esa-sceva/satcom-chunk-collection huggingface dataset:
# Corrosion Behavior of Zn-Al-Mg-Si Coatings in Sulfur Dioxide-Containing Environment
## 3 Results and Discussion
### Corrosion Resistance Determined via Sulfur Dioxide Test with General Condensation of Moisture  
The corrosion resistance of Zn-Al-Mg-Si coatings was determined comparatively on the basis of mass changes during the sulfur dioxide test with general condensation of moisture. The comparison coating was conventional zinc hot-dip coating. The relationship between mass changes and the test duration is shown in Figure 4a. The graph shows that the Zn-Al-Mg-Si coating is characterized by an initial mass increase. Then, its weight decreases. Comparative Zn coating shows a weight loss from the beginning of the corrosion test. The mass loss of Zn coating is relatively rapid and much more intense than that of Zn-Al-Mg-Si coating. After 30 cycles of exposure in the Koesternich chamber, the average specific mass loss of Zn-Al-Mg-Si coating was 12.63 \(\pm\) 2.32 g/m\({}^{2}\), while for Zn coating, it was 26.27 \(\pm\) 3.29 g/m\({}^{2}\). The dissolution intensity of Zn-Al-Mg-Si coating was approximately two times lower than the comparative Zn coating.  
The appearance of the coating surfaces after 30 cycles of testing in the Koesternich chamber is shown in Figure 4b. After completing the corrosion test, it can be stated that the tested coatings remain continuous and have not penetrated the substrate. The thicknesses of the coatings measured before starting the corrosion test were 43.27 \(\pm\) 3.2 \(\upmu\)m, respectively, for Zn-Al-Mg-Si coating and 69.23 \(\pm\) 4.6 \(\upmu\)m for Zn coating. The thickness of the coatings determines whether the protection remains effective in this corrosion test. Distinct grains are visible on the surface of Zn coating, while the appearance of Zn-Al-Mg-Si coating after the corrosion test is much grayer and matte (Figure 4b). The microstructure of Zn coating  
Figure 3: SEM images of a cross-section of Zn-Al-Mg-Si coatings: (**a**) general view of the coating; (**b**) outer layer; (**c**) Zn/MgZn\({}_{2}\) eutectics; (**d**) diffusion layer.  
(Figure 4c) and the comparison of the morphology of its structural components indicate the typical structure of the phases of the Fe-Zn system: outer layer of \(\eta\) phase (solid solution of Fe in Zn) and diffusion layer of \(\delta\)1, \(\zeta\) intermetallics [3]. The presence of grains on the Zn coating surface after the corrosion test confirms that the corrosion wear occurred in the outer layer of coating. Taking into account that the weight loss of Zn-Al-Mg-Si coating was smaller, it can be concluded that corrosion wear also occurred in the outer layer of this coating.
## 2 Materials and Methods
### Volatile Compound Analysis  
The volatile compounds of the douchi were tested using a Shimadzu QP2020 NX gas chromatography-mass spectrometer (GC-MS, Shimadzu, Kyoto, Japan). Sample pre-treatment was conducted based on the method described by [PERSON] [16], with minor modifications. Douchi samples (3 g) were put into 30 mL headspace vials, an internal standard (1,3-dichlorobenzene, 100 \(\upmu\)g/mL, 100 \(\upmu\)L) was added, and the headspace vials were immediately sealed. Solid-phase micro-extraction (SPME) was performed at equilibrium at 60 \({}^{\circ}\)C for 20 min, adsorption was performed at 60 \({}^{\circ}\)C for 30 min using a 50/30 \(\upmu\)m DVB/CAR/PDM fiber (Supelco, St. Louis, MO, USA), manual injection and desorption were performed at 250 \({}^{\circ}\)C for 5 min.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Location** \\ \hline DC-1 & Chongqing \\ DC-2 & Chongqing \\ DC-3 & Chongqing \\ DC-4 & Sichuan \\ DC-5 & Chongqing \\ DC-6 & Sichuan \\ DC-7 & Chongqing \\ \hline \hline \end{tabular}
\end{table}
Table 1: The locations of the seven samples.  
Chromotographic conditions: The volatile compounds were separated by an SH-Rxi-5 Sil MS column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m; Shimadzu, Kyoto, Japan). Helium was used as a carrier gas with an average linear velocity of 1.0 mL/min. The injector temperature was 230 \({}^{\circ}\)C. The column temperature was initially 40 \({}^{\circ}\)C and was held for 3 min. Then, the temperature was increased to 105 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min. Then, the temperature was increased to 150 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min. Finally, the temperature was increased to 240 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and maintained there for 15 min.  
Mass spectrometry conditions: The electron ionization (EI) was set at 70 eV. The ion-source temperature was 250 \({}^{\circ}\)C. The scanning mode was the full-scan mode (SCAN), with a mass-scanning range that was configured from \(m/z\) 35 to 500.  
The volatile compounds were preliminarily identified by comparing their mass spectra with the mass spectral library (NIST 2017), which was at least 80%, and matching the retention indices (RIs), retention times, and mass spectra, which were calculated for n-alkanes (C7-C40, Shanghai, China). Each experiment was repeated three times.  
The components were quantified according to the semi-quantitative method. The formula for calculating the concentration of the flavor compounds is as follows:  
\[\mathrm{W}_{i}=\frac{A_{i}\times W_{0}}{A_{0}} \tag{1}\]  
where \(\mathrm{W}_{i}\) is the sample's mass concentration, \(A_{i}\) is the sample's peak area, \(W_{0}\) is the standard mass concentration, and \(A_{0}\) is the standard peak area.  
The odor activity value (OAV) was determined by dividing the concentration of a volatile compound by its perception threshold.
# MSSP: A Blockchain Sharding Protocol Based on Multi-Shard Storage
## 5 System Analysis
### Security Analysis
#### 5.1.2 Protocol Security  
After the blocks in MSSP are added to the chain, they will be sorted according to the priority of the consensus zone. During the sorting process, the system will discard conflicting blocks submitted by malicious nodes by comparing priorities, to defend against double-spending attacks. To prevent malicious nodes from predicting the priority of the consensus zone in advance, MSSP dynamically adjusts the priority of the consensus zone after each block submission. This flexible adjustment of priorities ensures fairness within the system.  
Upon completion of cross-shard transactions, relevant nodes are required to update their data. The leader initiates a request to update the data for the nodes. Once the node has completed its data update, it will probabilistically return a proof of completion based on its own recent four rounds of updates. This probabilistic approach to returning proofs helps prevent malicious nodes from evading system monitoring. Nodes that consistently fail to complete updates promptly will be deemed as faulty and will no longer be able to participate in the consensus process. This method of restricting nodes to actively maintain data consistency enhances the overall security and reliability of the blockchain network.  
The security and liveness of the MSSP system depend not only on the design of the protocol but also on the underlying PBFT consensus algorithm. In PBFT, if the total number of nodes is \(3f+1\), there can be at most \(f\) malicious nodes, and there must be at least \(2f+1\) honest nodes in the system to achieve consensus. Therefore, if the number of malicious nodes in each shard does not exceed \(\frac{1}{3}\) of the total number of nodes, protocol security can be guaranteed.
## 5 Two-Way Measurement Method in CCFD
### Key Errors of the Two-Way Measurement Method in CCFD
#### 5.3.3 Time Delay of Periodic Relativistic Effects  
Under the influence of special relativity, satellite clocks exhibit a nominal frequency offset (NFO) and periodic relativistic effects. The NFO can be eliminated by adjusting the clock frequency before transmission, and the correction formula can be expressed as follows [67]:  
\[\Delta f_{NFO}=\frac{GM}{c^{2}}(\frac{1}{R_{e}}-\frac{3}{2a}) \tag{44}\]  
where \(\Delta f_{NFO}\) is the NFO in Hz, and \(a\) is the satellite orbit semi-long axis in m. The periodic relativistic effects can be calculated and corrected using the following formula [68]:  
\[\begin{array}{l}\tau_{ij}^{rel,p}=\frac{2}{c}(\tau_{i}(t^{s1})\mathbf{v}_{i}(t^ {s1})-\mathbf{r}_{j}(t^{r1})\mathbf{v}_{j}(t^{r1}))\\ \tau_{ji}^{rel,p}=\frac{2}{c}(\tau_{j}(t^{s2})\mathbf{v}_{j}(t^{s2})-\mathbf{r}_{i}(t^ {r2})\mathbf{v}_{i}(t^{r2}))\end{array} \tag{45}\]
# Jamming Analysis between Non-Cooperative Mega-Constellations Based on Satellite Network Capacity
## 3 Satellite Network Capacity Calculation Method
### Transmission Rate
\begin{split} R_{(i,j)}^{(i,j-1)}&=\left\{\begin{array}[ ]{l}\sum\limits_{k=0}^{\frac{M-2}{2}}\left(\frac{M-1}{2}-k\right)\hat{R}_{i,(j +k)\bmod M,t}^{\
earrow},M=2n\\ \sum\limits_{k=0}^{\frac{M-1}{2}}\left(\frac{M-1}{2}-k\right)\hat{R}_{i,(j+k) \bmod M,t}^{\
earrow},\text{otherwise}\end{array}\right.\\ \\ R_{(i,j)}^{(i+1,j)}&=\left\{\begin{array}{l}\sum\limits_{k=0}^{\frac{N-2}{2 }}\left(\frac{N-1}{2}-k\right)\hat{R}_{(i-k)\bmod N,j,t}^{\
earrow},N=2n\\ \sum\limits_{k=0}^{\frac{N-1}{2}}\left(\frac{N-1}{2}-k\right)\hat{R}_{(i-k) \bmod N,j,t}^{\
earrow}\text{otherwise}\end{array}\right.\end{split}  \tag{12}\]\[R_{(i,j)}^{(i-1,j)}=\left\{ \begin{array}{l}\frac{N-2}{2}\left(\frac{N-1}{2}-k \right)\tilde{R}_{(i+k)\bmod N,j,t^{\prime}}^{\bigotimes}N=2n\\ \sum\limits_{k=0}^{N-1}\left(\frac{N-1}{2}-k\right)\tilde{R}_{(i+k)\bmod N,j,t ^{\prime}}^{\bigotimes},otherwise\end{array} \right. \tag{14}\]  
Therefore, the transmission rates of intra-plane ISLs \(R_{o}\) and inter-plane ISLs \(R_{h}\) are, respectively,  
\[R_{o}=\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{M-1}\left(R_{(i,j)}^{(i,j+1)}+ R_{(i,j)}^{(i,j-1)}\right) \tag{15}\]  
\[R_{h}=\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{M-1}\left(R_{(i,j)}^{(i+1,j)}+ R_{(i,j)}^{(i-1,j)}\right) \tag{16}\]  
The total transmission rate \(R\) of \(\Psi_{A}\) is  
\[ \begin{array}{l}R=R_{o}+R_{h}=\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{M-1} \left(R_{(i,j)}^{(i,j+1)}+R_{(i,j)}^{(i,j-1)}\right)\\ \hskip 14.226378 pt+\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{M-1}\left(R_{(i,j )}^{(i+1,j)}+R_{(i,j)}^{(i-1,j)}\right)\end{array}  \tag{17}\]
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation
### Verification of the Bottom Non-Singular Sliding Mode Guidance Law
#### 5.2.3 Performance of the NSMG Law with Different Initial Leading Angles  
The initial leading angles are set to 20, 40, 60 and 80 deg., respectively. The designated impact time is set to 45 s. Other parameters are the same as the parameters used in Section 5.2.1. Simulation results are shown in Figure 6.  
The legends represent different simulation situations. For example, \"\" represents the simulation results obtained by using the NSMG law when the initial leading angle is 20 deg.  
It can be seen from Figure 6a that for different initial leading angles, including the case of large initial leading angle, the UAV can reach the target in a designated time. It can be seen from Figure 6b,c that the leading angle increases in the initial stage to extend the flight time and reduce the impact time error. When the designated impact time is fixed, the larger the initial leading angle is, the smaller the initial impact time error will be, the faster the convergence speed of the impact time error will be, and the smaller the corresponding acceleration command will be. When the impact time error converges to 0, the UAV will fly with pure proportional guidance. When it reaches the target, the relative distance between the UAV and the target is 0, the leading angle is 0, and the acceleration also converges to 0.  
Figure 5: Simulation results under different designated impact time.
## 4 Geometrical Analysis
### Macrogeometrical Deviations
Conversely, specimens with \(D\) = 14 mm exhibit the highest \(STR\) deviations, with results that suggest significant axial curvature introduced during the machining process. When compared with the FFF reference parts, the improvements brought by subtractive post-processing are also evident for \(STR\). Although the high-quality printed specimen achieves acceptable straightness, the machined specimens generally outperform it in terms of geometric accuracy. The low-quality FFF specimen shows considerably poorer \(STR\), demonstrating the limitations of additive-only fabrication for precision applications. The analysis of \(CYL\) (Figure 9c), which integrates both \(RON\) and \(STR\) deviations, provides a comprehensive view of the geometric fidelity achieved through turning. Among the three macrogeometric parameters evaluated, \(CYL\) is the most sensitive to variations in cutting conditions and specimen rigidity because it reflects deviations in both radial and axial directions simultaneously. The results show a clear reduction in \(CYL\) deviation with increasing \(v_{c}\). Specimens machined at \(v_{c}\) = 200 m/min generally achieve better cylindricity compared with those machined at \(v_{c}\) = 50 m/min due to the more continuous and stable material removal at higher speeds. This effect is particularly significant in parts with higher slenderness, where tool deflection and dynamic instability are more likely to induce form errors. Regarding the \(f\) values, increasing \(f\) from 0.05 mm/rev to 0.2 mm/rev results in an increase in \(CYL\) deviation across most diameters. This is explained by the higher mechanical loading and surface disruption associated with greater uncut chip thickness, which amplifies both \(RON\) and \(STR\) deviations and therefore degrades overall cylindricity. As with the other parameters, the specimen diameter plays a decisive role. Smaller diameters (\(D=14\) mm) exhibit the highest _CYL_ deviations, confirming the limited structural stiffness of these parts to resist the compound effects of cutting forces. Larger specimens (\(D=18\) mm) consistently show lower _CYL_ deviations, benefiting from greater geometric stability and reduced deflection during machining. In comparison with the FFF reference parts, machining substantially improves cylind-dricity. Even the high-quality printed specimen shows a noticeable _CYL_ deviation when compared with the turned surfaces. The low-resolution FFF specimen exhibits the poorest _CYL_ values, illustrating the limitations of additive processes in maintaining form integrity over longer geometries and reaffirming the benefits of combining AM with precision finishing operations.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 9 Conclusions  
The proposed algorithm maximises the SINR of UAVs in a 5G network by using DRL for power control and interference mitigation. It overcomes the unique challenges posed by the 3D motion of UAVs and the resulting air-to-ground interference by leveraging the cloud-based architecture of 5G systems. The algorithm outperforms traditional algorithms like Q-learning and FPA. The proposed DQL requires the UAV to send its position coordinates and receive the SINR at each step of the way to the gNB. However, it has no previous information about the CSI, which effectively means that channel estimates and associated training sequences are not needed. Thus, overall UAV feedback is reduced when the UAV sends its coordinates. Therefore, the interference mitigation proposed in this paper surpasses the existing methods, outperforming the next best algorithm by 41.66%, and offers a solution to one of the major obstacles in the implementation of 5G-connected UAVs, namely air-to-ground interference.  
Conceptualization, A.W.; methodology, A.W.; software, A.W.; validation, A.W. and S.A.-R.; formal analysis, A.W.; investigation, A.W.; resources, S.A.-R.; writing--original draft preparation, A.W. and S.A.-R.; writing--review and editing, A.W., S.A.-R. and G.I.; supervision, S.A.-R. and G.I.; funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Engineering and Physical Sciences Research Council (EPSRC) and Satellite Applications Catapult.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 21: Comparison of results with state-of-the-art method.
# An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN
## 4 Model Solving
### The Basic Firefly Optimization Algorithm  
The firefly algorithm is a bionic optimization algorithm. Its principle is to simulate firefly individuals with points in the search space. Each individual has brightness and attractiveness. Because of the phototaxis characteristics of fireflies, fireflies move towards brighter individuals. The core is used to keep fireflies close to brighter individuals by continually updating their positions. Combined with the SDN load balancing problem, the mapping relationship between the switch and controller in the strategy space is considered to be the location of a firefly, and the objective function of the Nash bargaining game is regarded as the brightness of the firefly. Individuals with low brightness will move to individuals with high brightness, and the process of firefly movement is the process of target optimization. The brightness of each firefly is expressed as follows: \(I(x_{i})=(LBD-\mathrm{a}_{LBD})(cost-\mathrm{a}_{cost})\), \(I(x_{i})\) is the brightness of the \(i\)-th firefly, \(x_{i}\) is the position of the \(i\)-th firefly, and \(d\) is the dimension of the problem.  
If the brightness of firefly \(i\) is greater than the brightness of firefly \(j\), firefly \(j\) is attracted by \(i\). The formula for calculating the attractiveness of \(i\) to \(j\) is as follows:  
\[\alpha_{ij}=\alpha_{\mathrm{max}}e^{-\lambda r_{ij}^{2}}. \tag{26}\]  
where \(\alpha_{\mathrm{max}}\) is the maximum attractive force, \(\lambda\) is the light absorption coefficient, and \(r_{ij}\) is the Cartesian distance between firefly \(i\) and firefly \(j\)--i.e., \(r_{ij}=\|x_{i}-x_{j}\|=\sqrt{\sum\limits_{k=1}^{d}{(x_{ik}-x_{jk})^{2}}}\).  
Firefly \(j\) is attracted by \(i\) and moves to \(i\). The position update formula is as follows:  
\[x_{j}^{k}=x_{j}^{k-1}+\alpha_{ij}(x_{i}^{k-1}-x_{j}^{k-1})+\delta\varepsilon_{j}. \tag{27}\]  
where \(x_{j}^{k}\) is the position of firefly \(j\) during the kth iteration, \(\delta\varepsilon_{j}\) is a random disturbance term, \(\delta\) is a constant, and \(\varepsilon_{j}\) is a random number obtained by uniform distribution.
## 4 Temperature Finite Element Model and Test Comparison
### Test Comparison  
Since the numerical simulation conditions for the U-shaped girder bridge had to be consistent with the ASHRAE clear sky model, actual sensor data collected under clear weather conditions were selected for comparative analysis in the simulation. Specifically, the collection of temperature sensor data on the test bridges was carried out on five consecutive days in the warmer months of May and July. During these ten days of observation, all five days in May were clear, while one of the five days in July was cloudy, which still meets the requirement for continuous clear weather. Taking the temperature field of the U-shaped girder bridge for five days in May as an example, the actual data were compared with the numerical simulation results, as shown in Figure 11. It is evident that in the initial two days, although the temperature change trend predicted by the model is roughly in line with the actual data, there are discrepancies in the specific values due to the initial conditions of the finite element model differing from the actual conditions. As the computation time increases, the consistency between the FEM results and the actual data significantly improves
###### Abstract  
Inverse Synthetic Aperture Radar (ISAR) images of space targets and their key components are very important. However, this method suffers from numerous drawbacks, including a low Signal-to-Noise Ratio (SNR), blurred edges, significant variations in scattering intensity, and limited data availability, all of which constrain its recognition capabilities. The terahertz (THz) regime has reflected excellent capacity for space detection in terms of showing the details of target structures. However, in ISAR images, as the observation aperture moves, the imaging features of the extended structures (ESs) undergo significant changes, posing challenges to the subsequent recognition performance. In this paper, a parabolic antenna is taken as the research object. An innovative approach for identifying this component is proposed by using the advantages of the Component Prior and Imaging Characteristics (CPICs) effectively. In order to tackle the challenges associated with component identification in satellite ISAR imagery, this study employs the Improved-YOLOv8 model, which was developed by incorporating the YOLOv8 algorithm, an adaptive detection head known as the Dynamic head (Dyhead) that utilizes an attention mechanism, and a regression box loss function called Wise Intersection over Union (WIoU), which addresses the issue of varying sample difficulty. After being trained on the simulated dataset, the model demonstrated a considerable enhancement in detection accuracy over the five base models, reaching an mAP50 of 0.935 and an mAP50-95 of 0.520. Compared with YOLOv8n, it improved by 0.192 and 0.076 in mAP50 and mAP50-95, respectively. Ultimately, the effectiveness of the suggested method is confirmed through the execution of comprehensive simulations and anechoic chamber tests.  
component recognition; Component Prior and Imaging Characteristics (CPICs); parabolic antennas; YOLOv8