esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Chl a, b and total chlorophylls increased by 98.8, 11.9 and 56.6%, respectively, with the N dose of 8.4 mg/L [54]. The highest antioxidant activity and phenolic content were obtained in the samples from the pot system treated with magnesium nitrate at high N doses (N-70 and N-90) as well as with ammonium and sodium nitrates at the N-90 dose. The results correlated with the level of nitrogen and the yield of the plant, which is similar to our previous findings [4]. In the hydroponic system, the highest DPPH activity was observed after the treatment with sodium nitrate at the N-70 and N-90 doses. [PERSON] et al. [55] concluded that the combined use of organic manure and a urea fertiliser led to enhanced antioxidant activity in three vegetables (_Solanum macrocarbon_ L., _Amarantthus viridis_ L. and _Telfariria occidentalis_ f. [PERSON]). Urea application can also improve the growth, chlorophyll contents and antioxidant activity of ramie (_Boelmeria nivea_ L.). Others [56] reported that the contents of hydrophilic antioxidant compounds (vitamin C and total phenols) as well as the antioxidant activity (DPPH and FRAP) in tomato (_Solanum lycopersicum_ var. [PERSON]) increased as the dose of urea was decreased. This is in contrast to our results, in which the TPC and TFC values increased with an increasing urea N dose. However, at the same time,this is in agreement with our results on antioxidant activity, in which the TEAC radical scavenging activity of the samples in pot cultivation decreased significantly as the N level increased from N-50 to N-70. The levels of antioxidant compounds were affected by different nitrogen forms. [PERSON] et al. [57] compared four types of fertilisers, i.e., urea, Ca(NO\({}_{3}\))\({}_{2}\), (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (ammonium sulfate) and an organic fertiliser, in terms of the oxidative status of rice plants in response to the oxidative stress caused by Cd. The results showed higher antioxidant levels in the plant treated with urea and ammonium sulphate compared to Ca(NO\({}_{3}\))\({}_{2}\) and the organic fertiliser. Regarding the total phenolic compounds, [PERSON] et al. [55] pointed out that the total polyphenol (510.70-521.50 mg GAE/g) and total flavonoid (609.51-742.50 mg rutin equivalent/g) contents were reduced when the dose of urea (CH\({}_{4}\)N\({}_{2}\)O) increased. On the contrary, in our experiment, when the urea (CH\({}_{4}\)N\({}_{2}\)O) level increased from N-50 to N-90, the levels of TPC and TFC increased. [PERSON] et al. [58] stated that an increased NH\({}_{4}\)NO\({}_{3}\) supply reduced the contents of phenolic compounds from 154 to 22 mg per 100 g\({}^{-1}\) FW in lettuce (_Luctaa sativa_ L.) from a hydroponic system. In our experiment, the N-70 dose of NH\({}_{4}\)NO\({}_{3}\) resulted in a higher level of the TPC than the N-50 dose of NH\({}_{4}\)NO\({}_{3}\). Increasing the N level to the N-90 dose, no significant increase in the TPC was observed.
# An Effective Simulation Scheme for Predicting the Aerodynamic Heat of a Scramjet-Propelled Vehicle
## 5 Conclusions  
In this paper, an effective simulation scheme for predicting the aerodynamic heat of a scramjet-propelled vehicle during flight is proposed. Each phase adopts a different grid strategy and numerical method, which were confirmed by the validation tests described in Section 3. In particular, the flux type dependencies, the cell Reynolds number dependencies, and the aspect ratio dependencies are discussed in Sections 3.3 and 3.4. The aerothermal simulation based on a wedge-shaped head of the RBCC scramjet-propelled vehicle during the climbing phase and cruising phase were performed. Through systematic analysis, several conclusions can be drawn, as follows:  
1. For the wedge section in the scramjet-propelled vehicle heating prediction, the suggested value of cell Reynolds number and aspect ratio of wall cells near the shock are different from those for blunt bodies. At the high supersonic stage, the ideal cell Reynolds number should be no larger than 16. At the hypersonic stage, neither the ideal cell Reynolds number, nor the aspect ratio of the wall cells near the shock should be larger than 40.
2. For the wedge section in the scramjet-propelled vehicle heating prediction, with the same grid strategy, upwind scheme, solver, and so on, the AUSM+ flux type performs better than [PERSON]'s FDS flux type. The Spalart-Allmaras turbulence model performs better at the transonic stage and the hypersonic stage, while the SST k-omega performs better at the supersonic stage and the high supersonic stage.
3. Smaller cell Reynolds numbers are not necessarily better. On the one hand, this may cause the quality of the mesh to decrease. On the other hand, as described in Sections 3.3 and 3.4, their improvement of calculation accuracy is limited; they even reduce the accuracy. The value of the aspect ratio is similar to the cell Reynolds number. It is necessary to confirm the appropriate values through CFD analysis based on numerical results and TVR plots.  
Conceptualization, L.Y. and G.Z.; methodology, L.Y. and G.Z.; investigation, L.Y.; formal analysis, L.Y.; resources, L.Y. and G.Z.; validation, L.Y.; visualization, L.Y.; writing--original draft preparation, L.Y.; writing--review and editing, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research is financially supported by the National Defense Basic Research Program of China (JCKY2019414D005).  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.
# Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments
## 3 Numerical Model Description
### Model Construction
#### 3.3.2 Numerical Model  
To convert the geometric structure image of the 2D porous media model into a COMSOL geometric model, we first converted it into a DXF file and then imported it into the software. The left end of the model represents the injection end, with injection velocities of 0.2 m/s, 1 m/s, and 5 m/s, while the right end represents the extraction end, with a pressure boundary condition of 0 Pa and no-slip boundaries on the upper and lower walls. For a coupled solution of Navier-Stokes and level-set equations using COMSOL Multiphysics coupling software, the interface thickness is set to one-third of the maximum grid cell size, the initial time length is 10-11 s, and the backward difference method is used to determine the computational time step. The PARADISO solver was used to solve the equations. The Delaunay triangular mesh is used to dissect the porous medium. In the constructed model, we calculated the mesh division with varying degrees of sparsity. When the mesh division is sparse, the calculation results are inaccurate. The gas-liquid interfacial repulsion of a calculation step will lack many details. With the mesh refinement, the calculation results in COMSOL tend to become more accurate. However, as the mesh division precision continues to increase, the improvement in calculation accuracy becomes less significant, while the calculation time increases exponentially. After verifying mesh independence, the mesh with 70,182 elements meets the accuracy requirements for the solution. First, the entire fracture channel is saturated with a liquid phase, and the gas phase is injected from the inlet at the left end of the model to displace the liquid within the pore, while the liquid exits through the outlet. Under the experimental conditions, it is assumed that no heat is absorbed or dissipated during the flow. In addition, the liquid phase is considered incompressible, and the effect of capillary forces during flow is taken into account. The nature of the fluid is assumed to be isotropic. The interior of the rift is a fully connected, contact-free spatial structure. The pore space varies greatly in different regions of the model, so in the mesh generation, the COMSOL embedded mesh method is used to refine the mesh in different pores by adjusting the grid number of the narrow region resolution. The meshing in the throat area should be compact enough to ensure the accuracy of the calculation, and the appropriate coarsening of the mesh in the pore area can reduce the calculation time. To adapt to the boundary of circular pores, the triangular mesh shape is selected, and a total of about 106,811 mesh elements and 1222 boundary cells are divided. The comparison of experimental groups is shown in Table 2. The schematic flow of a nonhomogeneous model and local meshing is shown in Figure 6.
# Mobility-Multihoming Duality
## 4 ILNP Implementation in Linux
### ILNP in IPv6  
ILNP packets are IPv6 packets that carry an end-to-end extension header, as defined in RFC6744 [41] and shown in Figure 3. The extension header is an IPv6 Destination Option header. So, it does not cause additional load to routing or forwarding processes; it is generated and processed only by end systems. The extension header contains a Nonce value of either 32 bits or 96 bits, which is used by the ILNP end systems. It serves two purposes: it marks a packet as being an ILNP packet, and the (pseudo-random) Nonce value contained acts to perturb off-path packet forging attacks.  
Figure 3: The ILNP IPv6 extension header as in RFC6744.
## 4 Noise Analysis of ODTS  
The test assessment methods of GNSS satellite clocks include the phase comparison approach, the radio two-way method, the orbital inverse algorithm, and the prevailing ODTS method. The ODTS method uses the observation data from global ground stations with high-precision carrier phase measurements, and undoubtedly, a systematic noise exists in ODTS products. These errors include the measurement error, the ionospheric delay, the tropospheric delay, the multipath effect, the dynamic model, and the influence of ground station distribution. Hence, the systematic noise of ODTS needs to be quantified before assessing the performance of GNSS satellite clocks. The high-precision orbit and clock products of IGS, XRS, and WUM are derived by the ODTS method, where there are some ground stations attached to high-stability AHM. Therefore, we can calculate the frequency stability of ODTS noise by differencing receiver clocks of two stations attached to high-stability AHM. Table 3 shows some stations' information used in XRS products.  
Figure 5: BDS-3 orbit results of XRS products compared with those of WUM products, where \(\times\) indicates 10 BDS-3 satellites with RAFS and \(\cap\) indicates 8 BDS-3 satellites with PHM.  
Figure 6: BDS-3 clock offset results of XRS products compared with those of WUM products.  
The time period from July 9 to August 26 in 2019 was chosen to assess the ODTS noise. The differenced values between one receiver clock of three stations (PTBB, MGUE, and NOVM) and the KOUR station were derived. Figure 7 shows the frequency offsets of the differenced values after processing the data editing procedure. It can be seen that the outliers or jumps are basically removed.  
Figure 8 shows the HDEV of one continuous arc frequency data as the function of averaging time. Results of three curves are comparable, though station NOVM is slightly worse than the other two stations. Table 4 gives the statistics of ODTS noise for the three stations. The tiny HDEVs at averaging time 10,000 s and 86,400 s show the consistency of receiver clocks between two ground stations attached to AHM. The average values are about \(1.13\times 10^{-14}\) and \(2.68\times
## 5 Results and Discussions
### Influence of Liquid Viscosity  
Figure 17 illustrates the axial solid concentration profile at \(r=0.5R\) as a function of liquid viscosity for the BCPBT. It can be seen that the BCPBT can effectively enhance the solid-liquid mixing degree under different liquid viscosities, which shows that it can be applied to various industrial productions, such as the preparation of PCMs. With an increase in the liquid viscosity, the C\({}_{z}\)/C\({}_{av}\) value prominently increases and lower solid concentration gradients in the axial direction are observed. The more viscous the liquid, the more easily the solid particles remain suspended, and the more uniform the particle distribution. The reason is that the higher viscous drag leads to the smaller settling velocity of a single particle, thus preventing the solid particles from returning to the bottom of the tank after suspension.
## 4 MiniGrid Hybrid Power System Description
### Power Convertor and Storage System  
A bi-directional DC-AC converter was employed for power conversion. When surplus power is generated by PV, diesel, and/or WG, the excess AC power is converted to DC and stored in a battery, to supply it back when necessary. A conversion efficiency of 95% and 15 years of lifetime are assumed in the simulation [33]. In this study, a Li-ion battery with a round-trip efficiency of 90%, an initial state of charge of 100%, a minimum SOC of 20%, and 10 years of lifetime with negligible O&M cost is considered [42].
## 2 Literature Review of Network Intrusion Detection
### Machine Learning in Intrusion Detection  
Machine learning techniques have been widely studied in network intrusion detection since the 1990s [39]. Early research utilized algorithms such as decision trees [40], K-nearest neighbors [41], and support vector machines (SVMs) [42] to classify network traffic as either normal or malicious. While these methods improved detection rates compared to traditional approaches, they still faced challenges in handling high-dimensional and imbalanced data. To overcome these limitations, researchers have proposed various preprocessing techniques, feature selection methods, and ensemble strategies to enhance the performance of machine learning-based IDS [43]. Despite these advancements, machine learning methods may still struggle to capture the complex patterns and relationships in network traffic data. As a result, there has been growing interest in deep learning algorithms, which have shown exceptional ability in learning hierarchical representations from high-dimensional data [44].
## 5 2D Results of Droplet SMD, Number Density, and Liquid Volume Fraction  
Figures 9 and 10 show the 2D images of the spray quantities, i.e., droplet SMD, droplet concentration (N) and liquid volume fraction (LVF) for spray sections at Z = 4 mm to 9 mm and at Z = 10 mm to Z = 15 mm, respectively, as follows:  
* At Z = 4 mm, droplet SMD ranges between 50 \(\upmu\)m to 60 \(\upmu\)m, N value ranges from 5 to 20 droplets per mm\({}^{3}\), LVF ranges between \(2.5\times 10^{-4}\) to \(5.5\times 10^{-4}\);
* At Z = 8 mm, droplet SMD ranges from a minimum value of 35 \(\upmu\)m to a maximum value of 60 \(\upmu\)m, N value ranges from 5 to 35 droplets per mm\({}^{3}\), LVF ranges between \(1.8\times 10^{-4}\) to \(5.5\times 10^{-4}\);
* At Z = 12 mm, droplet SMD ranges from 30 to 60 \(\upmu\)m, N value ranges from a minimum of 5 droplets per mm\({}^{3}\) to a maximum of 40 droplets per mm\({}^{3}\), LVF ranges between \(1\times 10^{-4}\) to \(5.5\times 10^{-4}\);
* At Z = 15 mm, droplet SMD ranges from 20 to 60 \(\upmu\)m, N from 5 to 60 droplets per mm\({}^{3}\), LVF ranges from \(0.1\times 10^{-4}\) to \(5.5\times 10^{-4}\).  
The absolute SMD values measured with the presented approach follows well with the PDA data, as has been previously demonstrated in [21], with the calibrated SLIPI-LIF/Mie ratio and SMD acquired from PDA for the hollow-cone spray of OD \(\sim\)1. The extracted N and LVF values in this investigation are in the order of the theoretical calculations performed based on a homogeneous distribution of monodisperse non-absorbing dielectric spherical droplets in reference [5]. Note that the numerical calculations in [5] are verified by an experimental study performed by [PERSON] et al. in [38]. Using all 30 sections (15 mm depth) of 2D maps of LVF (see Figures 9 and 10), a resultant 3D reconstruction can be generated as shown in Figure 11. The LVF value is lowest at the center of the spray and highest at the spray peripheries.  
Figure 8: (a) Calculated \(\upmu_{\text{e}}\) of a diluted homogeneous milk solution, which is fairly constant throughout; (b) Calculated \(\upmu_{\text{e}}\) of different diluted milk samples plotted as a function of milk volume concentration (%) in the sample. Image data from [26].  
Figure 9: Two-dimensional maps of droplet SMD, N, and LVF, for spray sections at Z = 4, 5, 6, 7, 8, and 9 mm. Droplet SMD varied in the range of 35 to 60 \(\upmu\)m. Droplet N varied in the range 5–35 droplets per mm\({}^{3}\). LVF values from a minimum of \(0.1\times 10^{-4}\) to a maximum value of \(5.5\times 10^{-4}\).  
Figure 10: Two-dimensional maps of droplet SMD, N, and LVF, for spray sections at \(Z=10\), 11, 12, 13, 14, and 15 mm. Droplet SMD varied in the range 20–60 \(\upmu\)m. Droplet N varied in the range of 5 to 60 droplets per mm\({}^{3}\). LVF values from a minimum of \(0.1\times 10^{-4}\) to a maximum value of \(5.5\times 10^{-4}\).
## 3 Experimental Procedures
### Characterizations  
To investigate the effect of the shielding gas flow on the resulting weld bead geometry, the individual weld beads were scanned using a 3D profilometer (VR-3100, Keyence Corporation, Osaka, Japan). The geometry was evaluated at ten positions along each of the weld beads. Existing thermal distortions of the substrate plates resulting from the process were compensated for by using the software provided by the manufacturer. For the metallographic analysis of the deposited cuboid, a cross-section through the center of the specimen was taken. The sample was ground in four steps (180, 300, 800, 1200 grit) and then polished (3 um, 1 um). To analyze the microstructures, the sample was treated with an etching solution (Adler) by immersion technique and examined with a digital microscope (VHX-7000, Keyence Corporation, Osaka, Japan). A scanning electron microscope (JSM-IT200, JEOL Ltd., Akishima, Japan) with an integrated unit for energy dispersive X-ray  
Figure 5: Configurations of the investigated shielding gas concepts: (**a**) lateral shielding gas nozzle; (**b**) optimized coaxial shielding gas nozzle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Unit** & **Single Tracks** & **Cuboid** \\ \hline Laser power \(P_{\text{L}}\) & W & 1500 & 1200 \\ Traverse speed \(v_{\text{t}}\) & m/min & 1.0 & 1.0 \\ Wire feed rate \(v_{\text{w}}\) & m/min & 1.0 & 1.1 \\ Focal position \(f\) & mm & \(-6\) & \(-6\) \\ Shielding gas flow rate \(f_{\text{s}}\) & 1/min & 10–70 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Process parameters for the deposition of individual weld beads and a cuboid.  
spectroscopy (EDS) was utilized to investigate the oxygen distribution in the built-up cuboid. The accelerating voltage applied was 20 kV. This investigation was carried out before treating the sample with the etching solution.  
Vickers microhardness measurements according to DIN EN ISO 6507-1 were performed on the cross-section of the built-up cuboid using a fully automated hardness tester (Qness 60 A+ EVO, ATM Qness GmbH, Mammelzen, Germany). For each indentation, a test force of 2.942 N (corresponding to HV 0.3) was applied for a dwell time of 10 s. The hardness was evaluated at 33 positions along three straight lines from the bottom to the top of the cuboid. For each line, the distance between the center points of adjacent indentations was 180 \(\upmu\)m.
# Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm
## 2 Materials and Methods
### Key Technology of Faster-RCNN  
#### 2.1.1 Anchor Boxes  
The regional proposal network uses anchor boxes to get the suggestion boxes of the feature map. Faster-RCNN uses three sets of rectangular boxes with different length-width ratios (2:1, 1:1, 1:2), and each set uses three fixed-size rectangular boxes with different scales (128, 256, 512) to stack at each pixel on the feature map, as shown in Figure 2. Then, each box was compared with ground truth box to calculate the intersection over union (IOU). The box with larger IOU than the large preset threshold is demarcated as foreground, and the box with smaller IOU than the small preset threshold is demarcated as background. Both the boxes with IOU between the two thresholds and the overflow boundary were directly discarded. Finally, we got the suggestion box to train the region proposal network (RPN).  
The introduction of anchor boxes have brought many advantages. By setting different scales, all the targets can be covered as far as possible, while reducing the calculation amount and greatly reducing the difficulty of subsequent regression algorithm optimization.
# Safety Design of Rotary Drilling Rig Mast Based on Multi-Condition Analysis
## 2 Classification of Working Conditions and Structural Mechanics Model
### Load-Bearing Capacity Analysis
#### 2.3.4 Fatigue Strength  
The fatigue strength verification formula [23; 24] is shown in Equation (10). The left-hand side of the equation represents the stress range under a typical working cycle [25], while the right-hand side indicates the limit design stress range represented by the stress history parameters, considering the working grade.  
\[\sigma_{\text{max}}-\sigma_{\text{min}}\leq\frac{\Delta\sigma_{\text{c}}}{( \gamma_{\text{mf}}\sqrt[3]{sm})} \tag{10}\]  
In the equation, \(\sigma_{\text{max}}\) and \(\sigma_{\text{min}}\) are the partial safety factors, considering the dynamic load coefficient in the design stress limit. \(\Delta\sigma_{\text{c}}\) represents the characteristic fatigue strength, defined as the fatigue strength under a constant stress range, corresponding to 2 \(\times 10^{6}\) cycles with a reliability probability of 97.7%. \(\gamma_{\text{mf}}\) is the fatigue strength resistance coefficient and \(s_{m}\) is the stress history parameter.
###### Abstract  
Unmanned Aerial Vehicles (UAVs) are increasingly employed across various domains, including communication, military, and delivery operations. Their reliance on the Global Positioning System (GPS) renders them vulnerable to GPS spoofing attacks, in which adversaries transmit false signals to manipulate UAVs' navigation, potentially leading to severe security risks. This paper presents an enhanced integration of Long Short-Term Memory (LSTM) networks with a Genetic Algorithm (GA) for GPS spoofing detection. Although GA-neural network combinations have existed for decades, our method expands the GA's search space to optimize a wider range of hyperparameters, thereby improving adaptability in dynamic operational scenarios. The framework is evaluated using a real-world GPS spoofing dataset that includes authentic and malicious signals under multiple attack conditions. While we discuss strategies for mitigating CPU resource demands and computational overhead, we acknowledge that direct measurements of energy consumption or inference latency are not included in the present work. Experimental results show that the proposed LSTM-GA approach achieved a notable increase in classification accuracy (from 88.42% to 93.12%) and the F1 score (from 87.63% to 93.39%). These findings highlight the system's potential to strengthen UAV security against GPS spoofing attacks, provided that hardware constraints and other limitations are carefully managed in real deployments.  
deep learning; drones; Genetic Algorithm; GPS spoofing; LSTM; machine learning; UAV +
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