esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Acid- and Alkali-Contaminated Soils  
#### 3.1.1 Atterberg Limits  
The results of the liquid and plastic limits for the soils contaminated with solutions of different pH values are shown in Figure 5. For the alkali-contaminated soil, the liquid limit and plastic limit increased with the increasing pH value. For the acid-contaminated soil, the plastic limit increased with the increasing pH value, while the liquid limit had an opposite trend. The changes in the thickness of the diffuse double layer of clay fraction may describe the mechanism of the effect of different pH values on the Atterberg limits [55; 56; 57]. As the alkaline pH increased, the diffusion layer became thicker, leading to the increase in the liquid and plastic limits of the alkaline-contaminated soils [26; 57]. When the soil was exposed to the acidic pollutant, H\({}^{+}\) ions would probably replace the commonly found  
Figure 4: **(a)** Sampling with cutting ring; **(b)** completed sampling.  
exchangeable cations (i.e., Na\({}^{+}\), Ca\({}^{2+}\), Al\({}^{3+}\), and Fe\({}^{3+}\)) from the diffuse double layer of the clay particles, leading to the increase in the double layer thickness [58,59] and, hence, the increase in the plastic limit. However, the liquid limit of the acidic soil decreased gradually with the decrease in pH value. This may be due to the fact that the free oxides and cemented minerals in the soil were gradually corroded by the sulfuric acid solution, resulting in the formation of a loose soil structure [59,60]. This process brought down the interparticle shearing resistance and had the tendency to decrease the liquid limit [61]. Compared with the increasing trend of the liquid limit caused by the increase in the diffuse double layer thickness, the decreasing trend of the liquid limit caused by the decrease in the interparticle shearing resistance might dominate, which ultimately led to the decrease in the liquid limit of the acid-contaminated soil.
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 4 Experiments and Analysis  
To demonstrate the proposed control strategy for the ARIS system, experiments were carried out on a real prototype vehicle shown in Figure 6. The suspension of vehicle was the double wishbone independent suspension. The power system of vehicle adopted four-in-wheel motor independent drive. The ARIS system was implemented in the prototype vehicle rear axle steering system, and the steering angle of each rear axle wheel was obtained and controlled by the real-time working stroke of the steering actuator. Each wheel steering angle could be indirectly measured by a magnetic encoder installed at the end of the actuator. The magnetic encoder was AS5047P with 12-bit resolution. Considering the measurement noise and the steering mechanical chain, the final actual resolution of steering angle was about 0.02 deg. The main parameters of the prototype vehicle and AIRS system are shown in Table 1.  
The model-based design (MBD) method was adopted to implement the proposed control strategy. The control strategy was modeled in a MATLAB/Simulink software (R2016b, Mathworks, Natick, Mass, USA) environment. The executable C code was generated by the automatic code generation technology, and then compiled and flashed into the embedded microprocessor. The microprocessor adopts stm32f407 (STMicroelectronics, Geneva, CH) based on Cortex-M4 Kernel. The PWM modulation frequency for the Metal Oxide Semiconductor Field Effect Transistor (MOSFET) of actuator motor was set to 20 kHz and the sampling frequency was 200 Hz. Figure 7 shows the control structure in the experiments.  
Figure 5: The block diagram of linear active disturbance rejection control (LADRC) tracking controller for ARIS system.
# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
## 5 Conclusions  
Spectral bands were the most accurate among the tested inputs in identifying soybean cultivars. Artificial neural networks provided the highest accuracy in identifying soybean cultivars. These findings demonstrate that it is possible to distinguish soybean genotypes more accurately using spectral bands using public images (Landsat-8 satellite) as input in the tested machine learning models. This represents an advance in soybean mapping, allowing us to accurately identify the most planted cultivars in a given region. However, it is also necessary that more orbital data be evaluated in the discrimination of plant species, seeking to achieve the absence of clouds, either through data with better spatial resolution (Sentinel-2/MSI) or even via satellite constellations (PlanetScope).  
Conceptualization, R.G., P.E.T., L.P.R.T. and C.A.d.S.J.; methodology, C.A.d.S.J., F.S.R., L.P.R.T, P.E.T. and D.C.S.; formal analysis, M.F.C., F.S.R. and R.G.; investigation, C.A.d.S.J., P.E.T. and L.P.R.T.; writing--original draft preparation, D.C.S., R.G. and M.F.C.; writing--review and editing, P.E.T., C.A.d.S.J., L.P.R.T. and R.G.; supervision, C.A.d.S.J. and P.E.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001, National Council for Research and Development (CNPq)--Grant numbers 303767/2020-0 and 309250/2021-8, and Fundacao de Apoio ao Desenvolvimento do Ensino, Ciencia e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)--numbers 88/2021, and 07/2022, and SIAFEM numbers 30478 and 31333.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001, National Council for Research and Development (CNPq)--Grant numbers 303767/2020-0 and 309250/2021-8, and Fundacao de Apoio ao Desenvolvimento do Ensino, Ciencia e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)--numbers 88/2021, and 07/2022, and SIAFEM numbers 30478 and 31333. We would also like to thank the research laboratories of the State University of Mato Grosso (UNEMAT)--[[https://pesquisa.unemat.br/gaaf/](https://pesquisa.unemat.br/gaaf/)]([https://pesquisa.unemat.br/gaaf/](https://pesquisa.unemat.br/gaaf/)) (accessed on 16 May 2022) and of the Federal University of Mato Grosso do Sul (UFMS).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the study design; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Results and Discussion
This has been used to evaluate how well the Weibull distribution fits a given set of data. Checking the goodness of fit of the data provides valuable information. A good fit of the Weibull distribution to the data indicates that the Weibull distribution is an appropriate model for the data and that the estimated parameters had a good degree of accuracy. On the other hand, if the goodness of fit is poor, this suggests that the Weibull distribution is not a suitable model for the data and that other models should be considered. Therefore, by identifying the beta and alpha parameters and checking the goodness of fit of the Weibull distribution to the data, a responsible officer can make better decisions about the nature of the failure and how to prevent it. The resulting Weibull analyses of DT-failure data are to be uploaded to computerized maintenance-management systems (CMMS) for data-driven decision-making for relevant managers. With this data, corrective (reactive) maintenance work or the frequency of preventive (proactive) maintenance work can be determined for different categories of DTs. In addition, ineffective maintenance-program issues with high failure rates of DTs could easily be identified, and reliability indices (SAIDI, SAIFI, CAIDI, and ENS) of power-distribution systems could be improved with cost optimization. Also, depending on the electricity supply contracts, prioritization of the scarce resources by giving due considerations on \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Beta Value** & **Alpha Value** & **Typical Failure Mode** & **Interpretation of Cause of Failure** \\ \hline \multirow{3}{*}{\(>\)4} & Low compared with standard values for failed parts (less than 20\%) & Old age, rapid wear-out (systematic, regular) & Poor machine/material design \\ \hline \multirow{3}{*}{Between 1 and 4} & Low compared with standard values for failed parts (less than 20\%) & Early wear-out & Poor system design \\ \hline Between 1 and 4 & Low & Early wear-out & Construction problem \\ \hline \multirow{3}{*}{\(<\)1} & Low & Infant mortality & Production problems, design problems, misassembled, quality control, overhaul problems \\ \hline \multirow{3}{*}{Between 1 and 4} & \multirow{3}{*}{Between 1 and 4} & Less than manufacturer-recommended preventive maintenance cycle & Indeequate preventive-maintenance schedule \\ \hline \multirow{3}{*}{Around 1} & \multirow{3}{*}{Much less} & Random failures with definable causes & Indeequate operating procedure \\ \hline \end{tabular}
\end{table} 
Table 4: Interpretation of Weibull CDF analysis data for DT failures [22; 27]. consumer compensations and penalty charges at the event of electricity disruptions can also be determined with this approach.
## 3 Results and Discussion  
### Physico-Chemical Parameters  
Physico-chemical parameters were measured in the frame of the MONITOX monitoring network and can be considered in the Water Framework Directive as supporting elements governing the development of the biological communities [45]. Water Framework Directive [46] establishes five ecological statuses for water bodies: high, good, moderate, poor, and bad, transposed in Romanian legislation with Order no. 161 of 16 February 2006 of the Minister of Environment and Water Management for the approval of the Normative on the classification of surface water quality in order to establish the ecological status of water bodies. In this Romanian Order, limit values corresponding to five quality classes are established for all physico-chemical indicators [47]. Our results (parameter values and analytical errors) are presented in Table S1 (Supplementary Materials), and the water quality classes for each target site are presented in Table 2.  
Ammonium may be found in surface waters as a result of degradation of proteins and organic matter from vegetable and animal waste contained in the sediment, industrial and domestic water discharge [48]. The highest value of 0.45 mg/L for ammonia was obtained in Site 31 (Mangalia) and the lowest of 0.04 mg/L in Site 22 (Musura bay mouth). In accordance with Romanian legislation, from the point of view of ammonium concentrations, water samples have values corresponding to the high ecological status (except Site 31 (Mangalia) with good ecological status). Along the Danube, up to Ceatal Chilia, in terms of nitrite concentrations, the values correspond to the good ecological status, with values between 0.011 and 0.023 mg/L (except from sites 8 (Siret R. upstream) and 9 (Siret R. downstream) with moderate ecological status). In the Danube Delta, water bodies have moderate ecological status, with nitrite concentrations from 0.035 mg/L to 0.050 mg/L. Across the Black Sea coast, the ecological status varies from good (sites 27 (Corbu), 28 (Mamaia), 29 (Constanta) and 32 (Vama Veche)) to moderate (sites 30 (Costinesti) and 31 (Mangalia)) and poor (site 30 (Costinesti)). The presence of nitrates in natural waters can be explained by water contact with the ground watershed or by water discharge from farmland [48]. For nitrate concentrations, expressed in nitrogen, all water bodies are framed in good ecological status (65.6% of
# Ground Simulation Tests in Two-Dimensional Dynamic Acceleration Environment
## 2 Simulation Test Method with Air-Cannon-Started Compound Centrifugal Turntable  
The requirements for the simulation tests are defined as follows. The acceleration process of the unit being tested includes two sequential stages: launch and maneuvering flight. After the system receives the launch order, the axial acceleration of the unit being tested must reach the set value within 0.2 s, and the radial acceleration of the unit being tested must be zero \(g\) during the launching stage. After the system receives the flight order, the radial acceleration of the unit should reach the set values within 0.5 s while the axial acceleration of the unit being tested remains unchanged during the flight stage.  
The block diagram of the air-cannon-started compound centrifugal turntable simulation test system is illustrated in Figure 1. It mainly consists of a console, a speed control system, an electromagnetic clutch, a 2-D acceleration vector distribution system, an impact loading system, and a measurement system. The console is responsible for control and test of the entire system. The speed control system provides a continuous AE. The unit being tested and the vector distributor are installed on the main arm of the centrifuge. The centrifuge is controlled by the speed control system and the electromagnetic clutch. The 2-D acceleration vector distribution system is made up of a vector distribution controller, a driver, and a distributor. With a servo motor and the turntable used as the distributer, the resultant acceleration of the main arm of the centrifuge is distributed to the axial and radial directions of the unit being tested. The impact loading system consists of an impact loading controller, an impact loading driver, and an impact loader. An air cannon is employed as the impact loader to propel the main arm of the centrifuge for instantaneous acceleration. The measurement system measures the 2-D acceleration of the unit being tested and tests the working state of the unit being tested.
## 3 Ionic Liquids (ILs) as Green Extraction Phase  
Ionic liquids (ILs) are non-molecular solvents with melting points below 100 \({}^{\circ}\)C, negligible vapor pressure at room temperature, high thermal stability and variable viscosity. The ILs' miscibility in water and organic solvents can be controlled by selecting the cation or anion combination or by the addition of certain functional groups in the IL molecule. Most often, ILs are composed of large asymmetric organic cations and inorganic or organic anions. The most usually employed IL anions are polyatomic inorganic species, such as PF6- and BF4-, and the most relevant cations are a pyridinium and imidazolium ring with one or more alkyl groups attached to the nitrogen or carbon atoms [30]. ILs have been successfully applied to the liquid phase microextraction technique (LPME) as a less toxic alternative to conventional organic solvents. Considering these most notable properties, the potential usage of ILs as the extraction phase for LPME and applications has already been extensively reviewed by different authors [31; 32; 33; 34; 35]. The successful use of ILs in extraction phases is related to their structure. In addition to the common interactions existing in conventional solvents, ILs also have ionic interactions which confer miscibility when dissolved in polar substances. At the same time, the presence of alkyl chains in the cation determines the solubility in less polar substances. A review by [PERSON] and coworkers in 2012 presents the physical properties of some of the most commonly used ILs [32].  
In 2003, [PERSON] et al. [36] reported the first application of ILs in the extraction phase in single drop microextraction (SDME). SDME is a simple, easy-to-operate and reliable LPME-based method developed in the 1990s [8]. In this report, IL-based SDME coupled with HPLC was applied for the preconcentration and analysis of polycyclic aromatic hydrocarbons (PAHs) using the IL 1-octyl-3-methylimidazolium PF6 [C8\({}_{5}\)L1M-PF6] as the extraction solvent. Compared with 1-octanol, ILs provided higher enrichment factors (EFs), enabling the use of extended extraction times and larger drop volumes. In 2015, [PERSON] et al. reviewed the analytical potential of ILs in SDME [37].  
One of the most relevant applications of ionic liquids concerns their use as the extraction phase in dispersive liquid-liquid microextraction (DLLME). DLLME is a powerful extraction technique in which microliter volumes of an extraction solvent are dispersed in the sample to extract and preconcentrate the analytes [7]. The tunable properties of ILs have made these solvents particularly attractive for DLLME applications. [PERSON] et al. [38] reviewed in 2013 the use of ILs in the different types of DLLME and [PERSON] et al. [39] recently reviewed modern approaches for IL-DLLME. In a recent application, IL-DLLME was used for the first time in the extraction phase for cortisone and cortisol determination from human saliva samples by HPLC-UV. The method provided high selectivity and EFs to achieve biological levels [40].
# Routing Protocols Performance on 6 LoWPAN IoT Networks
## 2 Related Work
### Ad Hoc On-Demand Distance Vector (AODV)  
AODV is a robust and reactive routing protocol which is designed primarily for mobile ad hoc networks, originating from the work in 2003 [22]. AODV operates on-demand, activating its routing algorithm in response to specific demands within the network. The AODV routing algorithm unfolds in two distinct phases which are the control phase and the route discovery phase.  
A key feature of the protocol is its utilization of a hop count-based route selection process, aiming to merge routes for efficiency and promote high bandwidth usage while minimizing data drop. However, the protocol remains cognizant of factors such as link reliability and node energy levels to ensure effective and reliable routing. AODV defines three different routing messages which function as control packets and play a crucial role in the routing process with details subsequent sections [23]. The AODV protocol executes its operations through Route Requests (RREQs), Route Replies (RREPs), Route Errors (RERs), and HELLO messages. The HELLO message, a special case of the RREP message with TTL (Time-To-Live) set to 1, may provide local connectivity information [24]. This communication mechanism ensures the creation of linked paths between nodes in the network only when specific clusters' source nodes send arbitrary request signals.  
Consequently, the AODV routing protocol relies on an on-demand nature, promoting a congestion-free system when communication is established between interconnected nodes [22]. AODV meticulously monitors and maintains each path in the network until they are acquired by the sources. Since its conceptualization in the Internet Engineering Task Force (IETF), AODV has undergone significant evolution, incorporating various improvements. The proposed variants of AODV are categorized into quality, multipath, energy, security, and routing strategy, demonstrating the protocol's adaptability and continuous refinement [24].
## Appendix B Numerical Derivatives of e-HAH Model  
In the previous study by [PERSON] and [PERSON] [57], the central finite difference method was adopted for numerical derivatives because of its faster convergence and lower error. Therefore, in this study the same finite difference was employed. For the step size decision for numerical differentiation, the scaled stress space idea was used [60]. For the sake of simplicity, the Voigt notation under the plane stress condition was used. The step sizes for each state variables were defined based on common step size as follows.  
\[\begin{array}{l}\delta\sigma_{\rm i}=\sqrt{\sigma_{ab}\sigma_{ab}}\times \text{a}\\ \delta\hat{h}^{s}_{\rm i}=\sqrt{\hat{h}^{s}_{\rm\,\,ab}\hat{h}^{s}_{ab}}\times \text{a}\\ \delta g_{\rm m}=g_{\rm m}\times\text{a},\,\,(\,\text{m}=1\,\sim 4,\,\text{L},\, \text{S},\,\text{R})\\ \delta\overline{\varepsilon}=\overline{\varepsilon}\times\text{a}\end{array}. \tag{116}\]  
In this study, \(\text{a}=1.0\times 10^{-6}\) was employed for the calculation. Examples of the first and second derivatives with respect to stress are shown as follows.  
\[\frac{\partial\Phi}{\partial\sigma_{3}}\approx\frac{\Phi(\sigma_{1},\,\sigma _{2},\sigma_{3}+\delta\sigma_{3})-\[PERSON](\sigma_{1},\,\sigma_{2},\sigma_{3}- \delta\sigma_{3})}{2\delta\sigma_{3}}. \tag{117}\]  
\[\frac{\partial^{2}\Phi}{\partial\sigma_{3}\partial\sigma_{3}}\approx\frac{ \[PERSON](\sigma_{1},\,\sigma_{2},\sigma_{3}+\delta\sigma_{3})-2\[PERSON](\sigma_{1},\, \sigma_{2},\sigma_{3})+\Phi(\sigma_{1},\,\sigma_{2},\sigma_{3}-\delta\sigma_{3 })}{(\delta\sigma_{3})^{2}}. \tag{118}\]  
\[\frac{\partial^{2}\Phi}{\partial\sigma_{1}\partial\sigma_{3}}\approx\frac{1}{4 \delta\sigma_{1}\delta\sigma_{3}}\bigg{[}\begin{array}{c}\[PERSON](\sigma_{1}+ \delta\sigma_{1},\,\sigma_{2},\sigma_{3}+\delta\sigma_{3})-\[PERSON](\sigma_{1}- \delta\sigma_{1},\,\sigma_{2},\,\sigma_{3}+\delta\sigma_{3})\\ -\[PERSON](\sigma_{1}+\delta\sigma_{1},\,\sigma_{2},\sigma_{3}-\delta\sigma_{3})+ \Phi(\sigma_{1}-\delta\sigma_{1},\,\sigma_{2},\sigma_{3}-\delta\sigma_{3}) \end{array}\bigg{]}. \tag{119}\]  
Similarly, all other derivatives associated to the e-HAH can be calculated. There is a remark for using numerical derivatives with respect to \(\text{g}_{\rm i}(i=1\,\sim 4,\,\text{L},\,\text{S},\,\,\text{R})\). g values have admissible range of value, e.g., \(0<\text{g}_{1}\leq 1\). When g\({}_{1}\) equals 0 or 1, the numerical derivatives cannot be defined. In this case, the forward or backward difference method can be applied.
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 5 Simulation Results
### DoA Estimation Performance  
Our objective in this section is to assess the accuracy of DoA estimation for radar operation. A target is assumed to be located in the far field region at a distance of \(\theta_{r}=0^{\circ}\). It is assumed that the target reflection coefficients remain constant from pulse to pulse during the radar pulse period but will change pulse-to-pulse as they are drawn from a normal distribution. The number of radar receiver array elements is set to \(M_{R}=10\). The number of pulses used was \(N=100\), and \(100\) snapshots per pulse were used at the radar receiver to build the data covariance matrix. Bartlett beam-forming is used to estimate the DoA for all methods tested. Throughout the different scenarios, i.e., communication in the main and side lobes, no performance degradation was observed in terms of radar operation and DoA estimation. The results are presented in the form of power vs. SNR, as shown in the Figure 13.  
Figure 11: The information decoding at the communication receiver after filtering.  
Figure 12: The performance comparison against the intercepts at the communication receiver at \(\theta_{c}=-50^{\circ}\).