esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The goal of this research was to develop a robust and cost-effective method for rapid forest \(AGB\) evaluation. Initially, ground \(FVC\) using a fraction of land cover by trees and image \(FVC\) using vegetation index from the optical remote sensing data were computed. Next, the ground forest \(AGB\) was derived from the ground data (tree density, tree height, and tree \(DBH\)) using an empirical model [27]. Subsequently, a relational model was developed between ground forest \(AGB\) and remote sensing \(FVC\) by blending this information by using a linear curve fitting approach.  
To estimate the forest \(AGB\), the devised model was applied to the \(FVC\) image derived from the optical remote sensing image. The effectiveness of the approach developed was assessed by utilizing Sentinel-2 imagery as test optical remote sensing data. The Tundi reserved forest area, located in Jharkhand state of eastern India, was selected as a test site for this study. The results obtained by the developed approach demonstrated excellent matching with the results achieved by ground-based estimates. The application of the developed method in Tundi reserved forest revealed that the test site contains forest \(AGB\) on an average of 463 tons per hectare. The result obtained by the proposed approach matches well with the result reported in previous studies from the study area. The result demonstrated more than a 90% match with the ground measurement.  
This study still has a future improvement scope, which will employ topographic information in the developed model.  
All authors have made significant contributions to the manuscript. Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON], [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON], [PERSON], and [PERSON]; validation, [PERSON], [PERSON], [PERSON] and [PERSON] and Hau curation, [PERSON], [PERSON], [PERSON] and [PERSON]; funding acquisition, [PERSON], [PERSON], [PERSON] and [PERSON]; supervision, [PERSON], [PERSON], [PERSON] and [PERSON]; project administration, [PERSON], [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
There is no external funding available for this research work.  
Not applicable.  
**Data Availability Statement**: Field based primary data have been used. The satellite data used for this study is freely provided by the 'European Space Agency' (ESA), Copernicus Sentinel-2 archived data, which has been duly acknowledged.  
The authors would like to thank the 'European Space Agency' for providing freely the Copernicus Sentinel-2 data used in this study.  
The authors declare no conflict of interest.
###### Abstract  
The effectiveness of sprayed and aerosolised pH-neutral electrochemically activated solutions (ECAS) containing 150 mg/L of free available chlorine in reducing total bacteria load and artificially inoculated _Salmonella enterica_ serotype Enteritidis 11 RX on eggs surfaces was investigated. Treatment groups included untreated control, sodium hypochlorite (positive control), sprayed and aerosolised water and sprayed and aerosolised ECAS. Sprayed ECAS (150 mg/L, 45 s) showed a significant reduction in total bacterial load (2.2 log reduction, \(p<0.0001\)) and S. Enteritidis (5.4 log reduction, \(p<0.0001\)) when compared with the untreated control. Aerosolised ECAS (120 s) was effective in reducing both the total bacterial load (1.4 log reduction, \(p<0.01\)) and S. Enteritidis (4.2 log reduction, \(p=0.0022\)). However, aerosolised ECAS (60 s) only significantly reduced S. Enteritidis counts (2.8 log reduction, \(p<0.0008\)), indicating that a longer time for bacterial reduction during fogging sanitisation is needed. Tests performed with one egg per oscillating ray were more effective in reducing both the total bacterial load and the S. Enteritidis counts than those with three eggs per oscillating ray. Sprayed ECAS (45 s) and aerosolised ECAS (120 s) did not deteriorate the egg cuticle integrity (\(\Delta E_{\text{ab}}\) *), which was evaluated using Cuticle Blue dye solution and colour intensity measurement. Overall, both the reduction in total bacteria counts and S. Enteritidis from the egg surface and retention of cuticle integrity suggest that sprayed and aerosolised ECAS could be used as alternative sanitising approaches to improve the food safety aspect of table eggs.  
pH-neutral electrochemically activated solution; total bacterial count; _Salmonella_ Enteritidis; egg cuticle integrity
## 4 Results and Discussion
### Entropy  
Figures 11 and 12 and Table 6 illustrate the diversity as measured using entropy with 20 bins. From the investigations carried out, no discernible difference was seen between \(\hat{\mathrm{E}}_{10}\), \(\hat{\mathrm{E}}_{20}\), \(\hat{\mathrm{E}}_{50}\) and \(\hat{\mathrm{E}}_{100}\). As such, the discussion is carried out using the swarm diversity measures, \(\hat{\mathrm{E}}_{20}\) and \(\hat{\mathrm{m}}\hat{\mathrm{E}}_{20}\  
Figure 11: Quantification of diversity using the Entropy measure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Function** & \begin{tabular}{c} **Mean** \\ **(\(\hat{\mathbf{E}}_{20}\))** \\ \end{tabular} & \begin{tabular}{c} **Stdev** \\ **(\(\hat{\mathbf{E}}_{20}\))** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **(\(\hat{\mathbf{E}}_{20}\))** \\ \end{tabular} & \begin{tabular}{c} **Stdev** \\ **(\(\hat{\mathbf{m}}\hat{\mathbf{E}}_{20}\))** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **(\(\hat{\mathbf{m}}\hat{\mathbf{E}}_{20}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Stdev** \\ **(\(\hat{\mathbf{m}}\hat{\mathbf{E}}_{20}\))** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **(\(\hat{\mathbf{m}}\hat{\mathbf{E}}_{20}\))** \\ \end{tabular} \\ \hline \(f_{1}\) & \(  
To calculate the standard entropy measure using \(\hat{\text{E}}_{20}\), the values of each dimension of particle positions are placed in 20 bins. The computation involves sampling in those 20 bins in each dimension. For the work reported here, there were 100 particles and, apart from the Inverted Rosenbrock function which was investigated in four dimensions, all of the other problems were investigated in two dimensions. Since the nature of niching algorithms is to spread particles across the search space in an attempt to locate multiple optima, it was expected that sampling across the four and two dimensions in 20 bins would result in high diversity. This diversity would decrease gradually as particles started to converge towards optima but would still be maintained at high levels. As such, the results shown in Figures 11 and 12 mirror what was expected.  
For the m\(\hat{\text{E}}_{20}\), particle positions in the mentioned dimensions are also placed in 20 bins. However, since particles in a niche are likely to have converged, or are very close to each other, the diversity is expected to be low and tends to zero as particles converge towards the optima. Results shown are thus as expected.  
In the case of n\(\hat{\text{E}}_{20}\), the computation is similar to the standard \(\hat{\text{E}}_{20}\) with the exception that only unique solutions are used. As such, a diversity measure using n\(\hat{\text{E}}_{20}\) will be high, but not as high as that obtained using \(\hat{\text{E}}_{20}\). The obtained results correctly predict this outcome.  
Various research [12; 13; 14; 15; 16; 17] has shown that entropy is a suitable measure to quantify dispersion. The work presented here postulates that entropy is a suitable measure for quantifying dispersion for solutions and candidate solutions of niching algorithms.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 2 Traditional Methods before Deep Learning
### K-Means  
In traditional image segmentation, the K-means algorithm is commonly used to cluster image pixels into several categories and facilitate subsequent segmentation. In image segmentation, the K-means algorithm needs to divide image pixels into K clusters, each of which has similar color or texture features. It is a simple and effective algorithm that can cluster a large amount of unlabeled data, so it has been widely used in image segmentation. However, the K-means algorithm also has disadvantages. First, the K-means algorithm requires the manual setting of the clustering number K, and different clustering numbers result in different segmentation results. Second, the K-means algorithm is sensitive to noisy data and outliers, which may lead to inaccurate segmentation results. Lastly, the K-means algorithm can only generate block segmentation results and cannot handle images with complex textures and edges. By contrast, deep learning uses deep CNNs to extract features from images. Through the analysis and learning of these features, the method can automatically obtain image segmentation results and handle images with complex textures and edges, thus obtaining accurate segmentation results.
# Feasibility Study of a Gripper with Thermally Controlled Stiffness of Compliant Jaws
## 3 Feasibility Study
### Further Discussions  
Although the feasibility of designing a gripper with thermally controlled stiffness of compliant jaws has been validated, there are some issues, exposed below. Firstly, the heating coil may be not an optimal heat dissipating method, with low-repeatability of coil windings and temperature control. In the future, an improved heating method for the beams should be investigated. A possible approach is to evaporate the thin-layer of electrically-conducting material on beams, which dissipates heat to heat up the beams by voltage control. Secondly, the relation between the applied voltage and the desired thermal change remains unclear. An accurate voltage control circuit in terms of accurate temperature change (corresponding to a change of internal compressive axial force) is to be investigated. Thirdly, the proposed design can only reduce the risk of breaking materials (accommodating actuation inaccuracy) by changing the stiffness of the jaw mechanism, while exactly knowing/controlling the gripping forces on the level of mN, etc. is impossible in current design. In order to address the third issue, we can bond a sensitive pressure sensor on the gripping surface to sense the small gripping force.  
It should be noted that, compared to electrothermally-activated grippers [4; 5; 6; 7], thermal control in this paper cannot result in any motion of two jaws due to the symmetry of the jaw mechanism; additionally, the jaws always have parallel motion, regardless of the presence or absence of imposing heat, which can eliminate the slippery force on the object.
# Effect of ElectroSpark Process Parameters on the WE43 Magnesium Alloy Deposition Quality
## 4 Conclusions  
This paper was devoted to study the effects of the Energy Input values on the deposition quality of WE43 magnesium alloy on homologue substrate by ElectroSpark technology. Based on the analysis of the results, the following conclusions can be drawn:  
1. The deposits' microstructure was very fine grained due to the rapid solidification process. The XRD patterns of the coating and starting material are similar.
2. The coatings' thickness increases significantly with the spark pulse-energy.
3. In the coating/substrate interface of WE43 alloy deposit a partial dissolution of the eutectic structures has been observed. In addition, the substrate micro hardness close to the coating interface does not change with process parameters, indicating that the heat input of the process does not alter significantly the substrate microstructure.
4. The deposits cross-section exhibit several discontinuities that occur as voids. With increasing of the heat input the percentage of defectiveness was reduced. Particularly with increasing of the spark energy the laminar shape defects are reduced.  
Figure 12: Energy density (E\({}_{\text{s}}\)) vs. thickness for ESD deposits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **s1** & **s2** & **s3** & **s4** & **s5** \\ \hline HV of deposits & \(49\pm 3.5\) & \(58\pm 5\) & \(60.1\pm 4.6\) & \(60\pm 5\) & \(59.5\pm 3.2\) \\ HV at 100 \(\upmu\)m far from the interface & \(75\pm 2\) & \(73.5\pm 1\) & \(73\pm 1\) & \(72\pm 3\) & \(74.3\pm 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average hardness value (HV300/15) of deposit and of substrate at 100 \(\upmu\)m far from the interface substrate/deposit.  
The average micro-hardness values of the deposits are lower than that of the substrate and distributed in a small range (49-60 HV). The coatings micro-hardness values reflect the trend of the defectiveness.  
Conceptualization, G.R. and P.L.; Methodology, G.R. and P.L.; Investigation, G.R.; Data curation, G.R.; Writing--original draft preparation, G.R. and P.L.; Writing--review and editing, G.R. and P.L.; Visualization, G.R., P.L. and C.C.; Supervision C.C.  
This research received no external funding.  
The authors declare that they have no conflicts of interest.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 4 5G Support for Industrial IoT Applications Utilizing Robotics
### Tactile Internet  
The core concept of tactile internet is that it allows performing operations and manipulation of physical objects over distance, including fine-grained physical interactions. Recent research works [32; 33] discuss novel applications with which an underlying technology, such as tactile internet, will change health care, education, manufacturing, smart grids, etc. Tactile internet will let humans control real and virtual objects in a wireless manner. The key challenge in tactile internet realization is that it has to adapt to human reaction times. Reaction times of approximately 100, 10, and 1 ms is required for auditory, visual, and manual interaction, respectively. One can easily see that the crucial requirements for tactile internet are ultra-responsive and ultra-reliable network connectivity for achieving acceptable user experience.  
An overview of the 5G communication features and the 5G capabilities to address tactile internet services and haptic interactions and communication is provided within [34]. These include a 5G system architecture that builds on a flexible software-based network design based on a distributed cloud infrastructure; also, a 5G wireless communication design toward reliable and low latency communication. Finally the paper describes the first trials and experiments the authors made with 5G-enabled tactile internet services.  
An interesting use-case of tactile internet is telesurgery; the paper [35] introduces a telesurgery robot based on the 5G tactile Internet and artificial intelligence technology. It explains the architecture, composition, characteristics, and advantages of telesurgery. Then, it presents optimization schemes based of 5G technology: edge-cloud integration (Section 5), network slice (Section 3.3), and AI in edge-cloud. Finally, the paper discusses the open issues of the presented telesurgery system regarding the ultra-high reliability, AI-enabled surgery robot, communication, and security.
# Low-Grade Clay as an Alkali-Activated Material
## 4 Results and Discussion
### XRD Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Phase (\%)**} & \multicolumn{4}{c}{**Raw Clay**} & \multicolumn{4}{c}{**Mortar (10\% Dosage)**} & \multicolumn{4}{c}{**Mortar (10\% Dosage)**} \\ \cline{2-10}  & \multicolumn{1}{c}{**Natural**} & \multicolumn{1}{c}{**Pre-treated at**} & \multicolumn{1}{c}{**Pre-treated at**} & \multicolumn{1}{c}{**Natural**} & \multicolumn{1}{c}{**Pre-treated at**} & \multicolumn{1}{c}{**Pre-treated at**} & \multicolumn{1}{c}{**Natural**} & \multicolumn{1}{c}{**Pre-treated at**} & \multicolumn{1}{c}{**Pre-treated at**} \\  & \multicolumn{1}{c}{**120 ° C**} & \multicolumn{1}{c}{**750 ° C**} & \multicolumn{1}{c}{**Natural**} & \multicolumn{1}{c}{**120 ° C**} & \multicolumn{1}{c}{**750 ° C**} & \multicolumn{1}{c}{**Natural**} & \multicolumn{1}{c}{**120 ° C**} & \multicolumn{1}{c}{**750 ° C**} \\ \hline Illite & & & & & & & & & \\ K\({}_{3}\)Al\({}_{3}\)Si\({}_{0}\)Cl\({}_{0}\) (OH) & 19.6 & 6.3 & - & 2.9 & 4.4 & 7.3 & 4.3 & 2.4 & 3.1 \\ Kaolinite & 21.3 & 8.8 & - & 1.0 & 2.6 & - & 0.5 & - & - \\ Quartz & & & & & & & & & \\ SiO\({}_{2}\) & 33.7 & 55.7 & 45.3 & 82.3 & 68.1 & 70.7 & 79.0 & 79.0 & 76.1 \\ Ratile & 1.3 & 2.4 & - & - & - & - & - & - & - \\ TiO\({}_{2}\) & & & & & & & & & \\ Chlorite & 1.1 & 2.9 & - & - & - & - & - & - & - \\ Mg\({}_{3}\)SiO (OH) & & & & & & & & & \\ Monometallic & & & & & & & & & \\ (Na,Ca)\({}_{33}\)(Al,Mg)\({}_{3}\)(Si\({}_{0}\)) (OH) & + & & & & & & & \\ Alite & 0.5 & - & - & 2.5 & - & 3.3 & 1.9 & - & 3.6 \\ Coattle & & & & & & & & & \\ FROOH & 0.6 & - & - & - & - & - & - & - & - \\ Musovic & - & - & 31.7 & - & - & - & - & - & - \\ K\({}_{3}\)SiO\({}_{0}\)\({}_{1}\) & & & & & & & & & \\ Scallate & - & - & - & - & 0.4 & 0.7 & 1.9 & 2.9 & 1.5 \\ Na\({}_{3}\)Al\({}_{3}\)Si\({}_{0}\)Cl\({}_{2}\) & & & & & & & & \\ NaAlAl\({}_{3}\)Cl\({}_{0}\) & + & & & & & & & \\ Amorphous & 21.6 & 23.8 & 23.1 & 10.1 & 20.3 & 18.0 & 12.3 & 15.3 & 15.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mineralogical composition of clay and alkali-activated mortar at 28 days (AM 1.0, dosages of 10% and 15%).
## 3 Results and Discussion
### Icp-Oes  
The content of Pd(II) in the prepared PdAS(x)-MA resins was then determined by atomic ICP-OES and the results are summarized in Table 2. Our analysis revealed that the real PdAS content in the resins decreased from \(\sim\)100% incorporation for PdAS(1)-MA to \(\sim\)50% in the case of PdAS(10)-MA. We note that during the suspension polymerization process, the various reagents (co-monomers, DVB, MA, PdAS MCM, and AIBN) were dissolved in toluene and dispersed in a water-NaCl-HEC mixture to produce  
Figure 4: CP/MAS \({}^{13}\)C NMR spectra of PdAS(x)-MA catalysts.  
Figure 5: DRS UV–vis spectra for the PdAS(x)-MA catalysts.  
drops of the co-monomer solution (dispersed phase) suspended in the aqueous dissolution (continuous phase), where radical polymerization results in the formation of the desired resins beads [35]. When polymerization takes place in these drops, an increased PdAS loading in the dispersed phase promotes the formation of highly cross-linked resins, as determined by characterization of the N\({}_{2}\) isotherms. This result suggests that limitations in the diffusion of PdAS MCM during the suspension polymerization process could be responsible for the decreased palladium content in PdAS(x)-MA resins where x \(>\) 1.
# Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes
## 2 Materials and Methods
### Spectral Measurement and NDVI Calculations  
The spectral data of the rice canopy was collected by using SpectraPen SP-100 (range 640-1050 nm, 2 nm scan-range, Photon Systems Instruments, Czech), which is a non-imaging, handheld hyperspectral sensor. The data collection period was between 10 DAT (days after transplanting) and 80 DAT. The acquisition time was between 10:00 a.m. and 2:00 p.m., and the sensor was held horizontally at nadir view in the position about 3-5 cm above the highest leaf of the plants. The integration time of spectrum collection was set to auto-sensitivity to minimize the interference of sunlight intensity variability. The collected spectrum data were used to calculate NDVI-based VIs, which can be expressed as follows:  
\[\text{NDVI-based VIs}= \frac{\lambda_{\text{a}}-\lambda_{\text{b}}}{\lambda_{\text{a}}+ \lambda_{\text{b}}} \tag{1}\]  
where the \(\lambda_{\text{a}}\) and \(\lambda_{\text{b}}\) respectively denote the reflectance of near-infrared (NIR) and red wavelengths.
# Estimation of Sedimentary Rock Porosity Using a Digital Image Analysis
## 2 Materials and Methods  
### Rocks Used  
Core samples of fresh sandstone, siltstone, and coal were collected from depths of 400-500 m in southeast Shanxi Province, China. All three sedimentary rocks were part of the Lower Permian Shanxi Formation. It is well known that the rock structure, which determines the physical properties of rock, may vary depending on the geological origin and level of weathering [32]. Deep underground rocks are typically less affected by external weathering factors, and for this reason, they may remain fresh under greater pressures which cause the development of mineral grains that have a more pronounced effect on the engineering properties [33].  
Block specimens of 1 cm\({}^{3}\) and specimens of flake shapes were utilised for MIP testing and SEM analysis. The mineralogical composition and density of each rock are given in Table 2. The coal rock was much lighter than the sandstone and siltstone, with an average density of 1.3 g/cm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Rock/Soil**} & \multicolumn{2}{c}{**Porosity, \%**} & \multirow{2}{*}{**Ratio,**} & \multirow{2}{*}{**References**} \\ \cline{2-2} \cline{4-5}  & **Experiment, n\({}_{\text{e}}\)** & & & **Digital Analysis, n\({}_{\text{d}}\)** & **n\({}_{\text{e}}\)/n\({}_{\text{d}}\)** & \\ \hline Mudstones & 6.5 & 1.4 & 4.6 & [PERSON] et al. [15] \\ \hline Sandstone & 8.72 & 9.27 & 0.94 & [PERSON] et al. [24] \\ \hline Carbonate rock & 7.9 & 14.2 & 0.56 & [PERSON] et al. [25] \\ \hline Shale & 3.5 & 2.75 & 1.27 & [PERSON] et al. [26] \\ \hline Sandstone & 21.84 & 21.64 & 1 & [PERSON] et al. [27] \\ \hline Hardened Cement paste & 10–25 & 5.56–20 & 1.8–1.25 & [PERSON] et al. [28] \\ \hline Carbonate rock & 22.83 & 22.70 & 1 & [PERSON] et al. [29] \\ \hline Mancos shale & 1.1 & 0.5 & 2.2 & [PERSON] et al. [30] \\ \hline Sandstone & 8–12 & 8.2 & 1.21 & [PERSON] et al. [31] \\ \hline \end{tabular}
\end{table}
Table 1: Summary of previous research on porosity using MIP and digital analysis methods.
## 3 Experimental Results
### Effect of Vibration on 1C Discharge Capacity
#### 3.1.2 Cumulative Distribution Function of Capacity Samples  
The theoretical cumulative distribution function of the capacity can be defined as follows:  
\[F(c)=p\{C<c\}, \tag{6}\]  
where \(C\) represents the capacity of the samples and \(c\) represents the capacity of any cells.  
Given the capacity sample \(c_{1},c_{2}\ldots c_{n}\) in ascending order, the sample cumulative distribution function of the 1C capacity can be determined as the following:  
\[F(c)=\left\{\begin{array}{c}0,c<c_{1}\\ \sum\limits_{i=1}^{i}{(\Delta c_{i}f_{n}(c))},c_{i}\leq c<c_{i+1},i=1,2,...n-1 \\ 1,c\geq c_{n}\end{array}\right.. \tag{7}\]  
According to the Glivenko-Cantelli theorem, when the sample size is large, the maximum difference between the empirical cumulative distribution function \(F_{(n)}(c)\) and the theoretical cumulative distribution function \(F(c)\) would be small; in other words, \(F(c)\) can be displaced by estimating \(F_{(n)}(c)\).  
Based on the method described above, the empirical cumulative distribution function of the of samples for 1C capacity before and after vibration is obtained, and the cumulative function of theoretical normal distribution before and after vibration is also obtained, as shown in Figures 7 and 8, respectively.  
It can be seen from the above figures that the empirical cumulative distribution function is well in accordance with the cumulative function of the theoretical normal distribution, no matter whether it is based on samples before vibration or based on the samples after vibration.  
Figure 6: The frequency histogram and theoretical normal distribution density function of the capacity samples after vibration.