esa-sceva/satcom-chunk-collection huggingface dataset:
# Dependence of the Staphylococcal Volatilome Composition on Microbial Nutrition
## Results and Discussion
### Chemical Composition of the S. aureus and S. epidermidis Volatilomes  
We evaluated the composition of the staph volatilome both by the numbers of volatiles in each of the 16 chemical classes (i.e., presence vs. absence) and by the relative abundances of the volatiles in each class using the 146 level 2 and level 3 volatiles. When defining the composition by the number of volatiles, a compound that was detected in any one of the _S. aureus_ or _S. epidermidis_ cultures in any medium was counted toward the volatilome. When defining the volatilome composition by the relative abundance, the highest peak intensity for each compound across any of the _S. aureus_ or _S. epidermidis_ cultures was added to the total. By the number of volatiles, the total staph volatilome, which is a combination of _S. aureus_ and _S. epidermidis_ in all four complex media, was dominated by hydrocarbons (HC, n = 42, 29%), followed by aromatic compounds (aggregated across all aromatic compound categories; ARO, n = 26, 18%), ketones (KET, n = 23, 16%), sulfur compounds (SULF, n = 12,
## 3 Results and Discussion
### Experimental Study of the Ag Adsorption by Atomic Absorption Spectrometry (AAS)  
#### 3.2.1 Calibration Curves for the Ag Ions and Ag Nanoparticles  
To carry out the spectrophotometry analysis, we must first observe a linear behavior in the concentration-absorbance plot according to Beer's law. Calibration curves at concentrations of 1, 2, 3, and 4 mg\(\cdot\)L\({}^{-1}\) were obtained from Ag ions and AgNP solutions, respectively, as shown in Figure 3. Linear behavior in both cases was observed. This behavior is present in a range of 1 to 4 mg\(\cdot\)L\({}^{-1}\), according to the manual operation of the equipment. This result suggests that the solutions of Ag NPs have a linear behavior, similar to that reported for the Ag ionized, and the slopes of the calibration curves for the Ag ions and AgNP solutions were very similar at 0.0265 and 0.0251, respectively. However, the differences between the Ag ions and AgNP solutions were considered and emphasized for subsequent analysis by atomic absorption spectrophotometry. On the other hand, the experimental data obtained from the AgNP solutions, suggest that the flame temperature employed during the ion atomization process was enough for nanoparticle disintegration and, consequently, for forming Ag atomic vapor.  
\begin{table}
\begin{tabular}{c c|c c} \hline
**Element** & **wt. \% Zeolite** & **Oxides** & **wt. \% Zeolite** \\ \hline Si & 81.7 & SiO\({}_{2}\) & 77.04 \\ Al & 8.01 & Al\({}_{2}\)O\({}_{3}\) & 13.2 \\ K & 6.04 & K\({}_{2}\)O & 6.42 \\ Fe & 1.145 & FeO & 0.66 \\ Ca & 1.2 & CaO & 0.75 \\ Mg & 0.725 & MgO & 0.53 \\ Na & 1.18 & Na\({}_{2}\)O & 1.4 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of natural zeolite.  
Figure 2: **(a)** SEM micrograph of the zeolite; **(b)** EDS mapping associated with the natural zeolite; and **(c–f)** maps of distribution of the elements Si, O, Al, K, Fe, and Ca, respectively.
## 3 System Methodology
### Construction of Multicast Tree with QoS Constraints  
The construction of a multicast tree with quality of service (QoS) constraints is crucial for optimizing the multimodal transportation network. To achieve this, we formulate the problem as an integer linear programming (ILP) problem. The decision variables and parameters related to construction of multicast tree are as follows.  
\(x_{ij}\): Binary variable indicating whether arc \((i,j)\) is included in the multicast tree (1 if included, 0 otherwise).  
\(y_{i}\): Binary variable indicating whether city \(i\) is selected as part of the multicast tree (1 if selected, 0 otherwise).  
\(c_{ij}\): Transportation cost associated with arc \((i,j)\).  
\(ce_{ij}\): Predicted CO\({}_{2}\) emissions associated with arc \((i,j)\).  
\(q_{ij}\): QoS parameter associated with arc \((i,j)\).  
\(C_{i}\): Capacity constraint for city \(i\).  
\(D_{j}\): Demand constraint for destination city \(j\).  
\(R_{T}\): Risk threshold for CO\({}_{2}\) emissions.  
\(DC_{ij}\) represents the delay cost associated with arc \((i,j)\).  
\(d_{ij}\) is a binary variable indicating whether a delay occurs on arc \((i,j)\).  
The overall objective is to minimize the total transportation cost, CO\({}_{2}\) emissions, and delay cost associated with arc \((i,j)\) while meeting QoS, supply, and demand constraints. The ILP formulation is as follows:  
\[Min{\sum}_{(i,j)\in A}\ c_{ij}\cdot x_{ij}+Min{\sum}_{(i,j)\in A}\ ce_{ij} \cdot x_{ij}+Min{\sum}_{(i,j)\in A}\ DC_{ij}\cdot d_{ij} \tag{22}\]
# Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
###### Abstract  
The joint use of multi-frequency signals brings new prospects for precise positioning and has become a trend in Global Navigation Satellite System (GNSS) development. However, a new type of inter-frequency clock bias (IFCB), namely the difference between satellite clocks computed with different ionospheric-free carrier phase combinations, was noticed. Consequently, the B1/B3 precise point positioning (PPP) cannot directly use the current B1/B2 clock products. Datasets from 35 globally distributed stations are employed to investigate the IFCB. For new generation BeiDou Navigation Satellite System (BDS) satellites, namely BDS-3 satellites, the IFCB between B1/B2a and B1/B3 satellite clocks, between B1/B2b and B1/B3 satellite clocks, between B1C/B2a and B1C/B3 satellite clocks, and between B1C/B2b and B1C/B3 satellite clocks is analyzed, and no significant IFCB variations can be observed. The IFCB between B1/B2 and B1/B3 satellite clocks for BDS-2 satellites varies with time, and the IFCB variations are generally confined to peak amplitudes of about 5 cm. The IFCB of BDS-2 satellites exhibits periodic signal, and the accuracy of prediction for IFCB, namely the root mean square (RMS) statistic of the difference between predicted and estimated IFCB values, is 1.2 cm. A triple-frequency PPP model with consideration of IFCB is developed. Compared with B1/B2-based PPP, the positioning accuracy of triple-frequency PPP with BDS-2 satellites can be improved by 12%, 25% and 10% in east, north and vertical directions, respectively.  
inter-frequency clock bias (IFCB); multi-frequency; precise point positioning (PPP); BeiDou navigation satellite system (BDS) 20179 734 10.3390/rs9070734
# Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
## 2 Methodology
### Data and Case Study  
The data of this study was acquired from the Pahang-Selangor tunnel, Malaysia (Figure 4). The main aim of constructing this tunnel was to provide a flow path of fresh water. The tunnel specifications are as follow: (1) diameter: 5.2 m; length: 44.6 km; and longitudinal gradient: 1/1900. In addition, under free-flow conditions, the maximum allowable discharge of the tunnel is 27.6 m\({}^{3}\)/s. In order to excavate the tunnel, three different _TBMs_ were used for about 35 km of the tunnel. The remainder of the tunnel was excavated using the drilling and blasting method. The geological units include granite, metamorphic and some sedimentary rocks; though, most of the rocks excavated with the abovementioned method is comprised of granite. Many geotechnical and geological investigations were conducted in the tunnel to collect rock block samples for testing. Finally, in multiple locations of _TBMs_ site, more than 100 granite block samples were obtained from the tunnel face. A robust procedure from the International Society for Rock Mechanics [61] was followed for preparing the samples to test. Several lab tests were conducted on the samples, including density (in dry condition), Schmidt hammer rebound number (\(R_{n}\)), uniaxial compression strength (\(\sigma_{c}\)), tensile strength (\(\sigma_{t}\)), point load index (\(Is_{50}\)), and p-wave velocity (\(V_{p}\)). In this study, the _BI_ values were calculated according to the following equation [62]:  
\[BI\ =\ \frac{\sigma_{c}}{\sigma_{t}} \tag{1}\]  
where, \(\sigma_{c}\) and \(\sigma_{t}\) are the uniaxial compression strength and tensile strength, respectively.  
Thus, considering and selecting _BI_ values as model output, four parameters of density, Schmidt hammer rebound number, point load index and p-wave velocity were set as inputs in the form of a database with 110 datasets. The range, mean, unit and symbols of inputs and output parameters in this study are tabulated in Table 1. According to this table, average values of 5491.6 m/s, 2.59 g/cm\({}^{3}\), 40.5, 3.6 MPa, and 15.5 were obtained for \(V_{p}\), \(D\), \(R_{n}\), \(Is_{50}\) and \(BI\), respectively. In the next section, modeling procedure in approximating _BI_ as a function form of \(f\) (\(V_{p}\), \(D\), \(R_{n}\), and \(Is_{50}\)) and the obtained results will be presented in detail.
## 4 Results
### Microrobot Inspection with QR Code Robot Selection in Confined Spaces and AR Headset Teaming  
The results of the QR code detection test using an AR headset and microrobot AR inspection of confined spaces are given in this section. Figure 16a depicts a system in which an ESP32-CAM attached to a HEXBUG Spider captures raw image data. As is displayed in Figure 16b, the microrobot and a small flashlight was deployed to survey a ceiling panel with water damage to find the source of the leakage stain. The camera mounted on the HEXBUG Spider is accessible on the AR headset by scanning the QR code affixed to the wall. Using this computationally efficient means of deployed camera selection, the defective pipe can be surveyed by inspection with an AR headset. The HoloLens has a  
Figure 15: **(a)** Image presented through augmented reality viewer by wireless telemetry transmitted from ESP32-CAM showing combined scene with detected object (baseball) with inset overlay of abstracted information. **(b)** Tool detection algorithm successfully identifies hammers and screwdrivers in image captured by microrobot. **(c)** Object detected in narrow pipe captured by microrobot.  
pre-installed QR code service with a maximum detection distance of 20 cm. This range is incompatible with the anchor registration requirements of this human-building interaction application. A workaround was to install a custom QR code reader software that enables the HoloLens to read QR codes with a range beyond 20 cm, as shown with a successful demonstration in Figure 16c.  
Figure 17 demonstrates the minimum and maximum detection distance versus the QR code size. In this figure, the blue line represents the minimum AR headset distance from the QR code when it can successfully detect each QR code size. The red line shows the maximum distance of the QR code from the AR headset that could be detected at that corresponding size.  
The results of the simulcast identifying key objects are demonstrated in Figure 18. In this figure, the screenshots capture the view of both AR headsets, in which the users are viewing the live stream from the microrobot in an AR overlay while monitoring the environment and interacting with each other. The team of AR headset users successfully identified two key objects in AR views and environment.  
Figure 16: (**a**) HEXBUG Spider located in confined ceiling space, (**b**) human wearing AR headset with inset showing first-person view from the microrobot, (**c**) QR code detected in AR headset, (**d**) AR headset used to inspect the damaged pipe.  
Figure 17: Maximum and minimum QR code detection distance with respect to QR code size.
## 5 Implementation and Validation
### Case Study  
To provide a clear understanding of the proposed solution, this subsection presents a single and very simple case study as an example. The subsequent validation subsection provides a comprehensive overview of the parameters considered for validation, along with the detailed results. The YANG file used in this case study is available in the author's GitHub repository [46]. The YANG module \"simple-example\" contains a container definition called \"bag\", a grouping definition called \"my-grouping\", and another container named \"example-container\" uses this grouping definition. Also, the module contains other definition statements, such as typedef, leaf-list, and rpc definitions. The YANG \"simple-example.yang\" is passed as an input file to the pyang opc-ua plugin using the following command:  
$ pyang -f opc_ua inputfiles/simple-example.yang -o generated-files/simple-example-model.xml  
The generated OPC UA model design file is maintained in the author's GitHub repository for reference [47]. The generated file contains the ModelDesign root XML element with namespace prefix and namespace declarations as defined in the YANG. It encompasses child node namespaces, ObjectType nodes for each grouping statement, variable nodes for leaf and leaf-list statements, and ObjectsFolder type for list statements. The generated simple-example-model.xml is then passed to the OPC UA model compiler to generate the OPC UA NodeSet2 XML files using the following command:  
$ Opc.Ua.ModelCompiler -d2 generatedfiles/simple-example-model.xml -cg generatedfiles/uamodel-generatedfiles/simple-example-model.csv -o2 generatedfiles/uamodel-generatedfiles/  
The model compiler generated multiple files, such as NodeSet2.xml, Types.xsd, PredefinedNodes.xml, and C# template files. These are maintained in the author's repository for reference [48]. To validate the generated NodeSet2 XML files, the model is loaded into the FreeOpcUA python-opcua server [49]. Then opcua-client-gui is connected to the server to browse through the information models and RPCs [50]. Figure 8 shows the screen capture of the OPC UA GUI client with the simple example NodeSet2 XML files loaded in the OPC UA server. In the GUI client, objects defined in YANG, such as Bag, ExampleContainer, and ListOfPorts, are visible.
## 3 Application to Gimpo Airport
### Three Types of Topology
#### 3.2.3 Pier Topology  
The pier topology is a layout in which aircraft can be moved through a long gate aisle. Since the pier topology can accommodate more gates and aircraft than the satellite topology, it can be used to deploy multiple aircraft and to shorten embarkation and disembarkation times [22]. As shown in Figures 21 and 22, since the TLOF pad and the gate area are clearly separated, congestion of the vertiport is low.  
Figure 19: Satellite topologies for the Gimpo Airport parking lot that comply with the FAA’s new regulations: (**a**) current parking space; (**b**) expanded parking space.  
Figure 20: Satellite topologies for the Gimpo Airport parking lot that comply with the EASA’s new regulations: (**a**) current parking space; (**b**) expanded parking space.
# An Improved Artificial Bee Colony for Feature Selection in QSAR
## 4 The Proposed Method
### ABC Algorithm for FS in QSAR  
The basic ABC algorithm is originally proposed for optimization problems in continuous space; however, FS is an optimization problem in discrete space. Each feature subset is represented with a binary string. \"1\" in the string means the feature is selected and \"0\" means the feature is not selected. Hence, the value obtained by Equation (4) needs to be converted into a discrete value by Equation (7):  
\[x_{i,j}=\begin{cases}1,&x_{i,j}\geq 0.5\\ 0,&otherwise\end{cases} \tag{7}\]  
If the value of a dimension is greater than or equal to the threshold value 0.5, the corresponding feature is selected and then its value will be set as 1. Otherwise, it is not selected and its value will be set as 0. Accordingly, an ABC-based algorithm for feature selection in QSAR is proposed, namely ABC-PLS. The pseudo code of the ABC-PLS algorithm can be seen in Algorithm 1.  
```
0: Population size \(SN\), Maximum number of iterations \(MaxIt\), Abandonment limit \(L\), \(counter=0\), \(t=0\).
0: The optimal food source \(x_{best}\), the best fitness value \(f(x_{best})\).
1: Initialize the population \(x_{i}\), (\(i=1,2,\ldots SN\) ) by using Equation (4).
2: Evaluate the fitness value of each food source by using Equation (1).
3:while\(t\leq MaxIt\)do
4: %Employed bee phase
5:for each employed bee do
6: Randomly select a different food source \(x_{k}\).
7: Generate a new food source according to Equation (5) and convert it into discrete values by using Equation (7).
8: Evaluate the fitness value of each food source by using Equation (1).
9: Update \(x_{i}\) according to greedy selection, and increase its \(counter\) counter by 1 if not update.
10:endfor
11: Calculate the selection probability of each food source by using Equation (6).
12: %Onlooker bee phase
13:for each onlooker bee do
14: Select a food source \(x_{i}\) according to the selection probability by the roulette-wheel scheme.
15: Randomly select a different food source \(x_{k}\).
16: Generate a new food source according to Equation (5) and convert it into discrete values by using Equation (7).
17: Evaluate the fitness value of each food source by using Equation (1).
18: Update \(x_{i}\) according to greedy selection, and increase its \(counter\) counter by 1 if not update.
19:endfor
20: %Scout bee phase
21:for each food source do
22:if\(counter\geq L\)then
23: Replaced by a new food source according to Equation (5) and convert it into discrete values by Equation (7).
24: Evaluate the fitness value of the new food source by using Equation (1).
25:endif
26:endfor
27:endwhile
28:Output \(x_{best}\) and \(f(x_{best})\).
```  
**Algorithm 1** Pseudo code of the ABC-PLS algorithm
###### Abstract
Many factors influence the ultrasonic regeneration efficiency, such as ultrasonic intensity, solution temperature and addition of additives, such as ethanol. Ultrasonic intensity affects collapse times of microbubbles and the temperature and pressure upon collapse. This in turn, leads to a greater desorption of contaminants [19, 20, 21, 22]. There is, however, a potential for damaging the adsorbent by ultrasound, which greatly depends on the ultrasound frequency and intensity. To get an optimum result for regenerating exhausted adsorbent, it is vital to identify the optimal ultrasound intensity [12,18]. In addition to the desorption process being endothermic in nature, higher solution temperatures cause a decrease in a liquid's tensile stress and viscosity, leading to cavitation bubbles being more easily generated [22,23]. The addition of ethanol can reduce the surface tension of aqueous solution, thus reducing the cavitation threshold and facilitating the generation of bubbles. The generation of more transient cavitation bubbles increases the amounts of high-speed microjets and high-pressure shock waves as they collapse. Ultrasound and ethanol even seem to produce a synergistic effect where the overall desorption was found to be greater than the sum of the two separate processes [23]. However, adding excess amounts of ethanol may reduce regeneration efficiency, possibly because of the coalescence of numerous cavitation bubbles, forming larger and more stable bubbles. These stable bubbles dampen the passage of sound energy through the liquid and eliminate many of the smaller bubbles that would have collapsed [20]. The aim of this study was to test the feasibility and effectiveness of ultrasonic regeneration of IPA loaded activated carbon. To properly test desorption, adsorption tests are first performed to identify optimum pH and create a reference point. Optimization for IPA desorption is performed for ultrasonic intensity, temperature and ethanol addition. Finally, a 4-cycle adsorption-desorption test takes place to test the regeneration efficiency over multiple cycles.
## 3 Results  
### Biometric Parameters of M. sativa  
As shown in Figures 1 and 2, the application of a GBH had a negative impact in both root and shoot length and biomass. By analyzing Figure 1a, it is possible to notice that there was a significant decrease in root length (F (5, 16) = 106.8; _p_\(\leq\) 0.05) for concentrations above the second lowest, with a monotonic dose-response relationship. Between 12 and 18 mg kg\({}^{-1}\) of the a.i. there was a drastic reduction in root length--the inhibition values rose from 27% to 68% comparatively to the CTL group, in which the EC\({}_{50}\) was estimated to be 16 mg kg\({}^{-1}\) (95% CL: 14-19). Regarding shoot length, despite the observed decrease as the concentration increased, significant differences (F (5, 16) = 36.21; _p_\(\leq\) 0.05) were only recorded when plants were exposed to the highest doses of GBH (18, 26 and 40 mg kg\({}^{-1}\) of the a.i.), with inhibition values up to 64% in relation to the CTL. Nevertheless, a similar EC\({}_{50}\) was estimated (16 mg kg\({}^{-1}\) of the a.i.; 95% CL: 14-22).  
Regarding fresh biomass (Figure 2), both roots and shoots were affected by GBH exposure in a concentration-dependent manner. Despite both organs exhibiting the same global trend, the results point towards a higher sensitivity of shoots when compared with roots. In fact, while in shoots, all concentrations are statistically different from the CTL (F (5, 15) = 92.02; _p_\(\leq\) 0.05)--reaching inhibition  
Figure 1: Average root (**a**) and shoot (**b**) lengths of _M. sativa_ plants, 21 days after exposure to different concentrations of glyphosate (GLY). Error bars correspond to the standard deviation. Statistically significant differences compared to the control (CTL; no GLY), considering _p_\(\leq\) 0.05, are marked with a \({}^{*}\) above bars.  
values ranging from 36% to 88%, in roots biomass--significant differences (F (5, 16) = 16.02; \(p\leq 0.05\)) were only detected upon exposure to a.i. concentrations of 18, 26 and 40 mg kg\({}^{-1}\), with reductions of about 62, 79 and 90%, respectively. The highest effects observed in shoots are translated into differences in the EC\({}_{50}\) values obtained. For root fresh biomass, the estimated a.i. concentration was 15 mg kg\({}^{-1}\) (95% CL: 12-22), whereas for the shoot fresh biomass it was 12 mg kg\({}^{-1}\) (it was only possible to calculate the lower limit of the CL, which was 8.5).