esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Background
### P2G Contribution to Energy Supply  
Although hydrogen produced from the first stage of the P2G process is more efficient than generating SNG, the employment of hydrogen is restricted to fuel cells in some specified industries. SNG generated has the capability to be used in the GFUs, and it also has other considerable applications to improve the operational flexibility of P2G and GFUs facilities. Furthermore, SNG has comparable properties to conventional natural gas (CNG), which can be gathered, transferred, and sold to the natural gas network. Thus, in general conditions, SNG is more realistic, and the P2G conversion facility is regarded in the present paper.  
P2G conversion technology has considerable trading opportunities despite its not-ideal conversion efficiency. Moreover, the cross-product arbitrage is implemented by P2G technology between electricity and natural gas networks. P2G technologies can benefit more from the energy trade when the price gap between the electricity and gas markets is significant. Additionally, the represented facility can achieve profit by participating in reserve markets by deploying rapid active proton exchange membrane (PEM) electrolysis [41].
# Magnetic Abrasive Finishing of Beta-Titanium Wire Using Multiple Transfer Movement Method
## 2 Experimental Equipment and Methods  
### Experimental Equipment  
Figures 1 and 2 show the experimental setups of the magnetic abrasive finishing process and the finishing equipment for beta-titanium wire alloys, respectively. The magnetic abrasive finishing (MAF) equipment uses a rotational magnetic field and consists of a stepping motor and a system controller. These allow forward rotation and reverse rotation with ultra-high precision to improve the surface accuracy of machined beta-titanium alloy. This equipment was designed to allow the wire workpieces to reciprocate inside a flexible magnetic abrasive brush. MAF equipment for beta-titanium wire alloy processing is divided into three main parts: (i) the wire moving system (WMS), (ii) the rotational magnetic field system (RMS), and (iii) the wire vibration system (WVS). First, the wire moving system (WMS) consists of two sensors and a drive spool, stepping motor, belt, and power supply. The WMS reciprocates wires left and right inside the rotational magnetic field. Secondly, the RMS system consists of a belt, rpm controller, pulley, (Nd-Fe-B) permanent magnet, steel yoke, steel chuck, belt, program controller, stepping motor, and the unbonded magnetic abrasive materials. Permanent magnets (Nd-Fe-B) are used because they are small in volume and can generate high magnetic flux density, which can improve the resulting surface roughness [13; 14; 15; 16]. Finally, the wire vibration system (WVS) consists of an electronic slider, a power supply, and a programmable controller. This method generates vibration in a rotational magnetic field of 8 Hz with 2 mm amplitude. The vibration of the magnetic field rotating system is used for increasing the number of finishing times, allowing the magnetic smoothing to proceed efficiently on the workpiece surface. The specimen used in this study is \(\beta\)-titanium wire with a diameter of 0.5 mm and a length of 50 mm. Figure 3 shows a schematic view of the device used to generate a rotational magnetic field for processing beta-titanium wire. Figure 3a shows the 3D view of rotational magnetic field device. Figure 3b shows the 2D dimensional view of the rotational magnetic field device used in this experiment. The finishing device consists of two sets of Nd-Fe-B magnets (20 mm \(\times\) 10 mm \(\times\) 5 mm), Al 6063 chuck, AISI 1018 steel yoke, and two sharp edges of AISI 1018 steel magnetic poles.  
Figure 1: Experimental setup of magnetic abrasive finishing process for beta-titanium wire.  
Figure 2: Magnetic abrasive finishing equipment for beta-titanium wire using Nd-Fe-B permanent magnet.
## 3 Results
### Optimizing the Model
#### 3.1.6 XGBoost  
The selected hyperparameters for XGBoost in this study are number of estimators as 100 and 200; learning rate as 0.01 and 0.1; maximum depth as 3, 5, and 7; and number of subsamples as 0.5, 0.7, and 0.9 [82,83]. The number of estimators specifies the number of decision trees to be generated. Learning rate determines the contribution of each tree to the final prediction; smaller values result in less contribution from each tree, while larger values increase their contribution. Maximum depth limits the maximum depth of each tree, as deeper trees can lead to overfitting. Subsample specifies the proportion of samples used to train each tree.  
When the CV value is 10, the highest R\({}^{2}\) and lowest RMSE and MAE were observed. For CV as 10, the test set achieved an R\({}^{2}\) of 0.93, RMSE of 4.52 MPa, and MAE of 3.15 MPa. The Delta RMSE was the highest at 2.24 MPa, suggesting that the model may be slightly overfitting to the training data. Despite this, the model still demonstrates superior performance in terms of R\({}^{2}\), RMSE, and MAE. The CV as 10 yielded the best results. Thus, based on the higher R\({}^{2}\), lower RMSE, and MAE, the model with the CV as 10 was chosen as the optimal model, even though the delta RMSE is higher compared to other CV values. Therefore, the optimal model is achieved with a CV of 10, and the corresponding optimal hyperparameters are 200 number estimators, a learning rate of 0.1, a maximum depth of 5, and subsamples of 0.5, as summarized in Table 3.
## 2 Methodology  
### System Principle and Setup  
Figure 1 shows a schematic setup of the pixelated spatial phase shift shearography system, including a 532 nm laser source with a power less than 300 mw, expanded by the beam expander, with an angle of 20 degree. The WTB sample is illuminated by the expanded beam, which is received by the [PERSON] interferometry-based shearography system, where a beam splitter separates the beam into two light paths, each having 50% of the original light intensity at the receiving end of the beam splitter. Two mirrors (mirror 1 and mirror 2) are placed in front of the beam splitter, and mirror 2 is tilted with a small angle to ensure the shearing function of the shearography system. The light beam that goes towards mirror 1 is polarised in the x direction by polariser 1 and is received again by the beam splitter after mirror 1's reflection. In a similar way, the other light beam is polarised in the y direction by polariser 2 and reflected by mirror 2. Based on optical principles, the linear polarised light will change its oscillating direction to a circular form after passing through a quarter-wave plate; consequently, the two polarised beams are left-hand and right-hand circularly polarised before they are received by the CMOS camera. An image lens placed in front of the CMOS camera adjusts the focusing and aperture size. This enables appropriate sizing of the aperture to make the speckles received by the camera more distinctive at the black and white spots. This has the benefit, in subsequent image processing steps, of simplifying the setting up of window size in the frequency domain to select the lower frequency areas in the middle of the 2D frequency domain as the speckle rings are separated for different frequency ranges [21]. The traditional spatial carrier phase-shifting method [14] also uses the aperture to control the speckle size, but the advantage of the proposed method is that it has no requirement for the CMOS camera's spatial resolution or advanced spatial filters, except the need for a micro-polarisation image sensor. The two light beams thus generated are received by the CMOS camera on which micro-polarisation  
image sensing [22] is applied. The pixelated illustration is shown in Figure 2. Each pixel is divided into four subordinate pixels (a, b, c, and d) with four directions of polarisation at increments of 45\({}^{\circ}\) polarisation angles, making up 0\({}^{\circ}\) to 135\({}^{\circ}\) clockwise. With the integration of the derived left-hand and right-hand circularly polarised beams from the quarter-wave plate and the special image sensor, the phase difference between the two beams becomes twice the polarisation direction angles on the micro-polariser sensor. Thus, for one of the pixels of the camera, the four divided small pixels (a, b, c, and d) will be at the phase positions of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), respectively, with respect to the two incoming beams.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Experimental Results
#### 6.3.2 eNPA Experimental Results
(**a**) Average Throughput, (**b**) Source to Sink Delay, (**c**) Total Energy Consumed. scenario and the one with the returned mobile node. Comparing it with its opposite scenario, the energy consumed in this scenario is less because mobile nodes are reused in the network compared to the scenario of no reuse, which uses more mobile nodes.
## 2 Results
### Ab-FAR-1 Asp Promotes the Parasitism and Pathogenicity of RWTNs  
To assess the role of Ab-FAR-1 in the parasitism and pathogenicity of RWTNs, we obtained two single-copy positive T3 generation homozygous overexpressing Ab-FAR-1 Asp _A. thaliana_ lines (FOE1 and FOE3) by transformation and screening (Figure S2 and Figure 4a). Western blot results showed that a specific hybridisation band at the expected size of \(\sim\)19 kDa was detected in the transgenic _A. thaliana_ lines by the anti-Ab-FAR-1B polyclonal antibody (Figure 4a), which indicated that Ab-FAR-1 Asp was normally expressed in FOE1 and FOE3. Phenotypic observations of FOE1, FOE3, plants expressing the empty vector (EV), and WT plants after 10 days of growth on MS medium showed that the fresh weight, number of lateral roots and primary root length of FOE1 and FOE3 were significantly lower (\(p<0.05\)) than those of WT and EV (Figure 4b-d). This result indicates that Ab-FAR-1 Asp might interfere with the growth and development of _A. thaliana_ and cause phenotypic changes.  
Furthermore, infection assays with RWTNs showed that the symptom severity in FOE1 and FOE3 was significantly higher (\(p<0.05\)) than that in WT and EV. The nematode populations on FOE1 and FOE3 were 3650 \(\pm\) 217 and 3313 \(\pm\) 321, respectively, which increased by 19.16% and 8.16% compared to WT (3063 \(\pm\) 199), and 28.03% and 16.20% compared to EV (2851 \(\pm\) 213), respectively (Figure 5a-c). Therefore, the two Ab-FAR-1 Asp  
Figure 4: Phenotypic changes in Ab-FAR-1 Asp overexpressing _Arabidopsis thaliana_. (**a**) Molecular detection of Ab-FAR-1 Asp overexpressing lines. The phenotypes (**b**), fresh weight (**c**), number of lateral roots (**d**) and length of primary roots (**e**) of Ab-FAR-1 Asp overexpressing lines. WT, Wild type Col-0 ecotype _A. thaliana_; EV, _A. thaliana_ transformed with empty vector; FOE1 and FOE3, Ab-FAR-1 Asp overexpressing _A. thaliana_ line 1# (FOE1) and 3# (FOE3); _Ab-far-1_, PCR fragment bands of _Ab-far-1_ gene; hyg\({}^{*}\), PCR fragment bands of hygromycin resistance gene; _Act1_, reference gene _Act1_. Values are shown as means \(\pm\) SE, \(n\) = 10. Different letters above bars denote values that are significantly different from each other (\(p<0.05\), Tukey’s test). There were three independent biological replicates. Bar = 10 mm.  
oveexpressing lines were more susceptible to RWTN infection than the control plants, indicating that Ab-FAR-1 Asp promoted the parasitism and pathogenicity of RWTNs.
## 4 Diversity Performance
### Envelope Correlation Coefficient (ECC)  
The diversity performance parameter called ECC is used to show the how much a single antenna radiation pattern is independent from the other antennas. The correlation between the radiation pattern of the MIMO system antennas determines the channel isolation in a wireless communication link. The ECC shows the correlation between the antennas in the MIMO system. The ECC for the MIMO antenna must approach very low values. The lower value of an ECC or zero shows that the antennas have low correlation between the elements. The ECC can be determined from the S-parameters by the following formula:  
\[\text{ECC}=\frac{|S_{ii}^{*}S_{ij}+S_{ji}^{*}S_{jj}|^{2}}{[1-(|S_{ii}|^{2}-|S_{ ji}|^{2})][1-(|S_{jj}|^{2}-|S_{ij}|^{2})]} \tag{5}\]  
The threshold value for ECC should be less than 0.5 for the MIMO antenna system, which guarantees the reliable communication link. Figure 10 shows that the ECC value of the MIMO antenna system is less than the required threshold (ECC \(\leq\) 0.5).  
Figure 9: Transmission coefficient of antenna (S\({}_{21}\), S\({}_{31}\), and S\({}_{23}\)) vs. frequency.
# Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance
## 3 Security and Privacy Model
### Trust Model  
The trust model describes the actors of interest in terms of operation, use and the protection goals introduced in Section 3.1. We discuss with respect to what protection goals an actor is trusted or untrusted, i.e., may act as an attacker in the latter case.  
The first actor to be considered is the _worker_ as the data subject. Data associated to this actor is processed by the smart documentation system. The worker is interested in the protection goals of confidentiality as well as integrity and authenticity of documentation data. Nevertheless, the worker is not fully trusted as he may have an interest in acting as a modifying attacker, e.g., to disguise evidence of errors by modification or deletion of documentation data.  
The _system supplier_ creates the smart documentation system and puts it into operation. We accept him as fully trustworthy. This means that he has no interest in breaking protection goals, nor does he support any other actor in breaking protection goals. We cannot avoid this assumption because the system supplier is also responsible for the integration and configuration of security features. The system supplier has administrative rights on the system, which he uses after commissioning for maintenance purposes only. However, we neglect the case of system maintenance in this work as we consider the system supplier as fully trusted.  
The operating organization is the main antagonist of the primary protection goal of confidentiality of documentation data. We represent the operating organization through its _system administrator_ who has administrative rights on the documentation system. We concede that he can become a privacy attacker at the direction of the organization's management, attempting to break the confidentiality of the documentation data by means of his privileges and capabilities. The system administrator is also not fully trusted with respect to the integrity and authenticity of the documentation data, which he may attempt to compromise at the direction of management, i.e., he may also become a modifying attacker. Both as a privacy attacker and as a modifying attacker, the system administrator may also attempt to target the derived protection goals of integrity and authenticity of the smart documentation system to achieve his attacker interests.  
Finally, we introduce a trusted party within the operating organization, i.e., an actor whom the worker can trust sufficiently to protect his privacy interests. This party is authorized to agree with the operating organization under which technical and organizational conditions the smart documentation system can be implemented in the organization. We introduce this party as a _trustworthy subject_ representing, for example, the works council of the operating organization. The trustworthy subject takes an examining stance towards the smart documentation system and the corresponding organizational processes, which we need for the system design introduced in the following section.
## 3 Results
### Analysis of Phylosphere Fungal Communities Using Network Analysis  
This study established a co-occurrence network in order to analyze the relationships among the phyllosphere fungal communities of the four native grass species (Figure 6). The network consisted of 116 nodes and 587 edges and the network diameter and density were 14 and 0.088, respectively (Table 2). We determined that the coexistence between communities was greater than the exclusion (99.83%). The network was composed of three phyla, including Ascomycota and Basidiomycota, in the phyllosphere fungal community of the four grass species, which accounted for 87.07% and 12.07%, respectively, of all species. Moreover, species _Erythrobasidiales_ sp., _Rachicladosporium luculiae_, and _Bjerkandera adusta_ had the highest betweenness centrality values in the network (Table 3) and they played key roles in maintaining the structure and function of the fungal communities. Therefore, they were the key fungal species of the four native grass species.  
Figure 6: Co-occurrence network of microbial taxa in the four native grasses. Nodes represent fungal species, whereas edges represent positive and negative connections between pairs of species.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **Nodes** & **Edges** & **Average Degree** & **Network Diameter** & **Network Density** & **Modularity** & **Average Clustering Coefficient** & **Average Path Length** & **Co-Presence** \\ \hline Index & 116 & 587 & 10.121 & 14 & 0.088 & 0.605 & 0.788 & 4.016 & 99.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network indices obtained through network analysis from fungal communities of the four native grasses.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & _Erythrobasialiales_ sp. & _Rachicladosporium luculia_ & _Bierkandera adusta_ & _Stemplylium herbarum_ & _Coprimopsis calospora_ \\ \hline Phylum & Ascomycota & Ascomycota & Basidiomyota & Ascomycota & Ascomycota \\ Betweenness Centrality & _697.43_ & _672.50_ & 585.50 & 387.89 & 323.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dominant keystone species from four native grasses.
###### Abstract
Third, machine learning techniques for smoke detection are recently emerging, like convolutional neural networks [16; 22; 23] and maximum likelihood [8], but depend heavily on the quality of training samples from different cover types (e.g., smoke, cloud, dust, and bright surface), choice of model/algorithm, and model hyper-parameter configuration. Finally, the most well-established smoke detection method is based on the aerosol index (AI) which has been calculated for several different satellite sensors spanning a period of more than 30 years [24; 25; 26]. Generally, the positive AI value indicates the presence of absorptive aerosols (i.e., plumes of dust, volcanic ashes, and smoke from fires) [27]. The most commonly used AI is computed from two ultraviolet bands (0.34 and 0.38 \(\upmu\)m) based on the spectral contrast due to differences in smoke aerosol absorption, which also removes ozone's impact because of the very limited ozone absorption at these two bands. Although using ultraviolet bands could minimize impacts from interfering absorbers (i.e., ozone), AI products derived from ultraviolet bands usually have a quite coarse spatial resolution. Recently, an aerosol detection product (ADP) has been developed for the visible infrared imaging radiometer suite (VIIRS) based on AI calculated from the 0.412 and 0.445 \(\upmu\)m bands. The VIIRS AI has a spatial resolution as high as 750 m although the band pair choice for AI calculation is not optimal [28]. However, both the ultraviolet bands-based AI and VIIRS AI tend to miss thin smoke plumes when smoke conditions are characterized by small aerosol optical depth, low aerosol layer height, and low single scattering albedo since absorption AI has been shown to be less sensitive to these three smoke conditions [29]. The AI-based method also tends to confuse smoke with bright surfaces (e.g., desert) due to the strong wavelength-dependent albedo of bright surfaces, which could lead to a similar spectral contrast as smoke absorption and then high AI values [30]. In addition, the AI-based method is hardly able to distinguish smoke from high clouds with an effective cloud fraction close to one because these clouds often present positive AI values. Therefore, the development of novel methods for detecting fire smoke plumes is still needed. Considering the strong Mie scattering at blue and green bands caused by smoke aerosols due to the similarity of smoke particulate diameters to these wavelengths, in this study, we developed a new scattering-based smoke detection algorithm (SSDA) to detect fire smoke plumes using daily VIIRS 750-m reflectance and brightness temperature data. The goals of this study were: (1) to design a new smoke detection algorithm (SSDA) capable of detecting smoke emitted by both large and small fires and separating smoke plumes from clouds and bright surfaces; (2) to validate the SSDA using manually delineated smoke reference data and compare its performance with existing smoke-related products; and (3) to test the SSDA potential for global application.
## 4 Design of Proposed Scheme: FAPR
### Algorithms and Formulation
#### 4.5.3 Backup Path Overlap Removal Algorithm  
As mentioned before, a loop may exist between the original path and its corresponding backup path, which needs to be removed. The algorithm for removing overlapping paths is shown in Algorithm 2. The input of this algorithm is the set of flows, \(F_{ij}\), on the link \(e_{ij}\), the set of flows' original paths, \(O_{ij,f}\), and the set of backup paths, \(B_{ij,f}\), and the output is the set of optimized backup paths, \(B^{\prime}_{ij,f}\), for each flow. For each flow, \(f_{ij}\), on the link \(e_{ij}\), we first obtain its original forwarding path, \(o_{ij,f}\), and all the \(K\) backup paths, \(b_{ij,f}\), allocated for it. Second, we compare the original path and one of its backup paths to delete their overlapping parts and to compose the new backup path, \(b^{\prime}_{ij,f}\). Finally, we obtain the set of optimized backup paths, \(B^{\prime}_{ij,f}\). For each flow, it is necessary to traverse the backup paths of the current link and compare them with the flow's original path. Consequently, the time complexity of Algorithm 2 is \(O(kn)\), where k is the number of backup paths of each link and n represents the scale of input flows.  
```
1:\(F_{ij}\), \(O_{ij,f}\), \(B_{ij,f}\);
2:\(B^{\prime}_{ij,f}\);
3:for flow \(f_{ij}\) in \(F_{ij}\)do
4: Compare \(b_{ij,f}\) with \(o_{ij,f}\);
5:if overlap path exists then
6: delete overlap path;
7: Add \(b^{\prime}_{ij,f}\) to \(B^{\prime}_{ij,f}\);
8:else
9: Add \(b_{ij,f}\) to \(B^{\prime}_{ij,f}\);
10:endif
11:endfor
```  
**Algorithm 2** Backup path overlap removal algorithm