esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Object--General Dynamics F-111 \"Aardvark\"  
To perform comparative analyses between the results obtained in the hydrodynamic tunnel and those obtained using the computational fluid dynamics (CFD) method, the authors chose the geometry of the General Dynamics F-111 \"Aardvark\" combat jet aircraft. This geometry was chosen as the subject of the study due to the full access to its technical documentation necessary for creating a three-dimensional geometric model intended for simulation and experimental research.  
The General Dynamics F-111 \"Aardvark\" aircraft was an American multirole aircraft with variable-sweep wings. It was designed in the 1960s by the General Dynamics company. One of the distinctive features of the aircraft was its variable-sweep wing mechanism, which allowed them to be adjusted in-flight for different flight conditions, from takeoff and landing to high-speed flight. The plane was a multirole design, intended for missions such as ground attack, strategic bombing, interception, and air-to-air combat. It had several variants, including fighter-bomber versions, reconnaissance variants, and specialized air-to-air combat models. The variants differed in terms of armament, equipment, and capabilities. The plane entered service with the United States Air Force and Navy in the 1960s. Its primary role was to engage ground targets during the Vietnam War and the Gulf War [20].  
The geometry of the General Dynamics F-111 \"Aardvark\" aircraft was chosen for comparative analysis because of its specific configuration properties, especially the folding wings. Tests were performed for different angles of attack and also for high values to verify an adequacy between CFD computations in real-scale and hydrodynamic tunnel experiments. Here, both CFD and tunnel tests were performed using the same scale and fluid (water), so the scale influence, as well as flow similarity issues with different fluids, was neglected. Additionally, the same aircraft geometry model was used in these studies, as shown in Figure 1.  
The aircraft model used in comparative tests was created based on a 1:50 scale model using SolidWorks 2022 CAD 3D software to represent the aircraft and its aerodynamic  
Figure 1: The General Dynamics F-111 “Aardvark” CAD model on a 1:50 scale.  
configuration. In this model, certain features such as engine inlet cones, boundary layer separators, and ventral fins were omitted due to their negligible impact on the flow over the upper surface of the wing.
## 1 Introduction
### Degradation Models  
The data collected from the experimental test sites provide information regarding the decrease in retroreflectivity over time, and the results can be used to predict the retroreflectivity value expected at a given time by statistical modeling. Statistical models provide details regarding the service life of pavement markings based on the variables and characteristics included to fit the data.  
Prediction of the end of pavement markings' service life started during the 1990s. The initial approaches considered linear or logarithmic models to predict the retroreflectivity as a function of age and initial \(\rm{R_{L}}\)[12; 13]. The authors evaluated the retroreflectivity data from pavement markings with different marking materials, considering paints, tapes, thermoplastic, and others. Since then, statistical models and analyzed data have evolved to more complex models using machine learning methods [20]. Table 1 presents several studies from the literature that proposed statistical models with the data from experimental test sites, including the main exploratory variables and the materials considered.  
Most of the studies presented in Table 1 predicted retroreflectivity as a function of age and the initial retroreflectivity value. Traffic volume was also frequently included in the models as a significative variable to the degradation rate of retroreflectivity. Furthermore, with the exception of [PERSON] et al. [18], all the researchers evaluated at least two different marking materials. However, [PERSON] et al. [18] evaluated the impact of glass bead variation by comparing the performance of standard and highly reflective beads and comparing their impact on the service life of pavement markings.  
Despite all the studies including more than one material type as a source of variation, none of them included an explanatory variable to describe the influence of distinct materials on the degradation rate or on the retroreflectivity value predicted. The authors adjusted different degradation models to evaluate the retroreflectivity and the degradation rate of pavement markings using different materials.In addition, the authors grouped the data based on the material type and did not present any differentiation regarding variations in the same material type, for example, by manufacturer.
## 1 Introduction
The authors developed a consistent campaign with in-plane and out-of-plane shear tests and concluded that the cavity size and the presence of insulation significantly contributed to shear transfer. [PERSON] et al. [9] analyzed some precast concrete sandwich panels used for exterior cladding. Specimens were experimentally tested with a push-out test, with and without corrugated shear connectors. The investigation of the in-plane shear performance showed a relevant impact of the core material in the structural response. And later, [PERSON] et al. [10] extended the study of the shear flow to the type of connectors. There are other relevant contributions about the shear performance of sandwich insulation panels using other types of tests like [PERSON] et al. [11], [PERSON] at al [12], [PERSON] et al. [13], or [PERSON] et al. [14]. To advance towards the utilization of more sustainable materials compared to synthetic fibers and steel, the present study concentrates on the creation of sandwich panels comprising a vegetal FRCM and expanded polystyrene as insulation. Authors have previously presented other complementary studies about FRCM vegetal fibers (see [PERSON] et al. [15; 16]) and some bending tests for sandwich FRCM solutions (see [PERSON] et al. [16]). For the present work, the innovation lies in the use of vegetal FRCM skins and the introduction of flexible connectors made from vegetal fibers. The connection between layers is a must to have the necessary mechanical properties to develop composite materials that are competent with those commonly used in the construction industry. In this study, sandwich specimens were created using different fabrics (hemp, sisal, and glass) and connectors (hemp, sisal, and steel). These specimens underwent a double-shear test to examine how these fabrics and connectors influence the panel's strength against shear. Additionally, a simplified connector slip-load model was developed and compared to the experimental results.
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## 3 Analysis of Outage Probability and Throughput of NOMA-V2X System  
In this section, the outage probability and throughput of the NOMA-V2X system are investigated as performance measures. Requirements for outage performance and QoS of vehicles can be satisfied in the NOMA-V2X system. The outage probability is examined to check the performance of vehicles over Nakagami-m fading channels. Especially, we analyze NOMA-V2X network by considering multiple antennas at vehicles for different scenarios. Let \(R_{1}\) and \(R_{2}\) denote the predetermined target rates of D\({}_{1}\) and D\({}_{2}\), respectively. In principle, an outage occurs when the achievable rate is smaller than the predetermined target rate.
# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 3 Scenario Generation Methods
### Adaptive Test Scenario Generation Methods
#### 3.2.2 Importance-Sampling-Based Method  
Importance sampling techniques generate test scenarios by modifying the sampling probability distribution [52]. This is used to effectively generate representative test scenarios, especially those corner cases or extreme scenarios that are harder to encounter but are critical to AV system performance. The main purpose of using importance sampling is to increase the efficiency of finding rare or high-risk scenarios in testing, thereby reducing the number of tests required.  
The work in [53] used naturalistic driving environments (NDE) to generate a naturalistic and adversarial driving environment (NADE). First, the Markov decision process (MDP) was used to establish the generation of a NDE model, and then the natural distribution of vehicle maneuvers was calculated from naturalistic driving data, and vehicle maneuver samples were extracted from it to generate the vehicle's driving behavior.Crude Monte Carlo (CMC) and importance sampling were then used to adjust for the natural distribution of vehicle maneuvers in NDE. IS was used to adjust the distribution of a small subset of variables that are important for rare events, while CMC was used for the remaining variables. The reason for this is that AV tests can be regarded as a rare event estimation problem with high-dimensional variables, and CMC handles high-dimensional problems well, while IS handles rare events well. Therefore, combining the two could solve the AV testing problem effectively. For the determination of variables for rare events, the approach used RL to achieve this. It identified principal other vehicles (POVs) by learning the challenges of background vehicle maneuvers for the tested AV, and then used the above-mentioned IS for maneuver distribution adjustment.  
By making sparse but intelligent adjustments to the natural driving environment, this method accelerates the evaluation process by orders of magnitude compared to the prevalent natural driving environment. In addition, the generated environment ensures that the test results of AVs do not deviate from the natural driving environment. This means that the assessment results, such as the rates of the different types of accidents, are statistically accurate and reflect real-world driving scenarios. Nevertheless, the case study used in the article was simplified. The article focused on highway driving and limited actions, which may limit the generalization of the research results to more complex driving scenarios. Therefore, further research and testing in more complex driving environments are needed to fully evaluate the applicability of the proposed method. Additionally, the proposed approach lacked perceptually relevant tests in generated natural and adversarial driving environments, such as evaluating the performance of AVs in different weather conditions, which are crucial for a comprehensive evaluation.
## 3. Results
### Analysis of Shrink Test Results  
Due to space limitations, the linear shrinkage rate of the mixture as a function of time was analyzed for a lime content of 8%, and the changes in the other groups were basically the same.  
Figure 12 shows that the line for shrinkage rate first increased rapidly, then increased slowly with time and finally stabilized, and the final shrinkage rate was between 0.8% and 2.2%. For example, in (c) shrinkage of the K = 90% sample increased rapidly from 0 to 1.9% in the first 10 h and then increased slowly before reaching approximately 2.4% at 35 h, and subsequent shrinkage tended to be flat with time.  
Figure 13 shows that the linear shrinkage rate of the mixture first increased and then decreased with increasing phosphogypsum content. For example, when the phosphogypsum content is 19.44%, the sample line shrinkage of 90% compaction reaches the maximum value of 2.22%; when the content of phosphogypsum increases to 23.62%, the sample line shrinkage of 92% compaction reaches the maximum value of 2.03%; when the phosphogypsum content is 26.14%, the linear shrinkage rate of the 95% compaction sample reaches the maximum value of 1.74%, and the mixture of different compaction degrees reaches the maximum value between lime:phosphogypsum = 1:2-1:3, and then decreasing gradually. When the content of phosphogypsum is low, lime will neutralize the acidity in the mixture, so that the formation of gelatinous substances and the content of ettringe will be reduced, and the change of viscous particles in the mixture is small, so the line shrinkage rate of the mixture increases. When the phosphogypsum content is high, as the phosphogypsum continues to increase, the gelling material, ettringe, and clay particles in the mixture continue to decrease, resulting in a decrease in the linear shrinkage of the mixture.
# Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM
## 6 Conclusions  
1. According to the characteristics of compound dynamic disaster accidents and the principle of hierarchical structure, the compound dynamic disaster evaluation index system is divided into three levels. The first level is the target level, that is, the risk degree of the compound dynamic disasters. The second layer is the criterion layer, which includes three primary indices: geological factors, coal property factors and mining technology factors. The third layer is the indicator layer, that is, sixteen specific secondary indices such as mining depth, fault scale index and residual gas pressure corresponding to each primary index.  
Figure 5: The division of compound dynamic disaster risk area in the second mining area.  
2. The AHP-EWM is used to consider subjective and objective weights for comprehensive weighting, and to select the appropriate membership function of the compound dynamic disaster evaluation index to calculate the corresponding index membership and combine the comprehensive weight and membership matrix to calculate the evaluation results and evaluation grades, forming a new method of compound dynamic disaster risk evaluation.
3. Compound dynamic disaster cannot be evaluated using the single dynamic disaster risk assessment method but must be evaluated using integration. The compound dynamic hazard assessment method based on the AHP-EWM comprehensively considers geological factors, coal property factors (coal impact tendency and gas conditions) and mining technology factors. The weights determined are scientific and reasonable, and the assessment results are highly consistent with the actual situation.
4. Based on the AHP-EWM, the hazard assessment of compound dynamic disaster for the second working face of mine B in the Pingdingshan mining area is II (weak hazard). According to the evaluation results, the mining area adopts the corresponding technology to carry out the compound power disaster prevention and control. No compound dynamic disaster occurred in the mining process of the 32,010 working face, which realized safe production.  
Conceptualization, R.Z.; methodology, R.Z.; investigation, R.Z.; validation, D.T.; formal analysis, X.K.; resources, L.X.; data curation, H.W.; writing--original draft preparation, R.Z.; writing--review and editing, D.T.; project administration, G.W.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51904141) and National Natural Science Foundation of China (No. U1908222).  
Not applicable.  
If you need research data, please communicate with the author.  
The authors declare no conflict of interest.
## 4 Materials and Methods
### RNA Extraction, cDNA Library Construction and Illumina Sequencing  
Total RNA of the _M. canadensis_ samples was extracted using RNAiso Plus (Takara, Dalian, China) according to the manufacturer's instructions. The quality and concentration of RNAs were measured using a ND-2000 UV spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA) and Agilent 2100 (Agilent, Santa Clara, CA, USA). cDNA library construction and Illumina sequencing were performed by Gene Denovo Biotechnology Co. (Guangzhou, China). Briefly, mRNA was enriched from total RNA by Oligo(dT) beads and then fragmented into short fragments. The fragmented mRNA was reverse transcribed into cDNA with random primers and second-strand cDNA was subsequently synthesized. Then the cDNA fragments were purified, end repaired, poly(A) added, and ligated to sequencing adapters. After size selection by agarose gel electrophoresis, the ligation products were PCR amplified and sequenced using Illumina HiSeq(tm) 4000 (Illumina, San Diego, USA).
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 2 Underwater Communication Technology Overview
### Visible Light Communication (VLC)  
The VLC is derived from optical communication. The wavelength of VLC ranges from 450 nm to 550 nm. VLC uses the blue-green spectrum. The distance for communication is up to 100 m. The speed of communication is 500 Mbps, which is very high, i.e., up to 1.5 m. VLC is perfect for one-to-one communication.
###### Abstract  
The spread of infectious diseases, such as COVID-19, presents a significant problem for public health and healthcare systems. Digital technology plays an important role in achieving access to healthcare by enhancing device connectivity and information sharing. This study aimed to develop, implement, and demonstrate a tracking and surveillance system to enhance monitoring for emerging infectious diseases, focusing on COVID-19 patient profiling. The system integrates IoT-based wearable devices, an artificial intelligence (AI) camera for real-time monitoring, and a MySQL database for data management. The program uses Charts.js for data visualization and Longdo Map API for mapping, leveraging Jetson Nano boards, webcams, and Python (Version 3.9). We employed a classification technique to categorize patients into two groups: those with a positive mood and those with a negative mood. For comparing accuracies, we utilized three types of models: multilayer perceptron (MLP), support vector machine (SVM), and random forest. Model validation and evaluation were conducted using Python programming. The results of this study fall into three parts. The first part involved testing the monitoring and surveillance system. It was found that the system could receive information from the wearable device, display the received data in graph form, and notify the medical staff when examining symptoms to consider whether the patient should be taken to the hospital. The second part focused on testing the device, and it was found that it could measure body temperature, heart rate, and blood oxygen levels (SpO\({}_{2}\)) and send those data to the database. The third part involved an AI camera test, and it was found that the most suitable algorithm to analyze the patient's facial expressions was Random Forest. The results show that the system supports hospitals in managing COVID-19 and similar diseases by enabling timely interventions through facial expression analysis.  
artificial intelligence; wearable devices; internet of things; smart health; predictive analytics +
Footnote †: journal: Applied sciences  
Article
## 2 Main Metabolomics Methodologies
### Sample and Extraction Techniques  
Obtaining a broad coverage of the metabolome is difficult, due to a wide range of physico-chemical properties exhibited by small molecules. For this reason, various techniques are used to evaluate the set of metabolites [23]. Once samples are collected, it is recommended to treat them with liquid nitrogen or specific solvents [24; 25; 26]. Since extracts, for example, from marine organisms, contain a high percentage of salt and lipids that interfere with the most common analytical methods used in metabolomics, such as LC-MS and HPLC, they must be eliminated. The most used methods for this purpose are solid-phase extraction with Diaion HP-20 Resins, pre-equilibrated with methanol [27], or C18 and PS-DVB SPE cartridges [28; 29], Sephadex LH-20 with a mobile phase of methanol and dichloromethane (1:1) or C18 SPE cartridges that are highly lipophilic [27]. After sample collection and preparation, separation techniques, such as liquid chromatography (LC) in its high performance (HPLC) or ultra-performance (UPLC) forms, gas chromatography (GC) and detection techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used [23; 24; 25]. Further, acquired data are processed in order to create a numerical matrix, which can be used for statistical or multivariate analysis [25]. Generally,  
Figure 3: Main steps of metabolomics technologies.  
the treatment of Nuclear Magnetic Resonance (NMR) data is simpler than that required for LC-MS data, consisting of phase correction; baseline adjustment; shift adjustment and binning that divides an NMR spectrum into many regions or bins to reduce the effects of pH, composition and ionic strength of sample [30]; fuzzy warping (an algorithm that can be used to establish correspondence between the most intense peaks of the spectra to be aligned) [31]; peak alignment using a genetic algorithm [32]; and normalization [33]. For LC-MS data, different software are available for data handling, some of which are open access, such as OpenMS [34], MZMine 2 [35], XCMS [36] and MS-DIAL [37]. \(t\)-test, analysis of variance, principal component analysis (PCA), and partial least squares (PLS) and orthogonal-PLS (OPLS) analyses are the most used statistical methods in metabolomics studies [38; 39]. In the following paragraphs, the most important separation and detection techniques are summarized.
## 2 Numerical Mean Echo Power Model for SAR Altimetry  
Unlike CAs, which only perform power accumulation in range resolution bins, SAR altimeters transform the echo signal into the range-Doppler domain using the slow-time Fourier transform. The proposed model provides the semi-analytic expression of the mean signal power in each range-Doppler bin. As demonstrated in Figure 1, each range-Doppler bin is associated with two ground areas; the signal power can be expressed as the summation of the scatterers' echo power from the two corresponding areas.  
In this section, we derive a mean echo power model for an SAR altimeter with antenna mis-pointing and vertical movement. The altimetry scenario is introduced in Section 2.1.  
We first derive the echo signal from a single point target in Section 2.2, followed by pulse compression of the signal in Section 2.3 and the along-track FFT of the compressed signal in Section 2.4. Finally, the final numerical echo model of all the scatterers on the surface is provided in Section 2.5.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 2 Materials and Methods  
### FLUXNET Data  
The FLUXNET data set [11; 30] is available at [[http://fluxnet.fluxdata.org/](http://fluxnet.fluxdata.org/)]([http://fluxnet.fluxdata.org/](http://fluxnet.fluxdata.org/)), accessed on 6 November 2021, and it contains several ecosystem fluxes based on the eddy covariance method. The eddy covariance method is currently the standard method used by biometeorologists to measure fluxes of trace gases between ecosystems and the atmosphere. Fluxes are measured by computing the covariance between the vertical velocity and target scalar mixing ratios at each individual node (site). In this paper, data from FLUXNET2015 data sets were selected containing GPP (umol CO\({}_{2}\)/m\({}^{2}\)/s), incident shortwave radiation (SW_IN, W/m\({}^{2}\)), diffuse incident shortwave radiation (SW_DIF), air temperature (TA, \({}^{\circ}\)C), and vapor pressure deficit (VPD, hPa). The FLUXNET GPP was derived from the difference between ecosystem respiration (RECO) and net ecosystem CO\({}_{2}\) change (NEE) measurements. The SW value was used to obtain PAR by multiplying it by a constant (0.48) [31]. Meteorology measurements of TA and VPD were used to calculate the light use efficiency (LUE)-constrained factor based on the EC-LUE model [32]. In total, FLUXNET sites containing diffuse radiation observations representing different vegetation types were selected in this study (Table 1) in order to develop a diffuse and direct APAR-based (DDA) GPP model.