases. Greater instability was observed during the swing phases. The distances reveal a significant higher instability at fast speed for both groups (\(p<0.001\)) for all the phases compared with spontaneous speeds. The anteroposterior distance was significantly greater for older adults, highlighting greater instability compared to young adults, while no differences were observed for the mediolateral distance all along the five phases, suggesting higher risks of backward and forward falls during stepping.  
biomechanics; gait; angular momentum; locomotion Article
## 1 Introduction
To minimize the performance degradation relative to the BPS based on the global CSI, we introduce the correlation coefficient between the end-to-end signal-to-noise ratios (SNRs) calculated by global and local CSI for each path, and find the subset of hops for local CSI in each path that maximizes the correlation coefficient. As the SNR correlation coefficient quantifies the statistical similarity between the end-to-end SNRs for global and local CSI, the proposed PPS with local CSI providing high SNR correlation can work most similarly to the BPS with global CSI. Assuming DF relays and Nakagami-\(m\) fading channels, we present an exact and closed-form expression for the SNR correlation coefficient. In addition, we provide an exact and closed-form expression for the end-to-end outage probability of the PPS using local CSI so as to investigate an impact of the SNR correlation coefficient on the end-to-end outage probability of PPS and compare the end-to-end performances of BPS and PPS using the SNR correlation. Main contributions of this paper are summarized as follows:  
* The PPS scheme for dual-hop relaying systems is developed and generalized for multi-hop ones under Nakagami-\(m\) fading channels. * The correlation coefficient between the end-to-end SNRs for local CSI for a subset of hops and global CSI on a given path between a source and a destination is incompletely introduced to quantify a level of similarity between those for the local and global CSI. * The exact SNR correlation coefficient for a given subset of hops on a given path is derived under Nakagami-\(m\) fading channels, and its closed-form expression is provided. * The exact and closed-form expression for the end-to-end outage probability of PPS with local CSI in Nakagami-\(m\) fading channels is derived to evaluate the end-to-end performance of PPS based on the SNR correlation coefficient. * Through numerical investigation, we show the relationship between the SNR correlation coefficient and the end-to-end outage probability of PPS and find certain channel conditions in order that the PPS with the highest SNR correlation coefficient achieves the similar performance to the BPS. The rest of this paper is organized as follows: related works to our research are covered in Section 2. In Section 3, we present the system model for multi-hop cooperative communications with parallel relays, BPS and PPS schemes, and their SNR distributions assuming independent Nakagami-\(m\) fading channels. In Section 4, we introduce the correlation coefficient between the end-to-end SNRs for local and global CSI in order to find the best PPS and derive a closed-form expression for the SNR correlation coefficient. In Section 5, the end-to-end outage probability is analyzed to evaluate the end-to-end performance of the multi-hop cooperative system with the PPS using local CSI. In Section 6, we provide numerical results to verify the analysis of the SNR correlation coefficient and outage probability and show the impact of the SNR correlation coefficient on the end-to-end performance of PPS. Finally, in Section 7, we conclude the paper.
## 2 GFB-OFDM Transceiver
### Advantages  
GFB-OFDM utilizes the two-level IFFTs to decompose the original large-size IFFT into two small-size IFFTs, enabling the operations of large-size IFFT dedicated to the ultra-wide bandwidth scenarios. Furthermore, the GFB-OFDM waveform possesses the following advantages:  
1. By alternating the number of subbands while fixing the subband bandwidth, it is possible to achieve data services in different channel bandwidths. Since the filtering is performed on each subband, a single filter is sufficient. In contrast, each service with a different bandwidth required a different filter in CP-OFDM. This facilitates the flexible support for various channel bandwidth requirements in certain hardware scenarios.
2. The polyphase filtering in GFB-OFDM efficiently employs an individual filter for each subband, thus mitigating the potential occurrence of the out-of-band leakage of the subbands. Therefore, it can accommodate various scenarios where different subcarrier spacings are used for different subbands, and these diverse subcarrier spacings can be uniformly processed using GFB-OFDM waveform processing.
3. Different types of data can be assigned to different subbands within a single GFB-OFDM symbol, and they can be modulated either in a multicarrier manner or a single-carrier manner. The submodule for per subband processing in GFB-OFDM enables a simultaneous processing of different types of data-bearing subbands, achieving an integration of single-carrier processing and the multi-carrier processing.
4. The receiver of the 4G/5G OFDM is the legacy of the proposed GFB-OFDM waveform without introducing any additional complexity.
# Vortex-Induced Micro-Cantilever Vibrations with Small and Large Amplitudes in Rarefied Gas Flow
###### Abstract  
This study employs a fully coupled fluid-structure interaction (FSI) to investigate the vibrations of an elastic micro-cantilever induced by a rarefied gas flow. Two distinct models are employed to characterize the beam vibrations: the small deflection Euler-Bernoulli beam theory and the large deflection beam theory. The cantilever is oriented normally to the free stream, creating a regular Karman vortex street behind the beam, resulting in vortex-induced vibrations (VIV) in the micro-cantilever. The Direct Simulation Monte Carlo (DSMC) method is used to model the rarefied gas flow to capture non-continuum effects. A hybrid numerical approach couples the beam dynamics and gas flow, enabling a fully coupled FSI simulation. A substantial number of numerical computations indicate that the range of vibration amplitudes expands when the natural frequency of the beam approaches the vortex shedding frequency. Notably, the large deflection beam theory predicts that the peak amplitude occurs at a slightly lower frequency than the vortex frequency. In this frequency range, as well as for thinner beams, the amplitude ranges predicted by the large deflection beam theory exceed those obtained from the small deflection beam theory. This finding implies that for more complex behaviours involving nonlinear effects, the large deflection theory may yield more accurate predictions.  
fluid-structure interaction (FSI); large deflection beam theory; small deflection Euler-Bernoulli beam theory; Direct Simulation Monte Carlo (DSMC); vortex-induced vibrations (VIV) 2020