esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 2 Method
### Route Optimization Establishment
#### 2.5.2 Route Optimization  
In terms of the updated global throughput, we use the backward UGVs to conduct the optimal route selection among the candidate routes considered. When a forward UGV signal arrives at the DT, it is turned into a backward UGV if the delay of transmission meets the requisite Dth (threshold delay); otherwise, it is discarded straight. This feedback UGV transports an intersection list before returning to the ST through the same but reverse path, as the comparable forward UGV. When the backward UGV arrives at a specific li, it first calculates the latest global throughput \(LGT_{ij}\) using the transported throughput \(GT_{ij}\)[29; 30].  
\[GT_{ij}\leftarrow(1-\sigma)\cdot GT_{ij}+\sigma\,LGT_{ij} \tag{12}\]  
\[GT_{ij}(i+T_{eva})\,\begin{cases}\eta\cdot GT_{ij}(t)\,\,if\,GT_{ij}(t)>\tau_{ min}\\ \tau_{min}\,\,Otherwise\end{cases} \tag{13}\]  
where:  
\(\sigma\): is the weighting parameter \((0,1)\). Obviously, this throughput updating procedure may prevent the routing path exploration stagnation and mitigate the influence of the \(LGT_{ij}\)'s immediate value. Following that, \(GT_{ij}\) is refreshed in the field of this backward ant's updated global throughput.  
Once all reverse UGVs are at TIS, the \(F(y)\) values of all possible candidate routes are compared, and the route with the highest \(F(y)\) value is chosen as the best. The ST then sends a positive routing message to the source vehicle to begin the data packet forwarding.
## 4 Analysis of Numerical Calculation Results
### Interlayer Soil Response  
In order to study the seismic response properties of interlayer soil between the upper and lower pipes, we keep the space position of the lower layer of pipe jacking fixed and the upper layer of pipe jacking moving up and down, four working conditions with different thicknesses of the soil interlayer are set, that is, t = 0.25 D, 0.5 D, 1.0 D and 1.5 D (t is the thickness of the soil interlayer and D is the outer diameter of the pipe jacking, which are shown in Figure 1b). The other parameters of the numerical model remain unchanged.  
Figure 10 shows the distribution of the effective plastic strain of the soil between double-layer pipe jacking with different soil interlayer thicknesses. The plastic zone strain distribution reflects the stability of surrounding soil, which will induce pipe failure.  
Figure 10: Effective plastic strain distribution of soil between vertical double layer pipe jacking with different thickness of soil interlayer: (**a**) 0.25 D; (**b**) 0.5 D; (**c**) 1.0 D; (**d**) 1.5 D.  
As the thickness of interlayer soil is reduced from 1.5 D to 0.25 D, the spacing ofpipe jacking is gradually reduced and the plastic area of the surrounding soil is continuously increased and connected into integration. The range of the plastic zone of the interlayer soil within the thickness of 0.25 D to 0.5 D is obviously connected as a whole, which causes the interlayer soil to be more vulnerable and further increases the horizontal and vertical displacement of the upper pipe. Finally, this will seriously damage the pipe. The heavier failure phenomena induced by the mutual effect of the upper and lower pipes can be defined as a small spacing earthquake failure effect. When the thickness of the interlayer soil is 1.0 D, the effective plastic strain areas around the pipe are relatively smaller and few are connected, which has no aggravating effect on pipe failure. When the the interlayer soil increases to 1.5 D, the obvious plastic zone range only appears in the lower tubular vault. The seismic response of the interlayer soil is no longer affected by the upper and lower pipes mutual effect.  
Because the lower pipe jacking is semi-embedded in the rock, the plastic zone of the soil is mainly concentrated on the crown of the pipe jacking, which shows under the earthquake vibration the soil above the pipe will become loose and deformed, damaging the overall stability of the pipe jacking. Therefore, when the clear spacing is less than 1.0 D, some appropriate measures such as vault grouting should be taken to strengthen the arch soil within the range of 120\({}^{\circ}\) to resist earthquakes.
###### Abstract  
MgO is a kind of high secondary electron yield (SEY) material with important applications in electron multipliers. MgO coatings can be used as the electron emission layer for multiplier dynode to improve the electron gain significantly. However, the SEY investigation on ultrathin MgO coatings is not complete and needed to be supplemented urgently. In this work, a series of MgO coatings with increasing thickness were prepared by atomic layer deposition. SEY properties and energy spectra were characterized, and the effect of coating thickness on SEY was systematically analyzed. Experimental results show that SEY of MgO/Si samples rises as the coating thickness increases. Merely, SEY almost does not change with the coating thickness when the thickness exceeds 30 nm. Then, a SEY semi-empirical theory was employed to interpret the SEY regularities of MgO coatings by regarding the coating samples as ideal double-layer structures. Theoretical calculation quantitatively explained the SEY variation observed during the experiments, and further quantified the SEY contribution level of top coating and bottom substrate for the 1 nm and 20 nm MgO coatings. The work is of great significance for comprehending the SEY of ultrathin MgO coatings and expanding the applications of nanoscale coatings with high SEY.  
s pacs: secondary electron emission; magnesium oxide; double-layer structure +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Cell Adhesion  
MC3T3-E1 pre-osteoblastic cells (ATCC 7594, BCRJ, Rio de Janeiro, RJ, Brazil) were used to evaluate the cellular responses to the different disc surfaces. The cells were routinely cultured with 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C on \(\alpha\)-Minimum Essential Medium (\(\alpha\)-MEM, Sigma, USA) supplemented with 10% FBS (Gibco(tm), Thermo Fisher Scientific, Boston, MA, USA). The MC3T3-E1 cells (density of 2.0 \(\times\) 10\({}^{3}\)) were seeded over the HA, c-RGD/HA, and p-RGD/HA samples and incubated for 24 h in \(\alpha\)-MEM, with and without 10% FBS. After this period, the samples were fixed in 4% paraformaldehyde (PFA) at room temperature. The cells were washed with phosphate-buffered saline (PBS) pH = 7.4, permeabilized with 0.1% Triton X-100, stained with Rhodamine-Phalloidin (Alexa Fluor(r) 546, Life Technologies, Thermo Fisher Scientific, Boston, MA, USA) for 30 min, and mounted on glass slides containing DAPI/DABCO(tm) (Fluoroshieldtm, Sigma-Aldrich, St. Louis, MO, USA). The images were taken using an Axio Observer A1 inverted fluorescence microscope (Zeiss, Germany). The cell numbers and the cytoplasmic spreading area were quantified using 10 images per sample with Image-Pro Plus 6.0 (IPP6) software. Three experiments were performed, resulting in 30 images per sample.  
For qualitative evaluation via scanning electron microscopy (SEM), the samples were fixed in Karnovsky's solution, washed in a cacodylate buffer, and postfixed in 1% osmium tetroxide (Sigma-Aldrich, St. Louis, MO, USA). After three washes with the cacodylate buffer, the samples were dehydrated in increasing concentrations of ethanol (30, 50, 70, 90, and 100%), critical point dried, coated with gold, and observed at 15 kV, with secondary electron imaging by SEM (TESCAN VEGA3, TESCAN, Brno, Cechia) for the cell morphology evaluation.
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 2 Related Works
Another interesting scheme is based on a decentralized trust model called the web of trust, where each user has a ring with a group of public keys that it trusts. The authors of [12] propose an efficient public key management scheme that is suitable for fully-self-organized MANETs, where all nodes play identical roles. Finally, a certificate-less alternative for PKIs is identity-based cryptography, where the public key of each user is some unique piece of information related to its public identity, so that public-key certificates are unnecessary. The main disadvantage of asymmetric cryptography is the high computational complexity of the schemes, since most public-key cryptosystems are too heavy to be used in lightweight environments like MANETs in IoT. To solve this issue, this paper proposes a combination of symmetric and asymmetric cryptography to allow the use of secret session keys in these environments. In particular, the proposal offers both strong authentication of legitimate nodes through open communications and the exchange of secret keys shared between pairs, which can be used as session keys. Node authentication is here performed using an approach based on the idea of zero-knowledge proof (ZKP) [13], which defines a method to prove the knowledge of a certain piece of information without revealing anything about it. Typical ZKPs are based on several challenges and responses, involving a successive exchange of messages, which implies the need to have a stable and continuous connection between nodes [14]. However, this assumption is impossible in a volatile environment like IoT, where sometimes, devices move at a high speed, such as, for example, smart vehicles. In these cases, a massive exchange of messages to run a typical ZKP can be infeasible due to possible connection failures during the protocol. In order to deal with this problem, the idea of non-interactive ZKP (NIZKP) has emerged in the literature [15; 16]. In an NIZKP, all of the challenges of a typical ZKP are condensed into a single package sent in a single message. This leads to the optimization of the time necessary for the exchange of messages, so that just a single message is necessary, and even this message can be sent as a beacon in broadcast mode. The work [17] shows a method that transforms an interactive protocol into a non-interactive protocol, which can be applied to turn interactive ZKPs into NIZKPs thanks to the use of hash function. Besides, on the one hand, as a general theoretical result, the authors of [18] present the first NIZKP for NP whose construction is based on one-way permutations and certified trapdoor permutations [19]. On the other hand, as a specific practical result, the work [20] shows an NIZKP of the Hamiltonian cycle problem, which can be used with the proposal described here.
## 3 Result and Discussion
### Characterization
#### 3.1.1 Phase Composition and Morphology
The smaller crystallite size in the material synthesized at 70 \({}^{\circ}\)C after its calcination at 500 or 600 \({}^{\circ}\)C compared to the material synthesized at 40 and 100 \({}^{\circ}\)C might indicate that a larger number of new anatase crystallites are formed in the TiO\({}_{2}\) synthesized at 70 \({}^{\circ}\)C during calcination than in the TiO\({}_{2}\) samples synthesized at 40 or 100 \({}^{\circ}\)C.  
Figure 2 shows SEM images of the titanium dioxide sample synthesized at 70 \({}^{\circ}\)C and those materials obtained after calcination at 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, or 600 \({}^{\circ}\)C. The shape and size of the synthesized titanium dioxide structures are largely irregular, and the samples consist of hierarchical structures that are composed of agglomerated TiO\({}_{2}\) particles. Furthermore, the SEM images show no significant changes in particle morphology after calcination. The rough surface of the particles appears smoother after calcination and the material calcined at 500 \({}^{\circ}\)C or 600 \({}^{\circ}\)C seems to have a higher density. EDX spectra (Figure S2) show the presence of titanium (Ti) and oxygen (O) in all samples. Ti and O were the only elements present in uncalcined TiO\({}_{2}\) and in the material calcined at 400 \({}^{\circ}\)C. Samples calcined at 500 \({}^{\circ}\)C or 600 \({}^{\circ}\)C show the presence of a small amount of sodium, probably resulting from the neutralization step. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Conditions**} & **Synth. at 40 \({}^{\circ}\)C** & **Synth. at 70 \({}^{\circ}\)C** & **Synth. at 100 \({}^{\circ}\)C** \\  & **Crystallite Size (nm)** & **Crystallite Size (nm)** & **Crystallite Size (nm)** \\ \hline Uncalcined & 7 & 6 & 5 \\ Calcined at 400 \({}^{\circ}\)C & 8 & 8 & 7 \\ Calcined at 500 \({}^{\circ}\)C & 14 & 11 & 12 \\ Calcined at 600 \({}^{\circ}\)C & 33 & 26 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Primary crystallite size of the anatase phase (crystalline size was estimated from the (1 0 1) reflection). Figure 2: SEM images of TiO\({}_{2}\) synthesized at 70 \({}^{\circ}\)C (**(A)** uncalcined sample, (B) calcined at 400 \({}^{\circ}\)C, (C) calcined at 500 \({}^{\circ}\)C, and (D) calcined at 600 \({}^{\circ}\)C).
# Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing
## 6 Conclusions  
The resolution of the basalt flow structure was improved by the use of optimized time- and depth-domain data processing of offshore seismic data regarding the use of cap rocks for CO\({}_{2}\) storage. Time processing yielded a subsurface image section with an improved signal-to-noise ratio by attenuating multiples and noise. Depth processing refined the velocity distribution so that the depth of the basalt flow was corrected by the lateral and  
Figure 12: (**a**) Overlapping tops of the volcanic mounds before (Figure 11a) and after (Figure 11b) the depth processing. (**b**) The contour map represents vertical differences between Figure 11a,b.  
vertical axis. Depth-domain processing used the MVA technique to update the velocity by the iteration of the travel-time calculation. Flattened CRP gathers qualitatively proved that the results of the processing approached the exact depth of the basalt flow. To quantify the accuracy of MVA, we confirmed that the velocity error stably decreased until third iteration. The slice map showed the aspect of basalt intrusion in detail and suggested that the cap rock was located at a greater depth of approximately 32.5-60 m compared with when only time-domain processing was applied. Comprehensive seismic data processing including both the time and depth domains is required when a relatively deep subsurface structure resulting from volcanic activity is to be imaged.  
Conceptualization, S.C. and M.K.; methodology, W.S. and S.C.; software, W.S. and C.L.; validation, W.S., S.C. and M.K.; investigation, S.C.; data curation, S.C. and M.K.; writing--original draft preparation, W.S., S.C. and C.L.; writing--review and editing, W.S., S.C. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Institute of Geoscience and Mineral Resources (KIGAM), grant numbers 21-3413-1 (Technology development for storage efficiency improvement and safety assessment of CO\({}_{2}\) geological storage) and 21-3312-1 (Development of the integrated geophysical survey and real-scale data processing technologies for 3D high-resolution imaging of the marine subsurface).  
Not applicable.  
The software Generic Mapping Tools was used to plot the map of the study area [33].  
The authors declare no conflict of interest.
###### Abstract
These models showed better prediction compared with selected empirical models and field measured data [12]. Lastly, path loss prediction based on ANN and the measurements at 189.25 MHz and 479.25 MHz provided the best ANN parameters and minimal error [13]. The abovementioned studies showed that RSSI varies according to environment and uncertainty, such as large-scale distance change, antenna height, tall grass, weeds, and blockages between nodes. These make the RSSI fade, resulting in delayed or low-quality communications. The present study applies the fuzzy theory to create a model for path loss prediction which corresponds to the nature of the RSSI uncertainty in different environments. This model is referred to herein as \"multi-boundary fuzzy linear regression\" (MBFLR). It consists of a center line, two upper boundary lines, and two lower boundary lines that cover almost all the RSSIs in the coverage areas. These components make the proposed model provide limited interference in the upper boundary and lowest RSSI for interruption. The proposed model defines the WSN characteristic in an agricultural farm through ML. Moreover, it can predict the RSSI under uncertainty fading with accuracy and can be applied to similar areas. The following contributions are made to address the previously indicated issues:  
1. A ML path loss model with RSSI fluctuation boundaries is proposed using MBFLR based on the measurements in different environments for short grass, tall grass, weeds, and blockage. 2. A breakpoint distance optimization is proposed for accurate prediction. 3. The measured RSSI data are captured using Lora LPWAN at 433 MHz for different environments. The remainder of this paper is structured as follows: Section 2 presents the related work; Section 3 proposes the MBFLR model; Section 4 demonstrates the experimental setup; the results are depicted in Section 5; the discussion and description are in Section 6; and finally, the conclusion and future work are presented in Section 7.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 4 Collision Avoidance System  
### Finite Control Set Model Predictive Control of the CAS  
Based on the USV model, the FCS-MPC for the CAS predicts the possible trajectories by the finite control behaviors (finite control set), and then judges all these trajectories by the predefined evaluation function to select an optimal control behavior. Figure 9 illustrates a block diagram of the FCS-MPC for the CAS.  
The design process of the system is as follows: The USV prediction model \(f_{p}\left\{X,S\right\}\) first needs to be established, and can predict the USV's state quantity \(X\) in response to the different control behaviors \(S\). At time \(t_{k}\), by the USV prediction model, the predictive values \(X_{pi}(t_{k+j})\) of the state quantity under the action of \(S_{i}\) can be calculated based on the current state quantity \(X(t_{k})\), i.e., \(X_{pi}(t_{k+j})=f_{p}\left\{X(t_{k}),S_{i}\right\}\), \(i=1,2,\cdots,c\). \(c\) is the number of all valid control behaviors of USV, \(j=1,2,\cdots,t_{p}\), and \(t_{p}\) is prediction horizon. Based on the system characteristics concerned, such as attainability, safety, stability, and rapidity, the evaluation function \(f_{e}\left\{X_{pi},S_{i}\right\}\) is constructed. Finally, the selected control command \(S_{o}(o\in\left\{1,\cdots,c\right\})\) that makes the function \(f_{e}\) optimal is used to control the USV.  
Figure 8: Steps of the tunable fast marching square (TFMS) method in a real sea area. (**a**) Electronic chart of the sea area; (**b**) Initial binary map; (**c**) Expanded binary map; (**d**) Potential map \(V\) with \(Saturation\) 0.9; (**e**) Arrival time matrix \(T\); (**f**) Path obtained after applying gradient descent on \(T\).
## 6 Real-Time Monitoring and Fault Diagnosis Technology
### Failure Prediction and Diagnosis  
With the rapid development of computer technology, big data and artificial intelligence technology, the fault prediction and diagnosis technology of mechanical equipment is also developing in the direction of intelligence. [PERSON] et al. [107] proposed a method combining the least square support vector machine (LSSVM) and hidden Markov model (HMM) into an artificial fish swarm algorithm for fault prediction, in order to solve the problem that the fault of the LHD electrical system cannot be predicted accurately by the single traditional diagnosis method. It was found that the accuracy rate of the new combined method could reach 91.1%, accurately predicting the failure and the change trend in the electrical system for the hybrid scraper. Caterpillar's intelligent information system (Cat productink) could help the customers monitor and manage the equipment in real time and also predict potential faults by collecting key performance indicators and running data from the excavators. The Clear Sky system in JLG of America could accurately find the fault point, guiding the maintenance man to go directly to the site. It also had the ability to enter the monitoring system to find and eliminate faults, effectively shortening the maintenance time and costs.  
To sum up, the technology of real-time monitoring and fault diagnosis for the underground intelligent LHD still faces the following problems:  
1. The data for the underground LHD real-time status are not fully utilized. Excavation on the collected data is not deep enough for fault prediction and diagnosis.
2. Fault prediction and diagnosis are mainly targeted on the engine and hydraulic system of the scraper and few studies have been conducted on other systems.
3. Even deep learning has attracted the attention of many researchers as a new method in the field of intelligent fault diagnosis, though few studies have been conducted on fault diagnosis for LHD to date.
## 2 Related Work
### Deep Learning-Based Methods  
With robust fitting abilities and flexible architectures, neural networks have proven effective in learning dust removal from extensive datasets. [PERSON] et al. [18] proposed a convolutional neural network (CNN)-based sandstorm image enhancement method that integrated a color restoration subnet with a dust removal subnet. [PERSON] et al. [7] established a benchmark for the training of CNNs and the assessment of algorithm performance in sandstorm image reconstruction, utilizing the Pix2 Pix network for validation. To address the feature inconsistency between Transformers and CNNs, [PERSON] et al. [8] developed a hybrid framework for effective sandstorm image restoration. These methods leverage the atmospheric scattering model to synthesize training datasets, yielding excellent results under specific conditions. However, the generalization capabilities of these methods require further validation. To mitigate the dependence on paired images, [PERSON] et al. [19] proposed a two-stage sandstorm image restoration method based on style transfer and an unsupervised sand-dust image restoration network (USDR-Net). [PERSON] et al. [9] introduced a cycle-consistent generative adversarial network for image de-dusting (D-CycleGAN). However, it struggled with severely degraded sandstorm images. Recognizing the intrinsic differences between sandstorm and haze images, [PERSON] et al. [10] developed an unpaired learning algorithm for image de-dusting based on Retinex with GANs (DedustGAN). While it effectively removes most dust from images, blurring artifacts are present, especially in the sky regions. More recently, several multi-task integrated frameworks have been proposed [20,21], but their performance remains constrained by the dataset's quality and the complexity of the models.