esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 3 Results and Discussion
### The Temporal Response of Vegetation Health to Oil Spill Exposure
It can also be observed from the boxplot in Figure 11d that the median \(\Delta\)NDVI is lower for SSs than CSs for all the vegetation types, with the biggest differences observed for sparse vegetation and mangroves. Moreover, the mangrove vegetation has more than 75% of \(\Delta\)NDVI values below zero compared to less than 75% for the sparse and dense vegetation. Furthermore, Figure 11d shows that the \(\Delta\)NDVI values at SSs are highly statistically different (\(p\)-value \(<\) 0.0010) from those at CSs for both sparse and mangrove vegetation and very significant (\(p\)-value \(<\) 0.0100) for dense vegetation. These observations concur with the findings of [PERSON] et al. [31], which found a significant difference with a \(p\)-value of \(<\)0.0050 between the NDVI values derived from the Landsat image of vegetation at spill sites and non-spill sites in mangrove vegetation in the Niger Delta. Also, [PERSON] et al. [14] found a significant difference with a \(p\)-value \(<\) 0.0500 between the LAI, NDWI,  
Figure 11: Relationship between \(\Delta\)NDVI and number of days after a spill for volumes above 225 bbl and control sites for (**a**) dense vegetation, (**b**) sparse vegetation, and (**c**) Mangrove vegetation and (**d**) boxplot summary of \(\Delta\)NDVI for spill sites (SS6) and control sites (CSs) for dense, sparse, and mangrove vegetation with the \(p\)-values from paired \(t\)-test analysis to determine the statistical differences in post-spill \(\Delta\)NDVI for corresponding pairs of SSs and CSs located within dense (\(n\) = 8), sparse (\(n\) = 8) and mangrove vegetation (\(n\) = 6). Levels of significance: \(p\)-value \(<\) 0.0010 (highly significant); \(p\) value \(<\) 0.0100 (very significant); \(p\)-value \(<\) 0.0500 (significant); \(p\)-value \(\geq\) 0.0500 (not significant) Figure 11d. and NDVI values derived from the Sentinel 2 multispectral bands for grassland in the Niger Delta. In summary, the statistical tests confirm that sparse and mangrove vegetation is most responsive to oil exposure in the region. Mangrove tidal wetland habitats are highly vulnerable to large and chronic oil spills [66]. Past oil spill events worldwide demonstrate that mangroves can suffer lethal and sublethal effects when exposed to oil, which can be impacted regardless of the distance from shore if oil reaches their roots and pneumatophores [68]. The presence of polynuclear hydrocarbons in the soil increases the incidence of a mangrove mutation in which chlorophyll is deficient or absent [69]. Also, continuous exposure of mangrove trees to high levels of pollution affects their health and productivity in the long term by imposing permanent stressful conditions [70].
## 3 Results and Discussion
### Adsorption Efficiency of LPD Biomass
#### 3.2.6 Effect of Temperature  
The effect of temperature on the adsorption capacity was investigated at 30, 35, 40, 45, and 50 \({}^{\circ}\)C, keeping the following parameters constant: adsorbate initial concentrations 55 mg/L, adsorbent dosage 0.2 g, adsorption period 2 h, and pH 7. The results depicted in Figure 4d indicate endothermic adsorption mechanism, in which the \(q_{e}\) value increased from 3.2 to 7.15 mg/g when temperature was raised from 30 to 50 \({}^{\circ}\)C [37]. Generally, the solution temperature plays a significant role in adsorption by influencing the equilibrium and rate of adsorption processes. Increasing the temperature enhances the diffusion rate of the adsorbate molecules and decreases the solution viscosity; both effects help ions move into adsorbent surfaces and pores of the biosorbent particles, as previously reported for natural plants [38; 39] and natural bentonite clay [40]. On the other hand, temperature may facilitate breaking down of the physical and hydrogen bonds, reducing the aggregation events, leading to higher exposure of active sites on the adsorbent for Pb\({}^{2+}\) ions and, thus, adsorption increase.
## 4 Discussion
### Power Factor of the IM  
From the power supply network point of view, a priority aspect is the PF of the IM, because it is known that these electric receivers have a significant weight (of about 46%) in the global electric power consumption. Based on the data in Table 3, we have drawn the graphs PF(k,\(\gamma_{\rm VK}\)), in which the k-order of the harmonics is the independent variable and the level of the voltage harmonic \(\gamma_{\rm VK}\) is a parameter (Figure 7).  
As a first remark, PF remains constant at the rated value, \(\rm PF_{n}=0.82\), for a level of the voltage harmonics \(\gamma_{\rm VK}=5\%\), regardless of the order of the harmonics. If the level of the voltage harmonics increases to 10%, the constant character of the PF related to the order of the harmonics is preserved (Figure 7), but the constant value is lower compared to the previous case, at PF = 0.81 (decrease by 1.2% compared to the rated value). At higher values of the voltage harmonics level, \(\gamma_{\rm VK}\geq 15\%\), significant decreases of the PF appear, especially at the lower orders, k \(\in\) {5, 7}, so that the constant appearance returns to the higher orders, k \(\in\) {17, 19}.  
Figure 6: Total harmonic distortion factor of electromagnetic torque \(\rm THD_{TE}(k,\gamma_{\rm VK})\).  
Figure 7: Power factor (PF) variations of the IM in the harmonics, related to the k-order of the voltage harmonics and their level \(\gamma_{\rm VK}\).  
For a weight of the voltage harmonics of \(\gamma_{\rm Vk}\) = 30%, the most significant decreases of the PF are recorded as follows: at the value PF = 0.72 (decrease by 12.2% compared to the rated value), when the order of the voltage harmonic is k = 5; and at PF = 0.75 (decrease by 8.5% compared to the rated value), when the order of the harmonic is k = 7 (Figure 7).
# Structural Performance of R-Type Steel Damper Used in Reinforced Concrete Members
## 2 Experimental Program
### Phase II Test Program
#### 2.2.2 Test Setup and Loading Procedure  
In order to evaluate the applicability of the proposed damper to structural members, an antisymmetric moment due to seismic loads was designed to act on the specimen. Figure 4 shows the test setup of the Phase II test specimen. The antisymmetric moment was implemented using a horizontal holding device. The lateral load was applied to the specimen using an electric actuator with a capacity of 1000 kN that was installed at the mid-height of the specimen. The lateral load actuator was bolted to the loading frame connected to the specimen. In order to simulate the damper resisting the shear force induced by the lateral load caused by the earthquake, a 1000 kN hydraulic actuator connected to the upper part of the load frame was used to prevent axial force from acting on the specimen.  
As shown in Figure 4b, two LVDTs were installed to measure the drift angle of the specimen. Another two LVDTs were installed at the front and rear of the damper to evaluate its behavior. Strain gauges were attached at the position where the maximum strain rate was expected to evaluate the deformation characteristics of the longitudinal reinforcing bars, stirrups, and the proposed dampers. Repetitive loading was performed two times, each with drift angles of 1/400, 1/200, 1/100, 1/75, 1/50, and 1/30.
# The Role of Plant Growth-Promoting Rhizobacteria (PGPR) in Mitigating Plan's Environmental Stresses
## 2 Properties and Potential of Plant Growth-Promoting Rhizobacteria (PGPR)  
Plant growth-promoting bacteria (PGPR) are soil bacteria living in the rhizosphere which, through the secretion of various regulatory molecules, are involved in promoting  
Figure 1: The sophisticated and efficient network of functional interactions created by PGPR to support plants’ health and performances in response to adverse environmental conditions and abiotic stresses. Abbreviations: EPS (exopolysaccharides); ABA (abscisic acid); IAA (indol2-3-acetic acid); HCN (hydrogen cyanide); VOCs (volatile organic compounds); ACC (1-aminocyclopropane-1-carboxylic acid).  
plant growth and development. They can be found associated with the roots (rhizosphere), with the leaves (phylosphere), or within the plant (endosphere). The endophytes (PGPE) are generally the most effective in supporting growth; being inside the plant tissues, they can communicate with the host plant and exert their beneficial effect much more efficiently [46]. Furthermore, the PGPE, protected from the external environment, are much less subject to the soil's frequent chemical-physical biotic and abiotic variations. Endophytic bacteria come from the rhizosphere ecosystem surrounding the roots and penetrate into plant tissues mainly by using natural fissures created in the roots during growth. The exudates and radical metabolites produced by plants represent an essential resource capable of selecting and attracting the most beneficial bacteria [47]. The profitable action of the bacteria associated with plants is mainly expressed by directly promoting the absorption of nutrients through the modulation of the levels of plant hormones. The most important and studied direct mechanisms are nitrogen fixation [48], the solubilization of inorganic phosphate [49], auxins (in particular 3-indole acetic acid, IAA [50]), cytokins, and gibbrellins [51]; the enzyme 1-aminocyclopropane-1-carboxylate (ACC) deaminase [52] and the production of siderophore molecules [53] (Figure 2). Instead, indirect mechanisms are defined as the inhibitory activities when the growth-promoting bacteria hinder the negative effect of pathogenic organisms (biotic stress). Some of the most common indirect mechanisms are the production of hydrogen cyanide, antibiotics, and enzymes capable of attacking and degrading the cell wall of pathogens [54].
# Service for Deploying Digital Twins of QKD Networks
## 5 Validation and Results
### Scenario Preparation  
To provide the reader with an example of how the service is used, a template of both the configuration and inventory YAML files introduced in Section 4.1 and employed in the experiments can be found below.  
In the configuration template that can be seen in Figure 6, the user must specify the version of the service to use (currently there is only one version, the 0.1.0 indicated in the YAML template), the API, and the QKD protocol. Then, the nodes have to be named, indicating their IP addresses and their neighboring nodes. Note that the IP address parameter can be left blank or filled with any value in the case of using the OSM feature of the service.  
The inventory document, of which a template can be found in Figure 7, is written in the form of an Ansible inventory. It collects the necessary parameters for Ansible to connect to the nodes, providing it with the IP address of the node, the target user, and its password. The last parameter named \"py_env\" refers to the directory where Python is installed, needed to download the packages required for emulating the QKD network. Note again that if the user intends to use the OSM function, the IP address parameter, named in the inventory file as \"ansible_hos\", can be left blank.
# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 8 Conclusions  
This study used ANN and regression analysis to predict geotechnical parameters from seismic wave velocity and other geotechnical parameters using a published data. The geotechnical parameters used here are plasticity index, LL, dry dry density, and water content. The aim was to evaluate the use of seismic wave velocity to predict geotechnical parameters so that it can save time and cost by minimizing the number of geotechnical tests.  
From the analysis this can be concluded that seismic wave velocity helps to predict water content and dry density. This validates that seismic wave velocity is sensitive to change in water content and dry density. Combination of other geophysical parameters may increase the accuracy of the prediction model. The study shows that the performance of the prediction models is better when lab and filed data are used separately. However, combining lab and field data resulted in poorer models and requires further investigation. From the comparison of ANN and regression models it is proved that ANN can predict better with more accuracy than traditional regression analysis. This study showed the correlation between seismic wave velocity and water content, dry density for four types of soil using field and lab data. Other geotechnical parameters such as angle of friction, cohesion, saturation, void ratio can be considered for developing correlations. Correlations can also be developed for other soil types.  
[PERSON] was involved in drafting the manuscript, made substantial contributions to the conception, design, analysis and interpretation of data. [PERSON] was involved in the interpretation of data, revising it critically on important intellectual contents and gave final approval of the version to be published. [PERSON] was involved in reviewing analysis of data and gave final approval of the version to be published. All authors have read and agreed to the published version of the manuscript.  
This study was funded by U.S. Department of Agriculture under Non-Assistance Cooperative Agreement 58-6060-6-009. The authors have no relevant financial or non-financial interests to disclose.  
This study uses data from a report on seismic wave velocity (P wave) published by the Engineering Research Institute of Iowa State University, Ames, Iowa [13].  
We thank the U.S. Department of Agriculture under Non-Assistance Cooperative Agreement 58-6060-6-009 for providing financial support. We also thank [PERSON], Department Chair and Professor, Civil Engineering Department, University of Mississippi for the ANN software, used for the analysis of this research.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Materials
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Materials**} \\ \cline{2-6}  & & **Binders** & & **Sands** \\ \hline
**Oxides** & **OPC (\%)** & **RC (\%)** & **FA (\%)** & **Fine (\%)** & **Coarse (\%)** \\ \hline Na\({}_{2}\)O & 0.2 & 0.3 & 1.2 & 0.9 & 0.1 \\ MgO & 1.9 & 0.5 & 1.8 & 0.1 & 0.0 \\ Al\({}_{2}\)O\({}_{3}\) & 5.3 & 3.9 & 24.4 & 7.0 & 3.9 \\ SiO\({}_{2}\) & 19.3 & 47.5 & 57.5 & 87.2 & 92.8 \\ P\({}_{2}\)O\({}_{5}\) & 0.0 & 0.0 & 0.4 & 0.0 & 0.0 \\ SO\({}_{3}\) & 3.1 & 0.4 & 0.8 & 0.0 & 0.0 \\ Cl\({}_{2}\)O\({}_{3}\) & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ K\({}_{2}\)O & 0.5 & 1.2 & 2.7 & 4.0 & 2.4 \\ CaO & 67.1 & 41.2 & 7.1 & 0.2 & 0.2 \\ TiO\({}_{2}\) & 0.0 & 0.0 & 1.0 & 0.1 & 0.1 \\ MnO\({}_{2}\) & 0.0 & 0.0 & 0.1 & 0.0 & 0.0 \\ Fe\({}_{2}\)O\({}_{3}\) & 2.7 & 1.5 & 2.6 & 0.4 & 0.5 \\ CuO & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ ZnO & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ SrO & 0.0 & 0.0 & 0.1 & 0.0 & 0.0 \\ ZrO\({}_{2}\) & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ BaO & 0.0 & 0.0 & 0.2 & 0.0 & 0.0 \\ Cr\({}_{2}\)O\({}_{3}\) & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular} Note: OPC: ordinary Portland cement, RC: recycled cement, FA: fly ash.  
\end{table} 
Table 1: XRF chemical composition of the raw materials. Figure 2 allows for the evaluation of the differentiated structure of these wastes composed of cement paste and aggregates. Furthermore, compared with FA, RC particles show a much more irregular shape.
# Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review
## 5 Polymer-Based Composites
### Graphene@Iron@Polymer Composites
#### 5.1.2 Interpretation of Graphene@Iron@Polymer Based Composites Articles
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**S. No** & **Material** & **Thickness** & **Loading** & **Methods** & **Frequency** & **Shielding** & **Year** & **Reference** \\ \hline S2 & PEDOT:NF5-Fe\({}_{3}\)O\({}_{4}\)-RGO & 3.86 mm & - & Hydrothermal method & 2-18 GHz & \(-61.4\) dB & 2018 & [156] \\ \hline S3 & Fe\({}_{3}\)O\({}_{4}\)/Cr:Cr:Pry & 0.8 mm & 2.8 wt.\% & \multicolumn{4}{c}{1. Hydrothermal method} & \multicolumn{4}{c}{2-8 GHz} & \multicolumn{4}{c}{\(>\)28 dB} & 2019 & [157] \\ \hline
54 & FeCo@RGO@PPy & 2.5 mm & - & \multicolumn{4}{c}{2. In-situ growth} & \multicolumn{4}{c}{2-18 GHz} & \multicolumn{4}{c}{\(-40.7\) dB} & 2017 & [188] \\ \hline  & \multicolumn{2}{c}{ROO-PAN-NiFe\({}_{2}\)O\({}_{4}\)} & 2.4 mm & \multicolumn{4}{c}{\(-49.7\) dB} & \multicolumn{4}{c}{\(-49.7\) dB} & \\ \cline{2-7}
55 & \multicolumn{2}{c}{ROO-PPy-NiFe\({}_{3}\)O\({}_{4}\)} & 1.7 mm & - & \multicolumn{4}{c}{2. Followers method} & \multicolumn{4}{c}{\(-44.8\) dB} & 2016 & [199] \\ \cline{2-7}  & \multicolumn{2}{c}{ROO-PEDP-NiFe\({}_{3}\)O\({}_{4}\)} & 2 mm & \multicolumn{4}{c}{\(-45.4\) dB} & \multicolumn{4}{c}{\(-45.4\) dB} & \multicolumn{4}{c}{\(-45.4\) dB} \\ \cline{2-7}
56 & Graphene/Li\({}_{3}\),32/Zn\({}_{3}\),76/Na\({}_{3}\)O\({}_{7}\)/TMA & 4 mm & - & 3D printing method & 2-18 GHz & \(-46.1\) dB & 2020 & [160] \\ \hline
57 & PMMA/ROC & 2.9 mm & 2.6 vol.\% & Self-assembly technique & 8-12 GHz & 63.2 dB & 2017 & [161] \\ \cline{2-7}  & PMMA/graphene & & & & & & 21 dB & \\ \cline{2-7}
58 & PVC/graphene & 2 mm & 20 wt.\% & Hot compression method & 8-12 GHz & 31 dB & 2019 & [162] \\ \cline{2-7}
59 & Fe\({}_{3}\)O\({}_{4}\)@SIA/C\({}_{4}\)C@PVA & 0.3 mm & - & Solution casting method & 8-12 GHz & 20 dB & 2015 & [163] \\ \hline
60 & PVA/Gr/Fe\({}_{3}\)O\({}_{4}\) & 0.2 mm & 0.08 wt.\% & Hummers method & 8-12 GHz & 40.7 dB & 2020 & [164] \\ \cline{2-7}
61 & GNP/CLF/PEK & - & 9 wt.\% & \multicolumn{4}{c}{Compression} & \multicolumn{4}{c}{8-12 GHz} & 27.1 dB & 2019 & [165] \\ \hline
62 & NiFe\({}_{3}\)O\({}_{4}\)-RGO-Polypropyl\({}_{2}\) & 2 mm & 5 wt.\% & \multicolumn{4}{c}{1. Hummers method} & \multicolumn{4}{c}{6-8 GHz} & 29.4 dB & 2019 & [166] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# A Cooperative MAC Protocol for a M2M Heterogeneous Area Network
## 6 Simulation Model and Results
### Performance Analysis
Use of silent periods does not affect the cluster throughput for two reasons: first, the silent period is very small, a maximum of 10.8 ms/s; secondly, due to the staggered link design, some of the silent duration will overlap with the sleep period of superframes of different links. From simulation results, it was found that the average cluster throughput is 12.4 packets/s and 12.3 packets/s for the no BB and the BB scenarios respectively. Results indicate that the BB signaling improves the overall network  
Figure 12: Packet loss rates due to collisions in heterogeneous and homogeneous networks. throughput and QoS by reducing inter-domain collisions, whereas cluster throughputs from end devices are not affected by the signaling technique.
## 6 Conclusions  
This paper provided a comprehensive overview on machine learning techniques applied for various purposes in IIoT and Smart Production. The domains covered include safety and security, asset localization, quality control, and proactive maintenance. IIoT security and safety is a very important aspect for the Industry 4.0 technology transition; hence, one can find many different application domains for ML techniques. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Typical Machine Learning Techniques** & **References** \\ \hline Fault Detection & KNN, SVM, Decision Tree, & [143; 144; 145; 146; 147; 148; 149; 150; 151] \\  & CNN & \\ \hline Diagnostics & Decision Tree, Random Forest, & [152; 153; 154; 155; 156; 157; 158; 159] \\  & KNN, SVM, CNN, RNN & \\ \hline Prognostics & SVM, Bayesian Networks, & [160; 161; 162; 163; 164; 165; 166; 167; 168; 169] \\  & RNN, CNN, Auto-Encoder, & \\  & LSTM, Gated Recurrent Unit & \\ \hline Manufacturing optimization & Unsupervised learning & [170; 171; 173; 174; 175; 177; 178; 181] \\  & (Regressions, SVM, GAN), & \\  & Reinforcement learning & \\  & (Q-learning, LSTM) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of applications of machine learning techniques in IIoT proactive maintenance. These include the identification of intrusion detection, supporting authentication, realizing privacy leaking, checking data integrity, supporting availability, and offloading security services. Asset localization is a very specific area of smart production, where machine learning has been applied extensively. The application areas for asset localization include learning mapping between measurements and location, predicting non-LOS propagation, and predicting location error. Regarding quality control, visual quality inspection and anomaly detection applications were found to specifically require machine learning approaches. In relation to maintenance, the main application areas include fault detection, diagnostics, prognostics, and some manufacturing optimization applications which were surveyed too. Besides providing a general overview of the applied techniques for the listed application areas, this paper also summarized the related references found for application domains, making it easier for practitioners and researchers alike to find ML-application patterns for their given field. The paper also contains the most important references of public datasets for developing domain specific algorithms and applications (see Table 5). Each main chapter includes a dedicated lessons-learned section to reinforce the main findings about the state-of-the-art and the research gaps of the discussed application area.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 5 Evaluation
### Results
#### 5.2.2 Stop Frequency  
Figure 9 illustrates the stop frequency and benefits under different demand levels and CR using the proposed method. The proposed method demonstrates significant benefits across various demand levels, achieving reductions in average vehicle stops ranging from 14.8% to 75.4%. Optimization benefits of the adaptive control method range from 14.8% to 30.1%, whereas this method achieves benefits ranging from 27.9% to 75.4% with the introduction of CBCVs. Moreover, as the number of CBCVs increases, the benefits of reduced stops become more pronounced. This is because more CBCVs facilitate a smoother passage of vehicles through intersections without stopping. As traffic demand increases, the optimization performance in reducing stops gradually diminishes. This is due to limited optimization opportunities for vehicle trajectory control under higher traffic demands.  
Table 5 displays the stop frequency for three control methods under the unbalanced traffic demand. Compared to fixed-timing control, the proposed method achieves emis  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Traffic Demand** & **Fixed Signal Control Method** & **Adaptive Signal Control Method** & **The Proposed Method** & **Compared to the Fixed Signal Control Control** & **Compared to the Adapted Signal Signal Control Control Method** \\ \hline I & 152.3 & 142.1 & 130.2 & 14.51\% & 8.37\% \\ II & 167.1 & 162.4 & 147.8 & 11.55\% & 8.99\% \\ III & 161.2 & 154.3 & 140.1 & 13.09\% & 9.20\% \\ IV & 169.3 & 161.2 & 154.2 & 8.92\% & 4.34\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average carbon emissions (g) under different transportation demand structures.  
Figure 9: Stop frequency and benefits (%) compared with the baseline.  
sion reduction benefits ranging from 41.58% to 47.19%. Compared to adaptive control, it achieves benefits ranging from 21.74% to 28.05%. These results indicate that the proposed method exhibits more stable performance under non-uniform traffic flows. This stability arises because the lower-level model targets minimizing stopping frequencies, utilizing coordinated optimization with signals to maximize the method's potential in reducing stoppages.