esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This research has demonstrated the effective application of magnetic [poly(aniline-co-o-toluidine)/Fe\({}_{3}\)O\({}_{4}\)] nanoparticles and magnetite (Fe\({}_{3}\)O\({}_{4}\)), in combination with alkyld paint, as a novel coating for corrosion protection in carbon steel in acidic environments. The magnetic nanoparticles and magnetite were blended with alkyld resin in varying percentages (5-25%). The electrochemical impedance spectroscopy (EIS) results revealed that this innovative coating offered superior protection against corrosion, compared to alkyld paint alone.  
Furthermore, the coatings incorporating the copolymer [poly(aniline-co-toluidine)/Fe\({}_{3}\)O\({}_{4}\)] nanoparticles displayed a higher inhibition efficiency than those mixed solely with magnetite. This improved performance was attributed to an increased polarization resistance (R\({}_{\rm p}\)), which was particularly noticeable at the maximum inhibitor percentage. As the inhibitor percentage increased, the coating's wear resistance was enhanced, forming a more robust protective layer on the carbon steel surface.  
The novel coating also demonstrated the capacity to reduce electrical capacitance, indicating its potential for displacing adsorbed water molecules or ions on the metal surface. The adhesive force of the coating on the metal alloy surface was notably improved when using the alkyld with [poly(aniline-co-o-toluidine)/Fe\({}_{3}\)O\({}_{4}\)] nanoparticles, creating a physical barrier between the alloy and the corrosive medium, and inhibiting electrolyte solution penetration.  
The findings from this research open promising avenues for the development of smart corrosion protection strategies for carbon steel.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13148189/s1](https://www.mdpi.com/article/10.3390/app13148189/s1)]([https://www.mdpi.com/article/10.3390/app13148189/s1](https://www.mdpi.com/article/10.3390/app13148189/s1)), Figure S1: (a) Nyquist plot, (b) Bode plot of poly (aniline-co-o-toluidine) with Alkyd paint for carbon steel in 0.1 M HCl at 298K; Figure S2: (a) Nyquist plot; (b) Bode plot of Fe3O4 MNPs with Alkyd paint for carbon steel in 0.1 M HCl at 298K.  
Conceptualization, methodology, software, validation, formal analysis, writing--original draft preparation, O.I.M., S.S.A.-L., A.S.A.-M. and H.L.; investigation, resources, data curation, writing--review and editing, A.C. and H.L.; visualization, supervision, project administration, funding acquisition, B.H. and Y.G.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data cannot be shared at this time as the data also forms part of an ongoing study.  
The authors declare no conflict of interest.
## 4 Framework Design
### Phase 1: Similarity Assessment
```
Input: Source Domain \(S_{n}\) for \(n=1,2,3\), Target Domain \(T\) Output: Trained Model \(M_{n}\) for \(n=1,2,3\)
1 Initialization:
2 Uniformize the size of the data sets. 3 Sample attack types and standardize the number of labels to 625,783. 4 Transfer learning:
5 Evaluate the similarity between \(S_{n}\) data sets before proceeding with transfer learning. 6 Phase 1 process:
7 while\(n\leq 3\)do
8 Input Train data \(S_{n}\) 100%, Test data \(T\) 70%. 9 Train data \(S_{n}\) learning through DNN layer with LeakyReLU. 10 Compile the model with an MSE loss function for similarity evaluation and Adam optimizer. 11 whileepoch\(\leq 100\)do
12 Check that overfitting does not occur at epochs 20/40/60/80/100. 13 ifvalidation\(loss<1\)then
14 Early stopping for overfitting prevention. 15 Evaluate the performance of the model using MAE function on Test \(T\) data set. 16 Save the trained model. 17 return Trained Model \(M_{n}\) for \(n=1,2,3\). ```  
**Algorithm 1**Similarity evaluation between source and target domains  
The goal is to evaluate the similarity between source domains (\(S_{n}\)) and a target domain (\(T\)) before conducting knowledge transfer. After the preprocessing and sampling procedure, our framework conducts deep learning with the aforementioned network layers for similarity assessment. Note that we use LeakyReLU as an activation function to evaluate which of the source domain is most similar to the target. The MSE and MAE loss functions are used as performance evaluation indicators of the regression model to compare the performance of the trained model (\(M_{n}\)). The training is repeated until the epoch reaches 100, and the appearance of each epoch is observed at 20, 40, 60, 80, 100. If there is overfitting at this time, the training undergoes early stopping to prevent overfitting.
## 2 Background of the Project and Technical Route  
### Overview of Regional Project  
This study is based on the copper-gold open-pit mine in the ninth district of Yinshan Mining Co., Ltd., Jiangxi Copper Industry Group. The site is located in the northern suburbs of Dexing City, Jiangxi Province, at the northwest foot of the Damaoshan branch of the Huaiyu Mountains (Figure 1). It is a typical hilly mountain landform. The mining area is 2.7 km long from north to south and 2.15 km wide from east to west, with an area of about 4.36 km\({}^{2}\) and a production capacity of 5000 t/d. The Neoproterozoic Zhangcun Group is widely exposed in the mining area, followed by the Mesozoic Lower Cretaceous Daguding Formation and a small amount of the Shixi Formation. The Zhangcun Group is the basement strata of the mining area, which is composed of metamorphic rocks. The Daguding Formation is a continental volcanic rock series covering the Zhangcun Group, which is distributed in the southwest of the mining area and near the Xishan crater. Its distribution is characterized by a northeast zonal distribution. The Zhangcun Group and the Daguding Formation are the main ore-bearing surrounding rocks in the mining area. The Shixi Formation is unconformably covered by the Zhangcun Group and the Daguding Formation, and a small amount is distributed in the southwest of the mining area. The main lithology of the mining area includes phyllite, quartz porphyry, and Quaternary residual deposits. Phyllite is the most important and widely distributed rock mass in the mining area, mainly comprising sericite phyllite and mixed with quartz. The quartz porphyry is a small dyke, which is nearly east-west to northeast-east, with an inclination angle of 80-90\({}^{\circ}\), a length of 300-800 m, a width of 2-20 m, and a depth of more than 500 m. The Quaternary is the Quaternary residual slope and alluvial loose accumulation layer, widely distributed in the mining area. The lithofacies are mainly clay, mixed with breccia such as slate and phyllite, mostly gravel with a diameter of less than 2 cm. The surrounding rock of the ore body is mainly phyllite, quartz porphyry, and a small amount of calcite porphyry. The surrounding rock is relatively stable. The factors affecting the stability of the surrounding rocks are the development of phyllite schist and faults in local sections.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 5 Measurement Campaign  
### Environment  
The environment for our measurement campaign can be described by a typical industry hall for metal work, e.g., metal lathe, metal cutting. The dimensions are approximately \(30\,\mathrm{m}\times 11\,\mathrm{m}\times 8\,\mathrm{m}\) (L \(\times\) W \(\times\) H), see Figure 5a. There are many metal objects and pieces of machinery that make for a rich wireless environment. Twelve static, frequency coherent, and distributed antennas were divided equally on each long side of the room, approximately \(4\,\mathrm{m}\) above the floor and separated by \(4\,\mathrm{m}\); see Figure 5b. The infrastructure antennas were tilted approximately 45 degrees to obtain better coverage of the floor level area while strong reflections from the walls directly behind them were avoided. The free-space radiation pattern of the antennas is omnidirectional in cross section, but this will of course not be true as soon as it is attached to the metal structure and other objects close to its proximity. However, as previously mentioned in Section 4, we save and evaluate the _radio channel_, which is the wireless propagation channel influenced by the antenna radiation pattern. During the measurements, the facility was used as usual, with students working on projects and people moving around.
## 3 Proposed Scheme
### Distributed and Reliable Data Transmission: DRDT
#### Transmission Phase
In the process of transmitting the control packet, if the sender node overhears the data packet transmitted from the helper node, the sender node can transmit an ACK packet in advance, before receiving the control packet. This can prevent unnecessary energy consumption due to the control packet transmission. Table 2 shows the pseudo-code of the transmission phase. Figure 5: Transmission phase by helper node.
## 4 Results and Discussion
### Particle Flow  
Figure 15 illustrates the particle flow patterns within the industrial air classifier under the four different guide cone structures. It reveals that the classification chamber was where the majority of the fine and coarse particles were classified. As discussed in Section 4.1, the distribution of the airflow affected the flow of the particles and their classification. In particular, the lower position of the large vortex formed in the classification chamber resulted in better classification. Therefore, under the guide cone with a diameter of 808 mm, the classification ability for fine and coarse particles was the best in the classification chamber. Additionally, the fine particles that were transported downward through the guide cone continued to travel back into the classification chamber via the different vortices. Similarly, the coarse material cone functioned as a secondary classifier, similar to an air cyclone, intensively rinsing the coarse material with the secondary air resource before discharging to remove any remaining fines. However, the vortices formed in the guide cone and the coarse material cone differed in terms of their positions, shapes, sizes, and rotational directions under the different guide cones (as shown in Figure 3). This also affected the particle flow and secondary classification ability in these regions. Figure 15 clearly indicated that the particle flows in the coarse material cone under the different guide cones were different. The coarse material cone with \(d\) = 758 mm had a larger number of fine material particles (blue particles) than in the remaining cases. This meant that this guide cone (758 mm) had a weaker capacity for secondary classification. These effects are more clearly demonstrated in the following section.
## 3 Numerical Simulation and Experimental Analysis
### Crack Location  
#### 3.2.1 Numerical Analysis  
In this subsection, the influence of the location of the transverse crack to the propagation characteristics of the elastic waves is studied. Mode III is chosen for the aforementioned reason. The non-uniform shaft is still the same one but with different crack locations as listed in Table 1. Similarly, substituting all the parameters into Equation (3), the transfer matrix can then be derived. The propagation characteristics of the elastic waves are obtained in a numerical manner, and the results are shown in Figure 8.  
As can be seen in the figure, the results for different locations are indicated by different colors, and the real line and the dotted line of the same color are a pair of conjunct propagation constant. There are still three stopbands for each location. Obviously, the propagation characteristics are affected by the crack location. The stopbands are not signed out in the figure for conciseness, instead, all of which are listed in Table 3.  
It can be seen from Table 3 that the bandwidths of the three stopbands fluctuate with the location. For locations from \(0.1L\) to \(0.9L\), the bandwidth of the first stopbands change slightly compared with the other two stopbands. For the bandwidth of the second stopband, it will reach a maximum value when the crack locates at \(0.5L\). In addition, the bandwidth of the third stopband will be the widest  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Locations** & **Stopbands (Hz)** \\ \hline \(0.1L\) & [26; 148], [352; 592], [976; 1284] \\ \(0.2L\) & [30; 142; 384; 526], [926; 1194] \\ \(0.3L\) & [34; 124; 346; 516], [826; 1261] \\ \(0.4L\) & [36; 116; 316; 542], [848; 1220] \\ \(0.5L\) & [38; 106; 288; 562], [896; 1140] \\ \(0.6L\) & [40; 106; 278; 540], [842; 1226] \\ \(0.7L\) & [40; 112; 282; 502], [790; 1262] \\ \(0.8L\) & [42; 122; 290; 496; 798; 1188] \\ \(0.9L\) & [44; 136; 294; 544], [830; 1210] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical stopbands derived at different crack locations.  
Figure 8: Numerical propagation constants derived from different crack locations.  
for locations \(0.3L\) and \(0.7L\). The second and the third stopbands vary with the crack location more obviously than the first one.
## 3 Numerical Simulations and Results
### Expression of the Surface of MC  
Surfaces of optical component MC should be conical shaped. Although the tip of the rear (convex) face can be produced in practice with enough sharpness, the front (concave face) cannot be made in a conical shape without a non-transmissive region that could be even larger than 2 mm in radius. It seems that a conical (aspherical) shape with a rounded tip and curvature radius larger than 2 mm on the front face will also highly distort the wavefront. The solution we found was to choose a conical (aspherical) shape with a rounded tip with curvature radius 5 mm (for variation 1) and to correct the shape of the rear face using higher aspherical coefficients. We would like to mention that spherical concave lenses with curvature radius less than 5 mm are commercially available. [PERSON] and [PERSON] [31] determined the shape of the rear surface of a component, being given the equation for the front face, such that the component behaves as an axicon that tilts the rays with angle \(\beta\). We use these equations by setting \(\beta=0\) to determine the shape of the rear surface and then we fit the data using the aspherical lens (Equation (13)).  
\[z(r)=\frac{r^{2}}{R\Big{(}1+\frac{r^{2}}{R^{2}}\sqrt{1-(1+\kappa)}\Big{)}}+ \sum\alpha_{2n}\,r^{2n}. \tag{13}\]  
The variation from the target value of mode conversion efficiency of MC for the waves which travel near the optical axis is not essential, in the case of variation 1 all modes are helical; therefore, this will not produce intensity on the optical axis in the focal plane. On the other hand, in the case of variation 2, the mode without topological charge is necessary to form the top-hat shape. Equations for shapes of front/rear interfaces of MC for variation 1 and 2 are presented in Table 1; for completeness, we give also the expression of the front face of the two aspherical lenses used for simulation.  
The back surface of MC is designed for an aperture diameter of 26 mm, while the clean aperture is considered 23 mm in diameter. It is very likely that better designs are possible for both versions of MC, while what we present is more a proof of concept. Aspherical coefficients \(\alpha_{2n}\) have alternating signs and very high values, which means that they compensate for each other to produce a correction that is not easily described by a polynomial function. As a result, the designed surface presents small variations from the ideal surface, given by equations from Reference [31], which scatter the rays. The geometrical spot for both cases is still sub-wavelength. For version 1 of MC, we could not properly fit the ideal surface near the tip; therefore, the waves that pass through the central region are not focused on the optical axis. MC for variation 2 looks more like a meniscus lens, having a larger curvature radius; therefore, this problem was not present.
# Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
## 3 Altitude of UAV and Task Allocation Optimization
Therefore, UAV can adjust its flying altitude in time according to the change of the horizontal distance between itself and the user. We also find that the optimal task allocation ratio is proportional to the bandwidth allocated to the UAV by the user. Moreover, the transmit power of the user and the bandwidth allocated to the BS also affect the optimal task allocation ratio.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 6 Results and Discussion
### Experiment Results and Discussion
#### 6.3.4 Experiment 4: Disturbance Exceeding the Maximum Anti-Disturbance Capability of System
This also shows that when there are multiple critical paths to choose from, determining the critical path based on different critical paths leads to different \(y_{\max}\). In the control strategy of this paper, the critical path is adopted as a random selection strategy. As shown in Figure 22e, as the whole mission process advances, \(y_{\max}(t)\) shows a decreasing trend in general, and \(y(t)\) gradually approaches \(y_{\max}(t)\). When \(t=72\), \(y(72)>y_{\max}(72)\), which means that the output of the system has exceeded the maximum output that can be controlled by the system. As shown in Figure 22f, after the control quan  
Figure 22: Diagram of the mission process in the case with disturbance exceeding the maximum anti-disturbance capability of system. (**a–c**) illustrate the mission process at \(t=16\), \(t=17\), and \(t=41\), respectively. (**d–f**) illustrate the mission process at \(t=46\), \(t=72\), and \(t=73\), respectively. (**g,h**) show the mission process at \(t=81\) and \(t=115\), respectively. ity generated by the system when \(t=72\) acts on the system, there is still a system error when \(t=73\) although \(y(73)<y(72)\); the system has been unable to eliminate the system error completely. At \(t=73\), there is already \(y_{\text{max}}(73)=108<y_{e}\), and at this moment, the maximum acceptable output of the system is already smaller than the desired output, which means that the system has lost any anti-disturbance capability. Therefore, as shown in Figure 22g, at \(t=81\), the system has an error and the generated control quantity is 0. At this moment, the system, like the no-control strategy in Experiment 2, is unable to eliminate any error, and the error continues to accumulate with the generation of disturbances. As shown in Figure 22h, at \(t=115\), the system has accumulated a total of 5 min of error, resulting in a 5-min delay in the start of the E7 mission. Therefore, the decision maker of the process control should pay attention to the change in the maximum anti-disturbance capability of the system at all times and try to take the early intervention means to control the system output within the maximum acceptable output. Table 5 lists the control quantity generated by the system for each controllable moment. From the last row of Table 5, we can see that \(y(72)=5\) which is larger than the maximum value of the absolute value of any control quantities. It means that the output of the system has exceeded the maximum output that can be controlled by the system, leading to a system error that could not be completely eliminated.
# Advanced Technologies for Geosite Visualization and Valorization: A Review
### COVID-19 and the Earth Sciences  
During the COVID-19 pandemic, the closure of educational institutions all over the globe especially impacted the teaching of geosciences, which usually involves excursions in which numerous student groups participate. Furthermore, geologists often need to conduct field surveys in foreign countries for research purposes. Moreover, just like scientists from other fields, geologists also need to take part in meetings and conferences. With the purpose of coping with COVID-19-related circumstances, the possibility of sharing research and teaching resources on the web became paramount, and is becoming increasingly important also nowadays, even though the peak of the pandemic has passed. Given the above, virtual reality techniques can play a special role, as they are devoted to facilitating 3D visualization of Earth Sciences [58]; virtual landscapes are based on open geospatial datasets [59], and digital terrain/surface models along with bathymetric data [60].  
The present work shows several examples of geosite visualization and promotion by means of cutting-edge, innovative techniques, among which the creation of virtual geosites, which can be put at disposal of the public owing to advanced methods involving Unmanned Aerial Vehicles (UAVs) as well as Structure for Motion (SfM) methodologies.
## 2 Materials and Methods
### Methodology
#### 2.3.1 Scenario-Based Flood Risk Analysis Framework
3.2 Historical Inundation Scenarios with Sentinel-1 Data preprocessing techniques, such as filtering and denoising, were applied to the images available for the PLEEZ starting from 2015. Significant floods in Poyang Lake typically occurred in July and underwent a months-long process from lake dryness to a rapid increase in water levels, followed by a gradual recession [37; 38; 67]. To determine the areas with maximum potential for inundation that may cause losses, we systematically analyzed each available image from May to September annually. By following the approach of [PERSON] et al. [40], who studied the 2020 catastrophic flood in Poyang Lake using Sentinel-1  
Figure 3: A technical flowchart of flood risk analysis framework using the PLEEZ as the illustrative case. data, we further divided the full lifecycle of a flood into the following four distinct stages to accurately extract inundation: (1) lake rising stage, (2) flooding stage, (3) flood receding stage and (4) stable stage. The final certified inundation scenario was determined by summing the increased water area during the flooding and flood receding stages. Regarding the identification of inundation areas with GEE, we employed the change detection procedure provided by the Office for Outer Space Affairs UN-SPIDER Knowledge Portal [68]. The procedure is based on analyzing the pixel changes between two time-adjacent images before and after the flood. \[F(x)=\left\{ \begin{array}{cc}0\ (nonwater)&x\leq T\\ 1\ (water)&x>T\end{array} \right. \tag{1}\]  
where \(x\) represents the raster result indicating the degree of change per pixel, obtained by dividing the post-flood image by the pre-flood image; \(T\) denotes the threshold value used to distinguish the water body, with a typical recommendation being 1.25; and the binary raster \(F(x)\) generated with this process illustrates the potential flood extent. The final inundation range is identified by overlaying the flood extents of \(F(x)\) in stages (2) and (3). The threshold value of 1.25 is an empirically derived standardized threshold, selected through trial and error [68], that demonstrates optimal recognition performance [69]. This rapid change detection method, along with the threshold selection of 1.25, was successfully validated and applied in recent inundation extraction studies [70; 71]. The runnable code on GEE to reproduce this process is provided in Part A of Appendix A.