esa-sceva/satcom-chunk-collection huggingface dataset:
# An AMR-Based Liquid Film Simulation with Surfactant Transport Using PLIC-HF Method
## 2 Governing Equations
### Level Set Advection and Re-Initialization  
In the present research, the level set function \(\psi\)[13; 39] was used as a refinement criterion in the Adaptive Mesh Refinement (AMR) method and for some surfactant transport calculations. The level set function \(\psi\) evolves over time according to the advection equation shown below:  
\[\frac{\partial\psi}{\partial t}+(\mathbf{u}\cdot\
abla)\psi=0. \tag{15}\]Since the Level Set function \(\psi\) represents the signed distance from the interface, it has the following property:  
\[|\
abla\psi|=1. \tag{16}\]  
Due to the numerical error from long-term integration, the \(|\
abla\psi|=1\) property may be compromised. To maintain the \(|\
abla\psi|=1\) property of the level set function \(\psi\), which is defined as a signed distance function, it is necessary to carry out re-initialization on the level set function \(\psi\).  
An initial re-initialization value for the level set is given by the volume fraction near the interface:  
\[\psi^{\rm init}\left(\mathbf{x}\right)=1.5\times\Delta x\left(2\left(\phi(\mathbf{x} )-\frac{1}{2}\right)\right) \tag{17}\]  
Then, re-initialization is performed by time evolution of the following equation within a physical time step \(\Delta t\) using a virtual time step \(\tau\), which is usually set as \(0.5\times\Delta x\).  
\[\frac{\partial\psi}{\partial\tau}+S(\psi)(|\
abla\psi|-1)=0 \tag{18}\]  
\[S(\psi)=\frac{\psi}{\sqrt{\psi^{2}+|\
abla\psi|^{2}\Delta x^{2}}} \tag{19}\]
# Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation
## 4 The Enhanced Scheme
### Performance Analysis  
We implemented the enhanced scheme in Python with several existing libraries, such as NumPy [28] and Cryptography [29] (the source code of the enhanced scheme instantiation is available at [[https://github.com/MoienBowen/Blockchain-Federated-Learning](https://github.com/MoienBowen/Blockchain-Federated-Learning)]([https://github.com/MoienBowen/Blockchain-Federated-Learning](https://github.com/MoienBowen/Blockchain-Federated-Learning)) (accessed on 1 April 2024)). In the experiment, we used a PC as the simulation platform, with an Intel(r) Core(tm)7-4770 CPU @ 3.4 GHz processor with 16 GB RAM.  
**Dataset.** Given our primary focus on 5G security, we selected a dataset tailored to this domain: the 5G-NIDD dataset [30], a robust compilation of network intrusion data specifically designed for 5G wireless networks, containing 1,215,890 records. This dataset was meticulously generated on an operational 5G testbed, part of the 5G test network (5 GTN) at the University of Oulu, Finland. It features an extensive range of simulated attack scenarios, offering a valuable dataset for AI/ML model training. Detailed categories and their respective distributions are presented in Table 1.  
**Setup.** Our parameter selection was guided by our goal of 128-bit security. For example, we used the Secp256r1 curve for the Burmester-Desmedt group key agreement protocol [27], AES-CTR for encryption, SHA-256 for hashing, and a 256-bit prime field as \(\mathbb{F}\). Moreover, we considered that there were \(n=50,100,150\), and 200 clients, tolerating up to \(m=\frac{n}{10}\) malicious ones, with \(d=1000\) as the size of the update gradient vector. Each client owned 24,053, 12,156, 8100, and 6075 records, respectively (each category of the dataset was divided equally and randomly into \(n\) copies). We omitted the consensus time of the blockchain but this did not affect the representation of the protocol performance.  
**Results.** In Table 2, we emphasize the runtime of Step 2--model parameter sharing and Step 3--model parameter aggregation, as the performance mainly depends on the number of clients. The values represent the average execution times of single iterations/epochs over 10 runs.  
Regarding the blockchain in Step 2, we calculate the computational time on the PC we use. In practice, the computational time will be based on the mining nodes. Moreover, the complexity will also come from the consensus protocol used in the blockchain platform. Obtaining precise running time statistics on chosen blockchain platforms will be looked at in future work.
###### Abstract  
Linear Support Vector Machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized Linear Support Vector Machine (KLSVM) to produce stable and consistent results. KLSVM is a linear classifying technique that learns the \"best\" parameter settings. We investigated its application to model and capture two phenomena: High dimensional multi-category classification and Spatiotemporal data correlation in wireless sensor network (WSN). In addition, the technique also detects anomalies within the network. With the optimized selection of the linear kernel hyperparameters, the technique models high-dimensional data classification and the examined packet traces exhibit correlations between link features. Link features with Packet Reception Rate (PRR) greater than 50% show a high degree of negative correlation while the other sensor node observations show a moderate degree of positive correlation. The model gives a good visual intuition of the network behavior. The efficiency of the supervised learning technique is studied over real dataset obtained from a WSN testbed. To achieve that, we examined packet traces from the 802.15.4 network. The technique has a good performance on link quality estimation accuracy and a precise anomaly detection of sensor nodes within the network.  
Keywords:linear support vector machine; linear kernel; correlation; high dimensional multi-category data classification; wireless sensor network +
Footnote †: journal: Sensors
## 3 Validation with Specim Experiments
### Validation of Reconstructed Impact Load with Structural Strain  
The accuracy of our method is examined by investigating whether the FE simulation model applied with the reconstructed impact load generates maximum strain responses similar to those of the actual dynamic impact experiments, as shown in Figure 2b. The measured strains induced by impact at two validation points of the structure are compared with the corresponding strains from the numerical simulations. The strain measured at Validation points 1 and 2 are the y-axis strain, as shown in Figures 2b and 5. The y-axis strains of Validation points 1 and 2 are caused mainly by the bending force induced by the impact load. Figure 5a,b shows the contour representations of the distributions of strains in the y-axis and the elements used for calculating the strains at the validation points. As shown in Figure 5c, a positive strain is caused by the tensile stress at Validation point 1, whereas a negative strain is caused by the compressive stress at Validation point 2. The error bars in the experimental data describe the deviation in the strain data measured in 10 sets of impact experiments. The peak strains at the validation points measured in the experiments are listed in Table 2. The error bars in the numerical simulation are presented to illustrate the uncertainty in the position of the validation points in the numerical model. Six elements selected at each validation point are shown in Figure 5a,b. The average peak strain measured at the two validation points for the loading angle of \(32^{\circ}\) are \(37.2\) and \(-80.7\)\(\mu\epsilon\), respectively, whereas the calculated values are \(35.2\) and \(-81.8\)\(\mu\epsilon\), respectively. The average peak strain measured at the two validation points for the loading angle of \(47^{\circ}\) are \(53.5\) and \(-54.0\)\(\mu\epsilon\), respectively, whereas the calculated values are \(54.4\) and \(-56.7\)\(\mu\epsilon\), respectively. The average peak strain measured at the two validation points for the loading angle of \(65^{\circ}\) are \(75.4\) and \(-80.7\)\(\mu\epsilon\), respectively, whereas the calculated values are \(78.6\) and \(-81.8\)\(\mu\epsilon\), respectively. The maximum difference between experimentally measured and numerically calculated peak strain is \(3.2\)\(\mu\epsilon\) This is observed at Validation point 1 with the loading angle of \(65^{\circ}\), which corresponds to the highest impact. The remarkable agreement indicates that the system developed in this study can determine externally applied impact loads with sufficient accuracy.  
Figure 4: Reconstructed impact loads.
## 1 Introduction  
The geometries of small machine components are easily measured because they are very common in mechanical engineering, and the relevant measurement instruments are available [1; 2]. However, a ship's engine room primarily contains equipment with medium and large components [3; 4] that are heavy and large, and display low, variable rigidity that makes them highly susceptible to flexural deformation. This group of machine parts includes crankshafts, camshafts, straight shafts, and stage shafts that are part of line shafting [5]. We consider a crankshaft to be large where the length-to-diameter ratio (L/d) is greater than \(12\div 15\), whereas the shape factor \(\alpha_{\mathrm{k}}\) determining the nature of cross-sectional changes may take on significant values \(\alpha_{\mathrm{k}}>1\)[6].  
Ship engine crankshafts are primarily used in diesel engines (reciprocating internal combustion engines), but they are also used in other types of haulage and transport (rail, road), agriculture, mining, and industrial construction.  
When characterizing large crankshafts, the manufacturing technology must be considered because it is closely linked with the construction and shape. Crankshafts are formed by machining, resulting in complex shapes with low rigidity. Additionally, their manufacture requires high geometric precision, causing crankshafts to account for an estimated 20-25% of the total engine cost [7; 8]. Crankshaft manufacturing precision largely determines whether a crank-and-piston system will function correctly and, consequently, the entire working machine's reliability, safety, and operating costs [9; 10; 11].  
Manufacturers impose strict requirements on the machine components that mate with the non-rigid thin-walled multilayer bearing liners used in modern marine engines. For this reason, modern manufacturing processes require continuous quality control for manufactured crankshafts [12; 13; 14]. A complete assessment of the geometric condition of a product to reflect its actual state can only be ensured using suitable measurement methods and techniques, and they should enable metrological and instrument accuracy within the specified tolerances [15].
# Compact and Broadband Microstrip Band-Stop Filters with Single Rectangular Stubs
## 4 Results and Discussion  
### Validation of Measurement Results  
As an example, Figure 7 shows the transmission and reflection characteristics \(\mid\!S_{21}\!\mid\) and \(\mid\!S_{11}\!\mid\) when the impedance ratio is \(Z_{1}/Z_{0}=1/2\), in the case of the sample pattern (a) in Figure 5. The three types of data show the calculated values based on the TEM transmission line theory, the EM simulated value by using a commercial EM simulator Ansys HFSS v.11, and measured values. There exists a discrepancy between the theoretical calculation and the other two above 4 GHz. This is because, for the sake of simplicity, the theoretical calculation does not consider the dielectric loss and the conductor loss due to the skin effect. Also, the EM simulated \(\mid\!S_{21}\!\mid\) value is larger than the measured value above  
Figure 5: Schematic view of measurement samples examined in this study. All numerical units are in mm. (**a**) Rectangular stubs of four different characteristic impedance ratios \(Z_{1}/Z_{0}=1/4\), \(1/2\), \(1\), and \(2\). (**b**) Rectangular stub of \(Z_{1}/Z_{0}=1/4\), with no matching section except for the either end of the I/O line. (**c**) Rectangular stub of \(Z_{1}/Z_{0}=1/4\) with a tapered line. (**d**) Rectangular stubs of \(Z_{1}/Z_{0}=1/4\) with two different series inductances. (**e**) Rectangular stubs of \(Z_{1}/Z_{0}=1/4\) with a larger series inductance. (**f**) Radial stubs of central angle \(\alpha=60^{\circ}\) and \(90^{\circ}\).  
Figure 6: Measurement system by using a vector network analyzer (VNA). The device under test (DUT) is inserted between the short, open, load, and through (SOLT) calibration planes, not including SMA connectors attached to the DUT substrate.  
6 GHz, despite the solve-inside-conductor option. It is considered that the conductor loss due to the skin effect at higher frequency is not calculated with sufficient accuracy in the HFSS [26]. Therefore, in this study, it is confirmed that measurements can be made with sufficient accuracy up to 6 GHz. Therefore, the transmission characteristics of 0-5 GHz, at which the quarter-wavelength resonance is observed, will be compared.
## 4 Discussion  
### Quality of PS-InSAR Measurements  
PS-InSAR results are relative to the reference point; hence, the absolute surface deformation is uncertain, as we were unable to obtain a record of the stable GPS station of the Centre d'Etudes Alexandries during our study period. A previous PS-InSAR study conducted in Alexandria used GPS station data to validate its results in the period between 2003 and 2010 [17]. That study applied the PS-InSAR technique over the city center of Alexandria and validated the PS-InSAR results based on the vertical velocity of a continuous GPS station at the Centre d'Etudes Alexandries; in this study, the vertical velocity was estimated to be 0.05 \(\pm\) 0.29 mm/yr with respect to the global international terrestrial reference frame. Their validation results indicated a mean ground motion of \(-\)0.39 mm/y in their study area. We in turn used this mean deformation value to validate our results. We found that the average vertical deformation of the same study area was \(-\)1.4 mm/yr. Therefore, we adopted a potential error range of \(\pm\)1-2 mm in our study vertical displacement results.  
Figure 12: Complex subsidence causality map of Alexandria presented on the Sentinel-2 gray image of 24 October 2018 blue band; the insert map shows corresponding annual subsidence velocity during the study period.
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 3 Framework Breakdown
### Mesh Module  
The mesh module bridges the gap between raw geometry and solver-ready computational meshes. This module is tasked with two primary objectives: geometry pre-processing and mesh generation. The first objective builds upon the modeled wing generated by the geometry module, performing geometric manipulations to prepare a watertight control volume for mesh generation. This includes steps such as feature recognition, body of influence (BOI) definition, and far-field boundary creation. These steps are further analyzed and discussed in Appendices A.2 and A.3.  
The second objective (mesh generation) involves discretizing the computational domain into smaller cells or elements, which act as control volumes for solving the governing equations of fluid flow. The quality of the mesh directly impacts numerical accuracy, numerical stability, and computational efficiency, as it determines the resolution of key physical phenomena such as boundary layers, wake regions, and separation zones.  
Implemented via the ANSA Python API, the module uses an object-oriented _pre_processor_ class with four method categories, as illustrated in Figure 6. The process begins with import methods, which handle the data transfer from the geometry module, importing STEP files and extracting attributes from the XML file for a case-specific setup. Next, feature recognition is performed to identify critical regions such as leading edges, trailing edges, and sharp perimeters. Bodies of Influence (BOIs) are then defined, along with the far-field boundaries of the computational domain. Following these geometric pre-processing steps, the meshing operations take place. Mesh parameters are dynamically adjusted using the custom _Compute_Mesh_Parameters_ method and the geometric attributes of the given wing. The custom _Batch_Mesh_Setup_ method then updates the meshing sessions/scenarios before generating the mesh in the following sequence: (1) surface meshing for both the wing wall and the far-field boundaries, (2) inflation layer mesh, and finally (3) volume meshing. Finally, the Export Mesh and Save Case methods save the mesh in Fluent-compatible _.msh_l5_ format and generate XML files for case tracking and attribute documentation.  
At this stage, one notable feature of the XML file becomes evident: it serves as a bridge between the modules by enabling the transfer of all attributes and data from the _Wing_ class to the _pre_processor_ class. This eliminates the need for direct serialization, streamlining data handling within the framework. Once the geometry generation step is complete, the XMLfile ensures that the meshing module can independently proceed at a later time without requiring direct interaction with the geometry module.
## 3 Results and Discussion
### The Bioactive Compound Content of Gluten-Free Bread with Hemp Inflorescence Addition  
The amount of polyphenols in the tested samples was affected by the addition of hemp infloresence (HI). The polyphenol content increased from 36 to 195% with HI addition in the 1% to 4% tested range of addition (Table 3). There was no statistically significant difference in the polyphenol content when 2% and 3% hemp were added. Similarly, the flavonoid content in the bread with hemp additions compared to the control bread increased from 100% for 1% HI addition to 433% for 5% HI addition (Table 3). All the tested bread samples had statistically significant differences in flavonoid contents. There are numerous studies in the literature on the polyphenol content of hemp. However, a majority of them have focused on the contents of these compounds in seeds, oils, and flours [53; 54; 55; 56; 57]. The polyphenol content of the leaves, for example, ranges from 0.09 to 0.56 [54], that of the infloresences ranges from 3.02 to 9.35 mg QE\(\cdot\)g\({}^{-1}\)[58], while for the seeds it is 51.6 mg GAE\(\cdot\)g\({}^{-1}\)[55; 56; 53]. According to [PERSON]'s research [14], the infloresence contains significantly more polyphenols than other parts of the plant (10.5-48.9 mg GAE\(\cdot\)g\({}^{-1}\)) depending on the variety. The polyphenol content in flour ranges between 0.74 and 1.71 mg GAE\(\cdot\)g\({}^{-1}\)[57].  
Due to the high polyphenol content of hemp flour, its addition to various types of bakery products helps to strengthen the antioxidant properties of these products. In comparison to the control samples, the addition of raw or roasted hemp flour to wheat flour cookies increased the total phenol content (0.559-1.634 mg GAE\(\cdot\)g\({}^{-1}\)) and antioxidant activity (34.41-40.88%). The TPC values increased with the addition of raw and roasted hemp flour, with the greatest increase in TPC observed for the biscuit samples containing 20% roasted hemp flour (4.03-fold increase in TPC compared to the control sample) [51].  
The antioxidant activity in the tested samples, as measured by DPPH and FRAP, increased as the hemp addition increased. The DPPH ranged from 1.66 \(\upmu\)m TE\(\cdot\)g\({}^{-1}\) d.m. for 1% hemp addition to 3.23 \(\upmu\)M TE\(\cdot\)g\({}^{-1}\) d.m. for 5% hemp addition. Compared to the control sample, with 1% of the additive, there was a 48% increase in the DPPH value, and with 5%, the increase was 188%.  
With the addition of 1% and 5% hemp, the antioxidant activity, expressed as FRAP, ranged from 1.25 to 3.00 \(\upmu\)M TE\(\cdot\)g\({}^{-1}\) d.m, and so for the highest addition, the value increased by 140%. The antioxidant activity measured by the FRAP test in the bread without the addition of hemp was 1.25 \(\upmu\)M TE\(\cdot\)g\({}^{-1}\) d.m (Table 3). There was no statistically significant difference in the antioxidant activity of FRAP between the control bread and the bread with the addition of 1% hemp, as well as between the samples with the additions of 3% and 4% hemp.
###### Abstract  
The response of flames' heat release to acoustic excitation is a critical factor for understanding combustion instability. In the present work, the nonlinear heat release response of a methane-air non-premixed flame to low-frequency acoustic excitations is experimentally investigated. The flame describing function (FDF) was measured based on the overall CH* chemiluminescence intensity and the velocity fluctuations obtained by the two-microphone method. The CH* chemiluminescence and schlieren images were analyzed for revealing the mechanism of nonlinear response. The excitation frequency ranges from 10 Hz to 120 Hz. The forced relative velocity fluctuation amplitude ranges from 0.10 to 0.50. The corresponding flame Strouhal number (_Stf_) ranges from 0.43 to 4.67. The study has shown that the flame length responds more sensitively to changes in excitation amplitude when subjected to relatively high-frequency excitations. The normalized flame length (\(\mathbb{I}_{f}/\mathbb{D}\)) decreases from 3.79 to 2.37 with the increase in excitation amplitude at an excitation frequency of 100 Hz. The number of oscillation zones along the flame increases with increasing excitation frequency, which is consistent with the increase in the \(\mathbb{S}_{f}\). The low-pass filtering characteristic of FDF is caused by the dispersion of multiple oscillation zones, as well as the cancellation effect of the adjacent oscillation zones under relatively high-frequency excitation. The main mechanism for the local gain peak and valley is the cancellation effect of positive and negative oscillation zones with various \(\mathbb{S}_{f}\). When two adjacent oscillation regions have similar amplitudes, the overall phase-lag becomes more sensitive to changes in excitation frequency and amplitude. This sensitivity leads to nonlinear anomalous changes in the phase-lag near the frequency corresponding to the gain valley. The calculated disturbance convection time is consistent with the measured time delay in the short flame scenario. Further research is required to determine whether the identified agreement is a result of the consistent occurrence of the oscillation zone in close proximity to the flame's center of mass, in conjunction with a precise determination of the average convective velocity.  
Keywords:non-premixed flame; flame describing function; nonlinear response; bluff body; convective velocity +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Correlations between Electric Current Density and HRT and Pollutant Concentrations
The requirements set for wastewater discharged into natural waters and the ground were met by the effluents from the REBDC treated at \(J\) = 0.63 A/m\({}^{2}\); the effluents treated at \(J\) = 1.25 A/m\({}^{2}\) and HRTs of 8, 12, and 24 h; and the effluents treated at \(J\) = 2.50 A/m\({}^{2}\) and HRTs of 12 and 24 h. In turn, the concentration of ammonia nitrogen was below 3.00 mg N/L, at all \(J\) and HRT values. In the present study, the extension of hydraulic retention time at all electric current densities tested led to increased effectiveness of nitrogen compounds removal. The results obtained corroborated earlier findings reported by [PERSON] et al. [34] that the longer hydraulic retention time facilitating denitrifying bacteria development contributed to the less accumulation of nitrite nitrogen (Table 1).