esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In recent years, the domains of signal processing and information theory have shown significant interest in compressive sensing (CS) [1, 2]. Compressive sensing, alternatively referred to as compressed sensing or compressive sampling, has emerged as a potent technique for signal processing, with extensive applications across multiple fields. By leveraging the innate sparsity or compressibility of signals, compressive sensing enables the acquisition and reconstruction of signals using considerably fewer measurements than conventional methods demand. This pioneering concept has transformed data acquisition and processing across diverse domains, including image and video processing, audio processing, medical imaging, wireless communication, etc. [3, 4, 5, 6, 7].  
Conventional approaches often depend on uniformly sampling signals at the Nyquist rate, which can be computationally burdensome and require significant storage capacity. In contrast, compressive sensing enables sub-Nyquist sampling, thereby lowering the demands for data acquisition and storage while preserving the fidelity of the signal.  
The CS suggests that, compared to the number of samples required by conventional Nyquist-based approaches, a signal can be reconstructed with fewer samples (observations) [8]. However, for CS to work properly, the input signal must be highly compressible, or more specifically, sparse. A sparse signal has few active (nonzero) components in relation to its length. The signals can exhibit this property either in their sampling domain or any other underlying transform domain such as Fourier, wavelet, curvelet, etc. In CS, there are two major components: encoding (sampling) and decoding (recovery). Let vector \(x\in R^{N\times 1}\) denote \(N\) successive samples coming from a transducer. The vector \(x\) can be transformedinto a sparse vector \(X=\psi x\) through a \(N\times N\) sparse transforming matrix \(\psi\). If \(X\) contains \(k\) non-zero elements, it is said to be \(k\) sparse. The reconstruction of the original data \(x\) from \(y\in R^{M\times 1}\) measurements, where \(y=\phi X=\phi yx\), requires \(k<<M<<N\). Generating a suitable sensing matrix to improve the reconstruction process is one of the most challenging tasks in CS. The restricted isometry property (RIP) states that the performance of compressed sensing algorithms improves when the mutual coherence between the sensing matrix \(\phi\) and the transforming matrix \(\psi\) decreases [9; 10].  
The rest of the paper is organized as follows: Section 2 briefly reviews the related work. Section 3 provides a detailed explanation of the proposed technique along with the theoretical background of the different processes used in this work. In Section 4, the evaluation results for the proposed technique with detailed analysis are presented. This section also includes a comparison of various methods. Finally, Section 5 concludes the paper.
## 2 Results and Discussion
### Results
#### 2.1.6 Gene Expression Profiling  
The expression levels of unigenes were represented as FPKM (fragments per kilobase per million) values based on our data produced by Illumina/Solexa platform with false discovery rate (FDR) \(\leq\)0.001. For the Pg-Con unigenes, the FPKM ranged from 0.02 to 7807.74 with an average of 14.67, while the FPKM for Pg-MeJA ranged from 0.02 to 19,547.06 with an average of 14.17. This indicates that both Pg-Con and Pg-MeJA unigenes showed a wide range of expression level implying that Illumina HiSeq(tm)2000 could potentially identify genes with extremely low to strong expression levels in non-model  
Figure 4: Histogram presentation of clusters of orthologous groups (COG) functional classification.  
Figure 5: **(A)** Unigenes of 11 sub-categories under “metabolism” category; **(B)** Unigenes of 13 pathways under “other secondary metabolites biosynthesis” sub-category.  
plants as well. Thus, unigenes above the yellow line in Figure 6 indicate up-regulation by MeJA and below this line show down-regulation. Using FPKM values, we identified 25,999 up-regulated and 39,536 down-regulated unigenes (Table S4).
## 4 Experimental Results and Analysis  
### Raw Wind Speed Series  
The spatial and temporal variations in wind speed are complex and show obvious seasonal characteristics. In this study, the wind speed data of a wind farm in Inner Mongolia for different time periods in the four seasons in 2023 were selected as the sample dataset. The data were sampled from 0:00 to 24:00 locally and collected every minute, with a total of 5760 samples. To ensure the validity of the model training and testing, the collected data were processed with outliers. Then, the sample data were divided into training and testing sets in a ratio of 7:3, ensuring that the model achieved accurate predictions. In addition, the official website of Iowa State University was used to obtain the wind speed data of a wind farm in the United States to facilitate the comparative study. The following experimental simulations were completed on the Matlab R2021a platform. The raw data had high volatility, as did the measured wind speed data on 20 March 2023. The raw wind speed is shown in Figure 6 as an example.
# Withstanding Capacity of Machine Guards: Evaluation and Validation by 3D Scanners
## 2 Materials and Methods
### Evaluation of the Ballistic Limit  
The ballistic limit velocity (\(V_{bl}\)) is \"the minimum velocity required by a projectile to completely penetrate a target\". The R&I equation establishes an exponential relationship between the ballistic limit velocity (\(V_{bl}\)), the impact velocity (\(V_{i}\)), and the residual velocity (\(V_{r}\)) of the projectile using two dimensionless parameters (\(a\) and \(p\)):  
\[V_{r}=a(V_{i}^{p}-V_{bl}^{p})^{\frac{1}{p}} \tag{1}\]  
\[a=\frac{m_{p}}{m_{p}+m_{pl}} \tag{2}\]where \(V_{r}\) represents the residual speed after the impact, when the projectile fully penetrates the target, \(p\) is an exponential parameter, \(m_{p}\) is the projectile mass, and \(m_{pl}\) is the plug mass. It is worth to note that \(a\leq 1\); if \(a=1\), the plug is absent.  
The characteristic parameters of the R&I equation can be found using a statistical approach based on a least squares regression of the data collected from at least five tests [28]. Figure 1 shows a typical R&I curve obtained by regression. The calculated ballistic limit is also highlighted.  
The evaluation of the withstanding limit of machine guards performed following this new approach is reliable and effective. In fact, it allows to consider the dispersion of the results of multiple tests in the evaluation of the ballistic limit. For this reason, regression is a good method to determine the mean or best-fit value of the ballistic limit.  
All the tests performed in worldwide laboratories confirmed that, using this approach based on regression, at least five shoots at different initial velocities are sufficient to define \(V_{bl}\) with a very good correlation coefficient, \(R^{2}\), up to 0.99.  
When \(V_{bl}\) is known, it is possible to use this value to set a safe test velocity (\(V_{safe}\)), which allows to perform a single standardized impact test obtaining a reliable result. Nevertheless, according to the conditions of suitability provided by the aforementioned standards, \(V_{bl}\) cannot be considered equal to the safe speed (\(V_{safe}\)), as a fracture could still begin, and small pieces of material can be ejected outside the machine working zone.
## 3 Results and Discussions
### Unconfined Compressive Strength (UCS)  
To determine the optimum curing time and examine the compressive strength of the stabilized specimens, a series of UCS tests for the given percentages of XG-treated soil was carried out. Figure 2a shows the values of UCS for the XG-treated soil with curing time.  
For all XG-treated specimens, the compressive strength of soil significantly increased with an increasing curing time, especially after 7 days of curing. The XG-treated specimens reached their peak soil strength values after 10 days, whereas untreated soil reached its maximum strength after 7 days of curing. The soil compressive strength increased with the increasing content of XG. Under 10 days of curing, the 2% XG-treated specimen shows almost 2.5 times higher UCS than the untreated sample. The increase is attributed to the bonding of soil with the XG additive. As moisture content plays a significant role in soil behavior, the moisture content of each specimen at the end of the UCS test was measured and summarized in Figure 2b. The moisture content of all samples, regardless of the XG percentage, was drastically decreased, and the moisture content of 10 days of cured specimens corresponds to the maximum soil strength in UCS tests. As the water content reduces, the biopolymer converts to a stiff material, which corresponds to an increase in soil strength. A similar relationship between water content and soil strength was also reported in previous studies [8; 25].  
The secant stiffness of the XG-treated specimens is given in Figure 3. Adding XG may enhance soil stiffness. The stiffness of both treated and untreated soil specimens gained with increasing the curing time and after 10 days remained almost unchanged.  
The obtained curing time corresponding to the maximum UCS value was considered the optimum curing and was taken for the following experiments (Figure 4). The results were consistent with earlier studies [25; 26; 27]. [PERSON] et al. [25] examined the effect of XG on the compressive strength of sand, mixture of sand with silt, and high-plasticity clay. They found that adding XG provides the highest strength for the mixture of sand with silt.  
Figure 2: (**a**) Variation of UCS for the XG-treated soil with curing time; (**b**) changes in water content with curing time.
## 2 Materials and Methods
### Design Optimization and Control Tuning
#### 2.2.3 Optimization Algorithm  
The nature of these problems tends to have an underlying competition between objectives, so a multi-objective optimization (MOO) needed to be implemented to find a set of solutions rather than a unique best solution. The PYMOO library [18] was used to implement the MOO algorithms. The MOO algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II) [19] was chosen for the first testing, given its importance in many studies from the literature. A second optimization was implemented with the MOO algorithm Reference Vector Guided Evolutionary Algorithm (RVEA) [20]. This algorithm is mainly used for many-objective problems. The main new contributions in the RVEA lie in the two components: the reference vector-guided selection and the reference vector adaptation. This algorithm has a scaling approach termed the angle-penalized distance (APD), adopted to balance the convergence and diversity of the solutions in the high-dimensional objective space. The algorithm also has an adaptation strategy to dynamically adjust the reference vectors' distribution according to the scale of the objective functions.
###### Abstract
* What is the correlation between communication distance and CPU load at the RSU, and how does it influence edge node performance? This paper provides insights into the performance dynamics of edge computing in 5G-enabled vehicular communication systems while considering the influence of communication distances. By conducting both real-world experiments and simulations, we offer valuable findings on energy consumption patterns and CPU load variations, contributing to the development of efficient and resilient edge computing solutions tailored for connected transportation systems. Our research is conducted within a meticulously crafted experimental framework deployed on a 5G testbed. The experimental setup involves a BMW car equipped with an OBU and an RSU supplemented by a simulation environment powered by the NS-3 network simulator [20]. This dual-pronged approach meticulously captures the intricacies of vehicular communication in both real-world and simulated scenarios. The BMW car, serving as the platform for the OBU, becomes the focal point for in-vehicle communication, embodying the dynamic and diverse nature of vehicular data exchange. Simultaneously, the RSU, functioning as an edge node, plays a pivotal role in the infrastructure, facilitating seamless communication between the vehicle and the broader network. The NS-3 simulation complements the real-world experimentation, providing a controlled environment for detailed technical analysis. Together, these components form a comprehensive ensemble designed to authentically mirror the practical challenges and dynamics of connected transportation systems. This approach ensures the relevance of our study and establishesa solid foundation for technically nuanced insights into the performance of vehicular communication networks. The remainder of this paper is structured as follows. In Section 2, we review related works. Section 3 details the experimental and simulation setups employed in our investigation. Section 4 presents our results and analysis, highlighting energy consumption patterns and CPU load dynamics across different communication distances. In Section 5, we discuss the implications of our findings. Finally, Section 6 concludes the paper, summarizing key insights, contributions, and potential avenues for future research.
## 3 Results
### Growth Characteristics of Kp and Pa in Media with Diesel or Its Constituents as Sole Carbon Source  
We first analyzed the diesel-constituent ranges that the two strains were able to utilize as a sole carbon source. We found that the Pa strain was able to grow (including good-growth and medium-growth) on 86% of diesel constituents on a mass basis, compared to 68% for the Kp strain (Figure 3a,b). Additionally, the Pa strain grew better than the Kp strain on eight diesel constituents (accounting for 75%), while the Kp strain grew better on only seven diesel constituents (accounting for 48%). In terms of alkanes, the Pa strain preferred long-chain constituents (Figure 3b) while the Kp strain preferred short-chain constituents (Figure 3a).  
In the culture medium with C\({}_{21}\) as a sole carbon source, the Pa strain was able to grow while the Kp strain could not. In addition, the Kp strain grew poorly in C\({}_{20}\)- and C\({}_{24}\)-containing medium, while the Pa strain grew well in C\({}_{20}\)-C\({}_{24}\)- as well as in C\({}_{16}\)-containing medium. In diesel medium (Figure 4a), the biomass of the Pa strain was significantly higher than that of the Kp strain in each period. The relative growth rates of the Kp strain and Pa were 0.4 d\({}^{-1}\) and 0.6 d\({}^{-1}\), respectively (Figure 4b). In the decline period, the declining rate of the Pa strain (\(-\)0.03 d\({}^{-1}\)) was lower than that of the Kp strain (\(-\)0.05 d\({}^{-1}\)). In cyclohexane medium, the Pa strain showed a decline with a relative growth rate of \(-\)0.26 d\({}^{-1}\) (Figure 4c,d). In contrast, the Kp strain showed \"S\" type growth with a relative growth rate of 0.8 d\({}^{-1}\). The biomass production of the Kp strain was higher than that of Pa throughout the culture time.  
In summary, the Pa strain grew faster than Kp in diesel medium; however, in cyclohexane medium, Pa was not able to grow while Kp grew well.  
Figure 3: Growth characteristics of Kp (**a**) and Pa (**b**) cultivated with diesel constituents as a sole carbon source. Dark grey, light grey and white areas indicate good-growth (OD600 \(\geq\) 1), medium-growth (0.4 \(<\) OD600 \(<\) 1) and poor-growth (OD600 \(\leq\) 0.4), respectively.
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
## 6 Simulation Result Analysis
### Influence of Different Grouting Pressures
#### 6.2.3 The Change in Regional Porosity  
Under varying grouting pressures, the changes in porosity before and after grouting across five regions (X1, X2, X3, X4, and X5) are depicted in Figure 15.  
The data illustrate that as grouting pressure increases, the porosity reduction in each region generally trends upward. This suggests that higher grouting pressures enhance the polymer's ability to penetrate and fill the pores, thus improving the compactness of the rockfill body. Under a grouting pressure of 0.1 MPa, the X1 area experiences a reduction of 6.7%, while the X2 and X3 areas see a reduction of 6.4%, and the X4 and X5 areas show a reduction of 6%. At a grouting pressure of 0.3 MPa, the reduction values are 7.4% for X1, 7.3% for X2 and X3, and 6.8% for X4 and X5. When the grouting pressure is increased to 0.5 MPa, the reduction values become 8.3% for X1,  
X2 and X3, and 8.7% for X4 and X5. Finally, at a grouting pressure of 0.9 MPa, the reduction values are 10% for X1, 9.5% for X2 and X3, and 9.4% for X4 and X5.  
Region X1, where the grouting hole is located, serves as the focal point for this analysis. From X1 outward, under consistent grouting pressure, the porosity reduction diminishes with increasing distance from the grouting hole. This trend indicates that the uniformity of polymer distribution decreases as the distance from the injection point increases. This phenomenon is attributed to the rising resistance encountered by the polymer foam as it flows, resulting in less slurry reaching areas far from the grouting hole.  
Moreover, the reduction in porosity generally escalates with increasing grouting pressure across all regions. However, at a grouting pressure of 0.9 MPa, the trend of the porosity change in regions X2 and X3 alters, showing a reduced decrease in porosity compared to other pressures. This reduced change suggests that the polymer distribution within these two regions has reached a balance, rendering any further increase in grouting pressure less effective in reducing porosity.
# Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area
## 6 Algorithm for Time-Choices Setting  
In this section, the time-choice process is explained in details. In the first part, a set of variables is defined. Due the large number of variables, only the general ones are explained; the remaining variables are explained further along om the process. In the second part, a set of components is calculated, which are used to capture the temporal constraints of the current activity. Finally, these components are used to select the duration and end-time of the current activity.  
The list of variables is presented as follows:  
* \(x,y\): two activities from the activity-plan.
* \(A\): current activity.
* \(B\): next immediate activity to \(A\) in the activity-plan.
* \(N_{A}\): remaining activities (that follow \(A\) in the activity-plan).
* \(z\): remaining activity \(z\in N_{A}\).
* \(ET_{A}\): earliest end-time of \(A\).
* \(LT_{A}\): latest end-time of \(A\).
* EST: earliest (simulation) time (in this study, it is defined as 05:30).
* \(duration_{x}\): typical duration of \(x\).
* \(Gap_{x}\): gap value of \(x\).
* \(DDist_{x}\): duration distribution of \(x\).
* \(D_{x}\): end-time distribution of \(x\).
* \(\epsilon_{x}\): end-time value from the distribution \(D_{x}\) (\(e_{x}\in D_{x}\)).
* \(e_{x}^{*}\): latest end-time value from the distribution \(D_{x}\).
* \(Ee_{x}^{*}\): earliest end-time value from the distribution \(D_{x}\).
* \(d_{x\to y}\): euclidean distance from \(x\) to \(y\).
* \(mode_{x\to y}\): transport mode used to go from \(x\) to \(y\).
* \(travelSpeed_{m}\): travel speed of the transport mode \(m\).
* \(C_{x\to y}\): activity-set from \(x\) to \(y\).
* \(|C|\): cardinal of \(C\).
* \(C_{First}(C_{0})\): first element of \(C\), \(C_{First}=x\).
* \(C_{Last}(C_{|C|-1})\): last element of \(C\), \(C_{Last}=y\).
* \(DDCof_{x}\): duration delay coefficient of \(x\).
* \(DyDCof_{x}\): dynamic duration delay coefficient of \(x\).
* \(StDCof_{x}\): static duration delay coefficient of \(x\).
* \(TTDelay_{x\to y}\): travel time delay from activity \(x\) to activity \(y\).
* \(TTDCof_{m}\): transport mode \(m\) travel time delay coefficient.  
* \(P\): proportion-time set.
* \(p\): proportion-time, \(p\in P\).
* \(r_{p}\): distribution rate of \(p\).
* \(S_{p}\): selection probability of \(p\).
## 4 Analysis of Numerical Calculation Results
### Influence of Soil-Rock Composite Stratum
#### 4.5.1 Site Frequency  
When the seismic wave propagates in the rock and soil medium, the site response spectral characteristics will change. The spectral characteristics have different effects on the underground structure with different natural vibration periods. Through time history analysis, the acceleration time history curves of the double pipe bottom are obtained. The fast Fourier transform (FFT) is carried out using SeismoSignal software, and the spectrum curves are shown in Figure 11.  
Figure 11: Spectrum curves of the bottom of pipe jacking at different stratum: (**a**) Homogeneous silty clay stratum; (**b**) Soil–rock combination stratum.  
As can be seen from Figure 11: 1. Under seismic action, the low-frequency components of the upper and lower pipe jackings are both gradually amplified and the high-frequency components are weakened, which conforms to the seismic response law of the formation. 2. In the homogeneous silty clay, the superior frequency distribution range of the pipe jacking tunnels is mainly 0.5-2.5 Hz and the influence range of ground motion in the homogeneous soil layer is relatively concentrated. 3. In the soil-rock combination stratum, the superior frequency distribution is in the range 0.5-10 Hz, which is more extensive than the homogeneous silty clay and is more likely to cause underground structural resonance and then cause vibration damage of the pipe jacking with different natural frequencies.
## 3 Applications of IL-Based Gels
### Na-Ion Batteries  
Following the good fortune of Li batteries, a new breakthrough, the rechargeable Na-ion battery, promises a wider commercialization as according to the research community and industrial entities such as Electrochemical Energy Storage Systems (EESS) [90,91]. Future perspectives [92] and suitable materials [90] for Na batteries have also been explained previously. Starting in 2020, several IL-based gel breakthroughs in Na-ion batteries were also developed, one of which was by [PERSON] et al. using polymer-based IL-based gel [69] and [PERSON] et al. using inorganic-based IL-based gel [93]. [PERSON] et al. reported an electrochemical enhancement of high safety IL-based gels using Pyr\({}_{13}\)FSI comprising SiO\({}_{2}\)-based mesoporous sieve (SBA-15) host as an ionic conductor due to its high conductivity, Na bis(trifluoromethane)-salt sodium sulfonymide (NaTFSI) because of its larger anionic radius than other Na salts, and 12 P(VDF-HFP) as a binder due to its high dielectric constant. P(VDF-HFP) increases the mechanical strength of Pyr\({}_{13}\)FSI containing NaTFSI when ground into nanoparticles with short-range ordering mesoporous structures, thereby increasing the storage space in the battery. Figure 5a,b shows the proportion regulation and optimization of components in Na-IL/PVDF-HFP@SBA-15. The ionic conductivities of the IL-based gel electrolytes are displayed in Figure 5c,d. As presented in Figure 4e, with the Na ion battery consisting of Na\({}_{3}\)V\({}_{2}\)(PO\({}_{4}\))\({}_{3}\) (NVP) cathode and a metallic Na anode with Na-IL/P(VDF-HFP)@SBA-15 IL-based gel electrolyte, an initial discharge specific capacity of 110.7 mA h g\({}^{-1}\) was obtained, and the cell retained 92% of its initial capacity after 300 operational cycles at 30\({}^{\circ}\)C. In contrast to solid-state Na ion batteries, [PERSON] et al. demonstrated a planar Na-ion micro-battery using a NaBF\({}_{4}\)-based IL-based gel electrolyte, showing a high ionic conductivity of 8.1 mS cm\({}^{-1}\), with a Na titanate (NTO) anode and a Na vanadate phosphate (NVP) cathode. The cycles of these Na micro-batteries last longer than those reported by [PERSON] et al. and can be discharged and charged to levels of 100 C in just 15-20 s.