esa-sceva/satcom-chunk-collection huggingface dataset:
# Crack Initiation and Propagation Fatigue Life of Ultra High-Strength Steel Butt Joints
## 2 Experimental Investigations
### Determination of Crack Propagation Parameters  
The assessment of crack propagation life requires the knowledge of suitable material parameters describing the crack propagation behaviour. Therefore, the determination procedure utilizing crack gauges presented in [24] is applied to measure the crack propagation of S1100 base material exhibiting 6 mm sheet thickness. Hereby, four single edge notch tension (SENT) specimens are tested under constant amplitudes at a stress ratio of \(R\) = 0.1 recording the respective crack propagation. The subsequent evaluation of the fatigue crack growth rate is performed according to ASTM E647 [52]; the according geometry factor to determine the specimens stress intensity factor is based on [53]. Figure 4 shows the resulting long-crack propagation behaviour of all four specimens.  
In the literature, many different models are provided describing the materials' behaviour in the d\(a\)/d\(N\)--\(\Delta K\) regime; for example, in [54; 55]. For this work, the crack propagation model according to Paris-Erdogan [56] is applied requiring only two parameters \(C_{P}\) and \(m_{P}\) (see Equation (4)). A non-linear fitting procedure was used to determine these parameters for the present experimental data, whose result is plotted in Figure 4 as well. The consequential values are \(C_{P}\) = 8.3509\(\times 10^{-10}\) and \(m_{P}\) = 1.721 for units of mm/cycle and MPa\(\sqrt{\text{mm}}\), respectively.  
\[da/dN=C_{P}\cdot\Delta K^{m_{P}} \tag{4}\]  
Figure 3: Fracture surface clearly showing the crack initiation point at the undercut (red arrow) [47] (\(\Delta\sigma_{n}\) = 200 MPa, \(N_{f}\) = 2.87\(\times 10^{6}\), \(R\) = 0.1).  
Figure 2: Fatigue test results according to the nominal stress evaluation [47].
## 2 Brief Overview of Geographic CA Modeling and Tools  
The essential function of geographic CA modeling tools is to support flexible configuration of five key elements (grids of cells, cell states, neighborhood functions, transition rules, and discrete time). In general, a CA can be expressed as follows [17]:  
\[S_{T+1}=\{S_{T},\ N_{T},\ F_{T},\Delta T\} \tag{1}\]  
where the new cell state (\(S_{T+1}\)) at time \(T+1\) depends on cell state \(S_{T}\) at the initial time \(T\), the cell's neighborhood (\(N_{T}\)), the transition rules (\(F_{T}\)), and the discrete time represented by the temporal interval corresponding with one iteration of the model (\(\Delta T\)). A CA model is run in a stepwise manner, where the grid of cells, cell states, neighborhood functions, and transition rules that generate or limit changes are stored in an intermediate data layer [44]. At the end of an iteration, intermediate cell states are added to the output data layer together [45; 46]. A geographic CA is spatially explicit because it is directly linked to georeferencing principles that determine the location of the cells and their connection to Earth's physical surface. As such, it innately links geospatial data as raster GIS data layers that govern the cell states. Each iteration output can then be represented as a map generated from the spatiotemporal model simulation.
## 2 Preliminary Techniques and System Model
### Unsupervised Learning  
UL is a fundamental branch of machine learning that focuses on identifying hidden patterns and intrinsic structures within unlabeled data. Unlike SL, which relies on labeled datasets to train models, UL operates without predefined labels, making it particularly valuable in scenarios where labeled data is scarce or unavailable.  
In the context of MANETs, UL plays a crucial role in managing the dynamic and decentralized nature of the network. By analyzing various network parameters, UL algorithms can effectively group nodes based on their characteristics, facilitating efficient network management and routing decisions. This clustering process not only reduces the complexity of routing algorithms by limiting decision-making to cluster representatives but also enhances the overall stability and reliability of the network. One of the primary advantages of UL in MANETs is its ability to improve routing efficiency. By clustering nodes with similar attributes, the network can streamline the routing process, as decisions are made based on cluster-level information rather than individual node data. This hierarchical approach reduces computational overhead and accelerates the routing process, enabling the network to scale effectively even as the number of nodes increases.  
Moreover, UL contributes to the robustness and resilience of MANETs. By identifying and prioritizing stable and reliable nodes through clustering, the network can maintain consistent communication links despite frequent node mobility and varying link qualities. This selective approach ensures that data transmission paths remain intact, minimizing the likelihood of route failures and enhancing the overall reliability of the network. Additionally, UL aids in energy conservation within MANETs. By grouping nodes based on their residual energy levels and connectivity, the network can make informed decisions that prolong the operational lifetime of individual nodes and the network as a whole. Energy-efficient routing strategies derived from clustering reduce unnecessary transmissions and balance the energy load among nodes, thereby preventing premature depletion of node batteries.  
Overall, UL provides a powerful mechanism for managing the complexities of MANETs by enabling intelligent clustering of nodes based on their inherent characteristics. Its ability to enhance routing efficiency, improve network robustness, and conserve energy makes it an indispensable component of resilient routing protocols in mobile ad hoc networks.
# Evaluation Model of Holographic Communication Experience in the 6G Era Based on Light Field Display
## 5 Discussion  
In this work, we propose a model that can directly map raw light field images into subjective experience levels. Innovations are proposed from two aspects: feature engineering and training strategies. At the same time, semi-supervised learning is applied to greatly save marking costs.  
We believe that the arrival of the 6G era will realize the vision of holographic communication. However, the current light-field-display method still has flaws in terms of experience and cannot achieve the holographic display effect of science fiction movies that people desire. At the same time, compared with traditional 2D images, the number of data transmitted in light field images is increased many times, which is also a huge test for the communication network. Based on the results of this article, we can further complete the inference from experience level to image quality and then further calculate the performance sequence requirements of the communication network.
## 4 Results and Discussion
We investigate the correlation value of the cases presented in Table 2. The nomenclature of Table 2 is as follows: \(\left[AVG\,of\,\,X^{FS}-AVG\,of\,\,X^{IS}\right]\) = [AVG of \(X_{1}^{FS}-AVG\) of \(X_{1}^{IS}\),..., AVG of \(X_{69}^{FS}-AVG\) of \(X_{69}^{IS}\)]T, where AVG can be substituted by STD, and X can be a vector of the set [VG,VP,DG,DP]. Of the 12 possibilities presented in Table 2, for six of them \(\left|\,\mathbf{r}\,\right|>=0.7\). Therefore, the segments as a group show some relationships. For example, the difference of AVG of VP explains quite well (r = \(-0.9445\)) the difference of AVG of DP, i.e., if speed increases, density decreases. Other less obvious relationships exist, as the difference of STD of VP potentially can explain (r = 0.8125) the difference of STD of DP, in the understanding that if the difference of STD of VP is negative, then in the FS the speeds are similar (the \(vp\) from period to period) than in the IS, also, if the difference of STD of DP is negative, then in the FS the densities are similar (the \(dp\) from period to period) than in the IS. The density vs. speed relationship for the segments of the FS is presented in Figure 3, showing \(AVG\,of\,\,DG_{i}^{FS}\)vs.\(AVG\,of\,\,VG_{i}^{FS}\). We can see that the pattern of the SCSs is different from that of the SNSs since for the SCSs were considered speed and density measurements during the whole cycle of the traffic lights. The AVG of DG \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Vectors Compared** & **\% of Values \(\geq 0\)** & **\% of Values \(<0\)** & **Positive Values AVG** & **Negative Values AVG** & \(p\_Change\) (\%)** & \(n\_Change\) (\%) \\ \hline \(AVG\,\,of\,\,VG_{i}^{FS}-\) & \(\sim\)66.66\% & \(\sim\)33.33\% & 0.2864 & \(-0.1965\) & 74.4601\% & 25.5399\% \\ \(AVG\,\,of\,\,VP_{i}^{FS}-\) & \(\sim\)66.869\% & 39.1304\% & 0.2757 & \(-0.0691\) & 86.1301\% & 13.8699\% \\ \(AVG\,\,of\,\,DG_{i}^{FS}-\) & \(\sim\) & 27.5362\% & 72.4638\% & \(4.0518\times 10^{-4}\) & \(-9.7645\times 10^{-4}\) & 13.6204\% & 86.3796\% \\ \(AVG\,\,of\,\,DG_{i}^{FS}-\) & \(\sim\) & 37.6812\% & 62.3188\% & \(2.1490\times 10^{-4}\) & \(-8.1261\times 10^{-4}\) & 13.7858\% & 86.2142\% \\ \(STD\,\,of\,\,DG_{i}^{FS}-\) & \(\sim\) & & & & & \\ \(STD\,\,of\,\,DG_{i}^{FS}-\) & \(\sim\) & 17.3913\% & 82.6087\% & \(1.2161\times 10^{-4}\) & \(-8.4806\times 10^{-4}\) & 2.9305\% & 97.0695\% \\ \(STD\,\,of\,\,DG_{i}^{FS}-\) & \(\sim\) & 18.8406\% & 81.1594\% & \(1.7210\times 10^{-4}\) & \(-6.5564\times 10^{-4}\) & 5.7435\% & 94.2565\% \\ \(STD\,\,of\,\,VG_{i}^{FS}-\) & \(\sim\) & 10.1449\% & 89.8551\% & 0.0678 & \(-0.2133\) & 3.4636\% & 96.5364\% \\ \(STD\,\,of\,\,VP_{i}^{FS}-\) & \(\sim\) & & & & & \\ \(STD\,\,of\,\,VP_{i}^{FS}\) & \(\sim\) & 26.087\% & 73.913\% & 0.0915 & \(-0.1991\) & 13.9623\% & 86.0377\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of results.
# Evaluation of the Occurrence of Phthalates in Plastic Materials Used in Food Packaging
## 2 Materials and Methods
### Method Validation  
The analytical performance was evaluated in terms of selectivity, linearity, sensitivity, precision (inter-/intra-day), and recovery (as a measure of trueness). The method selectivity was evaluated by the absence of interfering peaks at the retention time (RT) of the phthalates, through the direct injection of an aliquot of the blank extract, and the solvent was injected directly. Six-point calibration curves of investigated phthalates were built in the concentration range reported in Table 1, in triplicate, to evaluate the method linearity. The evaluation was performed based on the average GC peak areas, percentage of relative standard deviation (% RSD), regression coefficients (R\({}^{2}\)), and linear ranges established for each target analyte analyzed. Sensitivity of the method was assessed through limit of detection (LOD, the lowest analyte concentration that produces a response detectable above the noise level of the system) and limit of quantification (LOQ, the lowest analyte concentration that can be accurately and precisely measured). The LOD and LOQ were determined using the residual standard deviation (Sy/x) of corresponding curves, LOD and LOQ being calculated by 3.3 Sy/x/b (b = slope) and 10 Sy/x /b, respectively, obtained in the calibration curve. Three different concentrations of target phthalates, added to the mixture of grounded packaging materials with water (1.5 g/6 mL of water), were used to evaluate the recovery of the proposed analytical method, within the linear range of the calibration curve. Seven replicates (n = 7) were carried out on the same day to determine intra-day precision (repeatability), whereas for the inter-day precision (reproducibility), five replicates (n = 5) were analyzed over six consecutive days (a total of n = 30). The recovery (accuracy) was determined as precision through the spiking of food packaging at three concentration levels. Both parameters were expressed as % RSD.
## 4 Conclusions  
In summary, we presented in the paper a calibration protocol for correcting the systematic errors in the contrast determination due to the realistic performance characteristics of X-ray detectors and the intrinsic bias of the photon assignment algorithms. Using a pinhole mask, we have verified the procedure. It is a simple scaling of the results from the droplet-based 'Greedy Guess' algorithm by using the value from an _incoherent_ speckle pattern. At an X-ray FEL, this value can be obtained by measuring an unfocused beam. Among the three evaluated photon assignment algorithms, the 'Greedy Guess' method shows superior performance when FEL source intensity fluctuations are large. The calibration procedure corrects the contrasts of a given speckle data set with a 1% level accuracy.  
While the data presented were recorded using the ePix100 detector at the LCLS, the concept and methodology is universal and generally applicable to X-ray detectors used for visibility spectroscopy and XPCS experiments at most currently operating FEL facilities. It can be potentially relevant for visibility spectroscopy measurements at synchrotrons as well. This lightweight procedure demonstrated here moreover suggests that optimizing the optical contrast level in the experimental design is of key significance to enable live feedback towards a successful XPCS measurement.  
Conceptualization, Y.S., M.S. and D.Z.; formal analysis, Y.S. and M.S.; software, V.E. and S.N.; investigation, all authors; writing--original draft preparation, Y.S., M.S. and D.Z.; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
Use of the Linac Coherent Light Source (LCLS), SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76 SF00515.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We acknowledge the Linac Coherent Light Source for the provision of the beamtime at the X-ray Pump Probe instrument and would like to thank the staff for their assistance. The authors declare no conflict of interest.
## 4 Results and Discussion
### LLaMA 2 and M-BERT Results  
The results show that LLaMA 2 (fine-tuned with LoRA) has significantly better performance than ModernBERT (M-BERT) on all evaluation metrics, with an F1-score of 96.58% and accuracy of 96.50% (as shown in Table 4). This superior performance is a result of LoRA's efficient fine-tuning process, affording LLaMA 2 great adaptability for Roman Urdu text. Due to the unstandardized form of Roman Urdu, contextual nuance capturing consequently becomes a pivotal factor in offensive language detection. LLaMA 2's average recall score of 96.50% demonstrates the model's ability to identify offensive content while missing few relevant instances, and its precision score of 96.58% means that there are few false positives.  
Conversely, ModernBERT (M-BERT) produces good performance as well, but still, its effectiveness is nt comparable with LLaMA 2's F1-score of 94.10% and accuracy of 94.67%. While M-BERT works fine for offensive language detection, it does not generalize as well as LLaMA 2 after fine-tuning it with LoRA. Similarly, the small decrease in recall and precision values for M-BERT indicates it must have had difficulty with different kinds of linguistic variations in Roman Urdu, especially when there are cases of code-mixing and informal spelling. Collectively, these results underline the suitability of fine-tuned transformer architectures for capturing the nuances of offensive language in Roman Urdu, which further helps in real-world content moderation tasks.
## 2 Materials and Methods
### Procedures
#### 2.3.2 Simulation Process
At least 3-4 elements are needed around the critical radius; hence, two different adaptive setups were chosen: one for the closing stage with a limited adaptive depth of 5 steps and another for the drawing stage with a maximum of 10 steps. On top of these limits, a minimum shell size was imposed to 0.5 mm. Figure 12 shows a top view of different mesh refinement levels at full punch displacement. The high density of elements generated around the lock bead and corners due to the high curvature of that geometry is clear. The forming simulation is interrupted at the adaptive mesh frequency chosen, and the mesh is evaluated for adaptive refinement. Each element is subdivided into one level during each adaptive cycle. The adaptive frequency is chosen to be small enough to prevent large elements from drawing over the radius before being refined. We chose to adapt the mesh every 2 mm of punch motion. Two criteria were used to control element refinement: the angle between two neighboring elements, and the curvature of the approaching tooling. The adaptive tolerance control is set to 4.0 degrees. Smaller values will produce more refined mesh, but we observed that the refinement with this value for this geometry is enough. The refinement method chosen is the one-pass method with look-forward, where a parameter indicates the minimum allowable separation distance before the tooling curvature is evaluated. This distance is also subjected to the proximity limits used in contact detection, so values larger than several sheet thicknesses will have no additional effect. Following the outlined mesh refinement procedure, a mesh convergence study was subsequently performed. Various adaptive parameter values were systematically evaluated until convergence was achieved, as evidenced by the absence of significant differences in the strain distributions across the critical areas. The measuring jig is modeled with five rigid surfaces. There are frictionless contacts within the part and those five surfaces. The fastening of the part with the jig is obtained with a rigid bolt connection model. There were two bolts each in zones (ii) and (iii) and four bolts in zone (i). The springback virtual measurement is conducted using LsDyna/Implicit R11.0.0. The need to account for gravitational force in the model arises from the part's dimensions, the thickness of the steel sheet, and the configuration of the jig support used for measuring springback. These factors collectively result in a relatively low stiffness of the component, making gravity a significant contributor to its final deformation. Ignoring gravity under these conditions would likely lead to an underestimation of the deformation, compromising the model's predictive accuracy. Figure 12: Adaptive mesh refinement. (The red box indicates the zoom area in each image).
# Simple Thalidomide Analogs in Melanoma: Synthesis and Biological Activity
## 3 Results and Discussion
### Biological Results  
#### 3.2.1 Cell Viability Assay  
The cytotoxic activities of synthesized compounds (**2a-l**) were examined against two human melanoma cell lines, A2058, derived from a metastatic site, and malignant melanoma cells, Sk-Mel28, two breast cancer cells, ER(+) MCF-7 and triple negative MDA-MB-231 and the human epithelial cervix carcinoma HeLa cells. The calculated IC\({}_{50}\) values of studied compounds in comparison with the reference drug, thalidomide, are listed in Table 1. As shown, thalidomide did not exhibit any activity on both the cell lines at the higher concentration tested. None of the compounds were shown to possess anticancer activity against the Sk-Mel28 cell line. Regarding the A2058 cell line, a slight activity was found for compound **2d**, which had no substituents on the phenoxy moiety. On the contrary, the presence of a chlorine substituent at the ortho position of the phenoxy nucleus seemed to enhance the activity since an IC\({}_{50}\) of 23.26 \(\pm\) 1.1 uM was calculated for compound **2i** on A2058 cells. A notable inhibitory effect on the viability of the A2058 cell line, with an IC\({}_{50}\) of 15.37 \(\pm\) 0.7 uM, was obtained for compound **2k**, bearing a methyl group at the ortho position of the phenoxy moiety, which performed best out of the compound in the series. Similarly, compound **2k** exerted a good antitumor activity against both breast cancer cells used in this assay (IC\({}_{50}\) = 20.99 \(\pm\) 0.8 and 22.72 \(\pm\) 0.9 uM on MDA-MB-231 and MCF-7, respectively), while no activity was recorded on HeLa cells. Compound **2i** was able to reduce the breast cancer cells growth with IC\({}_{50}\) values amounting to 25.34 \(\pm\) 1.0 and 31.2 \(\pm\) 1.2 uM on MDA-MB-231 and MCF-7 respectively, and, to a lesser extent, against cervix carcinoma HeLa cells growth (IC\({}_{50}\) = 54.5 \(\pm\) 0.8 uM). A slight anticancer activity was also recovered for compound **2d** towards the breast cancer cells MDA-MB-231 (IC\({}_{50}\) = 76.5 \(\pm\) 1.2 uM) and MCF-7 (IC\({}_{50}\) = 177.0 \(\pm\) 0.7 uM) and towards HeLa cells (IC\({}_{50}\) = 42.1 \(\pm\) 1.1 uM). No interesting activity was detected for the compounds lacking the phenoxy group against all cell lines used. To assess potential cytotoxicity towards normal cells, a screening of the compounds was performed on the non-tumoral human embryonic renal cell line Hek-293. As can be seen in Table 1, most of the compounds and thalidomide did not interfere with the normal cell viability, at least until the concentration of 200 uM and under the conditions used for this assay. A slight cytotoxicity was found for compound **2i**, though at a concentration 6-fold higher than the active one. A certain cytotoxicity against Hek-293 cell lines was found for compound **2c**, which, however, did not exhibit antitumor activity on the melanoma cells.
# Numerical Simulation of the Kelvin Wake Patterns
## 1 Introduction
[PERSON] [35] obtained a moderate efficiency by using an inexact [PERSON]'s method. Recently, [PERSON] [36] applied the Jacobian\(-\)free Newton-Krylov method to solve the fully three\(-\)dimensional nonlinear ship wave problem with high efficiency. By combining the boundary integral method with the Jacobian\(-\)free Newton-Krylov method, Kelvin wake problems can be investigated efficiently. Actually, both ship speed and ship size have effects on the wake waveform. However, most previous academics used the different Froude numbers to explore the effect of ship speed on the wake angle, paying little attention to the effect of ship size. Therefore, in order to complement the previous research, this paper proposes a numerical scheme with the mechanism of ship wave transformation to explore the effect of ship size. In this paper, based on the theory of potential flow, the boundary integral method is used to address the nonlinear free surface hydrodynamical problem. The radiation condition formulation of decay type is used to select a particular solution which does not contain the small spurious waves. Then, the Jacobian\(-\)free Newton-Krylov method with a preconditioner is adopted to solve the system of nonlinear equations. After comparing the simulation results with real ship waves, the wake angle is discussed with different Froude numbers, source strengths and disturbances. This paper aims to indicate the effects of ship speed and ship size on the waveform by imitating different Kelvin wake patterns. The rest of this paper is organized as follows: Section 2 gives the details of the problem formulation; Section 3 describes the numerical method to address the full nonlinear problem; Section 4 compares the simulation results with real ship waves; Section 5 discusses the Kelvin wake angle for the effects of ship speed and ship size; Section 6 presents conclusions and future works.