esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 RFQ-Decelerator for the GSI HITRAP Facility
### RFQD Description \"as Fabricated\"  
An accelerating-focusing channel of RFQ-type, as well as a decelerating-focusing channel of an RFQ-Decelerator, is formed by four modulated rods (vanes). The period of modulation (cell length), the average distance between adjacent rods and the channel aperture vary along the structure, following the dedicated laws. As the machining data for the HITRAP RFQD rods were not available anymore, the original rods were disassembled from the tank and precisely measured, in order to get a basis for reliable beam dynamics simulations. The photometric data were used to build a 3D surface of the rods \"as fabricated\" (Figure 5).  
Figure 4: Schematic overview of the HITRAP setup.  
Based on the measured shape of the rods, a detailed distribution of the electrical potential inside the RFQD channel was built solving the Laplace equation by means of dedicated routines of the DYNAMION package. The boundary conditions were formed by all four rods, as well as by the input and output flanges. The 3D electrical field mapping was obtained as a derivative of the 3D electrical potential. The input and output fringe fields are represented as a 3D field mapping, while for the regular RFQD cells the calculated 3D field mapping is approximated with the well-known eight-term series [14]. It is essential that the bead-pull measurements of the rf-voltage along the tank have been taken into account. Finally, such represented external electrical field is used as an input for macroparticle beam dynamics simulations.  
Obviously, inside the RFQ channel a distribution of the electrical field is determined by the shape of the rods and the cavity. As a consequence, the electrical field is the same, regardless of beam injection from the low or high energy end, i.e., when RFQ(D) would be operated in accelerating or decelerating mode. Therefore, the 3D field mapping, already calculated for an RFQD in decelerating mode, could be easily transformed (mirrored) to a field mapping for the same channel but operated in accelerating mode (and vice versa).
# Effect of Stiff Hangers on the Longitudinal Structural Behavior of Tied-Arch Bridges
## 3 Stiff Hangers
### Effect of Hanger Stiffness.  
The stiffness of the hangers is the most relevant factor governing their effectiveness in terms of reducing internal forces and deflections. In Figure 11, the bending moments both for the arch and the deck are drawn for two cross-sections of the hangers: the first one is a RHS of 800 \(\times\) 800 \(\times\) 20, and the second one is a RHS of 400 \(\times\) 400 \(\times\) 20 mm.  
It can be seen how the stiffer cross-section is always more efficient. In the case shown in this example, the 400 \(\times\) 400 cross-section is less efficient than the 800 \(\times\) 800 section, even when it is fully fixed and has only one hinge.  
However, the internal forces in the hangers increases as they become more effective. Figure 12 shows the internal forces for the two cross-sections of the hangers and for the two load cases considered. It can be seen how the 800 \(\times\) 800 is the most effective cross-section, although at the expense of supporting higher bending moments for every load case.  
Figure 11: Effect of the hanger stiffness: bending moment distribution at the arch and deck.
## 2 Methodology  
### Directional Framework for Discontinuity Detection  
In sedimentary and structural settings, regions such as intra-channel zones or the same side of a fault often belong to a unified depositional system or structural unit, characterized by consistent lithologic composition, physical properties, and sedimentary architecture (indicated by the green arrow in Figure 1). As a result, when observed along the channel axis or fault strike direction, lateral geological variations are relatively subtle, and seismic reflections tend to exhibit limited structural contrast. In contrast, observation paths oriented perpendicular to the structural trend are more likely to intersect different facies belts, opposing fault blocks, or lithological transition zones, leading to pronounced geological differences. These differences typically manifest in seismic data as enhanced amplitude variations, phase discontinuities, and other strong lateral changes. This sedimentary-geological principle provides a physical basis for directional structural detection: by scanning directions and selecting the one that best highlights lateral variations, it becomes possible to more effectively delineate geological discontinuities such as faults and channels.  
In this study, we introduce a directional coherence strategy based on the principle of maximizing central trace prediction error. The core idea is as follows: if the analysis point lies on a fault or channel boundary, the predicted center trace generated from the direction perpendicular to the structural trend will differ significantly from that generated in the parallel direction. As a result, the cross-correlation between these two predicted traces will reach its minimum. Unlike traditional methods, this strategy uses prediction consistency as a direction selection criterion, making the search more geologically meaningful and sensitive to discontinuities. Moreover, it achieves optimal direction identification with reduced computational cost. The following sections provide a detailed explanation of the method's underlying principles.
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 3 FRR Port to ns-3 DCE
### FRR Extensions to Run over DCE  
In addition to the extensions to DCE mentioned in the previous section, changes were made in FRR in order to run FRR over DCE. These changes were made for practicality reasons, due to the difficulty of adapting DCE to run FRR in its original form. The changes took two forms: changes in the method of compiling and changes in the source code.  
In general, in order for an application to run in DCE, it needs to relocate the executable binary into memory. In turn, these executable files need to be built with specific options for the compilation and _linking_ stages as explained in the ns-3 DCE manual. For the case of FRR, which is a framework optimized for several different platforms and also for real networking hardware, we need to tune the compiling process.  
Compiler optimizations often use function symbols that DCE does not implement. For example, when compiling FRR with the default compiling configuration, the obtained binary uses symbols such as __strndup or pthread_condattr_setclock. Therefore, we opt to disable some compiler optimization so as to reduce the number of new functions to implement in DCE.  
Regarding source code changes to FRR, we perform some minimal modifications to avoid the usage of some unimplemented glibc's function symbols in DCE. The changes are related to the log buffering of FRR, which does not have impact on the functionality of the application. The compiling and source code changes are summarized in a compilation script available in [30].
## 2 Hydrogen-Metal Interaction and Fatigue Mechanisms
### Summarising Experimental Approaches
The general procedure is to expose a metal sample to a hydrogen-containing corrosive medium and apply a certain amount of stress to monitor the crack growth and the fracture behaviour of the metal. The microstructure and crystal defects of metal samples are analysed by using various characterisation techniques [18; 86], such as SEM, TEM, XRD, etc., to understand the effect of hydrogen on the microscopic properties of metals. They are generally used to reveal the microscopic mechanism of hydrogen-metal interaction and provide theoretical support for the hydrogen embrittlement phenomenon. The general steps are as follows: microstructural images or crystallographic data of the metal samples are collected, analysed and interpreted. Current hydrogen embrittlement experiments suffer from a variety of shortcomings. Firstly, the complexity and high cost of some methods limit the feasibility and popularity of the experiments. Secondly, the lack of a unified standardised method leads to the differentiation of experimental conditions and test methods, making it difficult to compare and verify the reliability of the results. Furthermore, some of the experimental methods cannot adequately simulate the hydrogen embrittlement properties under actual working conditions and are affected by scaling effects, while the lack of dynamic monitoring capability limits the accuracy of the experimental results. Finally, some of the methods can be carried out in the laboratory, but it is difficult to be directly applied to actual engineering, which lacks practicality. Figure 4: Four typical test experiments for hydrogen embrittlement: (**a**) general view of the UTS100K test machine and the high pressure test chamber [75]; (**b**) schematic diagram of electrochemical treatment system for hydrogen charging of 304 stainless steel [77]; (**c**) HELP + HEDE model: schematic of the transition of the HE mechanism, HELP (HESIV) \(\rightarrow\) HEDE (HESIV), for different hydrogen concentrations and applied stress conditions [61; 83]; (**d**) the parameters of stress corrosion cracking [85].
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### Deep Learning for Drones  
Similarly, deep learning technology is a promising solution that can be used to address mobility management issues of drones in mobile networks. Research has also been conducted in the literature to investigate the efficiency of deep learning for addressing the mobility issue of connected drone networks [140; 142]. Further enhancements with the use of these techniques can be achieved with the latest developments in the AI field, handover optimization, handover load balancing, handover decision making, and other aspects [37]. The precision and efficiency of predictions for resource allocation can be improved. Recent years have witnessed an overwhelming development of deep learning methods. It is now possible to integrate these fundamental methods in drone networks to address motility issues.
# Dataset Transformation System for Sign Language Recognition Based on Image Classification Network
## 4 Results
### Sign Language Recognition Results  
The generated dataset was trained on ResNet, an image classification model, and its accuracy was measured. To check the effect of data augmentation, the accuracy before and after application was compared. In the experiment comparing the effect of data augmentation, only four words were used for learning. Figure 12 shows the validation loss during training.  
It was confirmed that the loss decreased in both graphs before and after the application of data augmentation. As depicted in Figure 12, the graph before data augmentation began with an initial loss of about 4.5 and converged to a value of about 1.5 after about epoch 20, making accurate classification difficult. However, after data augmentation, the graph revealed that the initial loss began around 1.75 and continued to decrease until the last epoch. This demonstrates that the data augmentation technique we employed has a positive impact on training. Figure 13 is the confusion matrix for the test data after training.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Single Data** & **Entire Dataset** \\ \hline Original & 2.7 MB & 94.39 GB \\ Proposed method without data augmentation & 11.1 KB & 105 MB \\ Proposed method with data augmentation & 120 KB & 954 MB \\ Ratio & 4\% & 1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of words and sentences in the KETI sign language dataset.  
Figure 12: Validation Loss.  
The difference between the results before and after data augmentation is clear. When data augmentation is not applied, the amount of data used for training is 16 per word. On the other hand, the number after data augmentation is 192 per word. The results after data augmentation show about 99% accuracy in all data. Table 4 compares the accuracy measured by using the entire sign language data for training in the proposed method with the accuracy of the other two models.  
When the image data generated through the proposed method were trained on ResNet, the accuracy was 84.5%. A model using CNN and RNN combined and a model using OpenPose and GRU were used for the comparison model. Both models are used for motion recognition, and the second model is designed for sign language recognition. As a result of training the sign language video dataset on the CNN-RNN model, the accuracy was measured to be 24.0%. The model used for sign language recognition by extracting human keypoints from the GRU model had an accuracy of 52.8%.
## 6 Conclusions  
Most algorithms are proposed for processing non-stationary signals in structural health monitoring, and significant progress has been observed in models' adaptability to non-stationary signals during the iterative process. However, some unresolved issues remain in current research. Below, the prospects for signal processing technology in the field of structural health are discussed. (1) As mentioned in Section 2, real-time data processing, such as dealing with rapidly changing damage characteristics, often requires sacrificing computational efficiency. However, improving computational efficiency often leads to increased computational complexity. For instance, STFT, when processing highly non-stationary signals, exhibits inadequate capture of time-frequency signal features. Although WT offers higher computational efficiency, it has low computational complexity, while WVD's high resolution relies on instantaneous calculations, resulting in low computational efficiency. Therefore, effectively balancing computational complexity, efficiency, and resolution is crucial for models to process real-time signals and achieve practical application. Further research in this area is recommended. (2) As stated in Section 3, adaptive time-frequency analysis effectively addresses the issue of basis function selection in non-parametric processing techniques. However, these models still face parameter selection issues. For example, the performance of MEEMD is sensitive to parameters like noise bandwidth, and VMD's performance is significantly affected by parameters such as the penalty factor and the number of modes. Some researchers have manually tuned these parameters using intelligent algorithms, but critical breakthroughs are still lacking, with efforts remaining at the stage of algorithmic iteration and approximation. Therefore, it incorporating clear physical meanings of signal processing into the development of parameter optimization algorithms, rather than relying on simple numerical comparisons, is suggested. (3) Researchers have made significant efforts at denoising, but there is limited research on periodic impulse signals, which are often treated as noise and removed. (4) Most current research on fault diagnosis in signal processing can identify fault locations, such as planetary gears, sun gears, or ring gears, but some can only identify fault states from normal states. However, determining fault types such as tooth root cracks and tooth surface pitting remains challenging. Therefore, it is recommended to focus more on more precise fault diagnosis, including establishing accurate dynamic models for different fault types, designing more targeted representative features, and developing more powerful \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Applicable Scenarios** & **Optimization Methods** \\ \hline
**MCKD** & Periodic fault signals & Fault cycle estimation optimization, noise suppression \\ \hline
**FMD** & Complex signal analysis & Parameter optimization, multi-variable signal processing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## 3 Simulation Results and Analysis
### Performance Versus the Mobility of UN  
The \(CD\) and the \(CU\) of the APD-TDMA and the BTB-TDMA are evaluated under various \(V\)max from 0.5 m/s to 5 m/s when \(N=25\) and \(R=1500\) m, as shown in Figure 10. The \(CD\) and the \(CU\) of both the APD-TDMA and the BTB-TDMA are degraded as \(Vmax\) increases, because the range of the propagation delay fluctuation becomes larger for higher mobility scenario. It can be seen from Figure 10 that the \(CD\) and the \(CU\) of the APD-TDMA are better than those of the BTB-TDMA. Furthermore, similarly to Figure 9, the slope of the APD-TDMA is smaller than that of the BTB-TDMA, which shows the robustness of the APD-TDMA with respect to the mobility. On average, the \(CD_{APD}\) is smaller than the \(CD_{BTB}\) by 29%, and the \(CU_{APD}\) is greater than the \(CU_{BTB}\) by 30%.  
Figure 9: (**a**) The \(CD\) and (**b**) the \(CU\) of the APD-TDMA and the BTB-TDMA versus the network range when 25 nodes move at the maximum speed of 5 m/s.
# Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
## 4 Results and Discussion
### Model Performance Comparison  
Both stationary and nonstationary spatial relationships between property crimes and driving factors were investigated with S-GWPR models. The Local-To-Global algorithm revealed no improvement in the modeling when the nine independent variables (Table 1) were iteratively selected as global variables for the S-GWPR models because the lowest AICc (2937.661) was obtained before variable selection. Hence, the S-GWPR models were not further analyzed. Different combinations of five to nine independent variables were included in the seventeen global and local GWPR models investigated (Table 2). Models 2 to 17 were given fewer predictor variables than model 1 in order to reduce the number of parameters being estimated. The excluded variables were selected because they measure a crime factor that is also characterized by another variable included in the model and/or they have a lower correlation with property crime rates. Table 2 details the goodness-of-fit measures of those models, where the results are arranged in decreasing order of the AICc values of the GWPR models. As expected, all the GWPR models have smaller values of the AICc than the global Poisson regressions, thus revealing a better fit. The Percent of Deviance Explained was also considerably improved in all GWPR models when compared to the global models. The  
Figure 3: Results of the Getis–Ord Gi* statistic for property crime rates per thousand inhabitants by municipality in 2017. GWPR model 8 was considered the best-fit model because its adjustment measures are similar to those of GWPR model 1, which has the smallest AICc, but the GWPR model 8 is more parsimonious as it only incorporates eight predictors. All parameter estimates of the Global Poisson model 8 are significant except 'Conventional dwellings' (Table 3).
# Domain Adaptation for Arabic Machine Translation: Financial Texts as a Case Study
## 5 Conclusions  
In this paper, we conducted several experiments to assess the performance of pre-trained NMT and LLM like GPT-3.5 using data augmentation in the domain of Arabic financial news articles. Generally, the results obtained from these experiments are very promising. While ChatGPT shows good results using few pairs, other models need more examples and still have lower performance. We explored the effectiveness of all models using data augmentation in the financial domain and found that MT quality decreased for all the models adequately. Here, ChatGPT shows inferior performance, while OPUS still performs better on back-translated data than on synthetic data.  
There are many future works that can be carried out based on the findings from this study. Firstly, we would like to explore new techniques and methods to enhance translation outputs rather than the approach of data augmentation. Secondly, we think it is valuable to integrate more high-performance automatic metrics into the comparison that take semantics into consideration in a better way than in COMET and BERTScore. Finally, we will explore novel approaches to integrate additional models or even incorporate domain-specific models for improved translation performance.  
Conceptualization, E.A.A. and J.Z.; methodology, E.A.A. and J.Z.; validation, E.A.A., J.Z. and F.A.A.; resources, E.A.A.; data curation, F.A.A.; writing--original draft preparation, E.A.A. and J.Z.; writing--review and editing, E.A.A., J.Z. and F.A.A.; visualization, J.Z. and F.A.A.; supervision, E.A.A.; project administration, E.A.A.; funding acquisition, E.A.A. All authors have read and agreed to the published version of the manuscript.  
The research team obtained funding from the Translation Studies and Research Grants Program of the General Authority for Literature, Publishing and Translation at the Ministry of Culture in the Kingdom of Saudi Arabia to complete this research study in the field of translation for the year 2022.  
The human study was approved by the Ethics Committee at the English Language Institute in King Abdulaziz University (protocol code: EA23 TR2-v1, date of approval: 5 February 2023).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: We made our datasets and fine-tuned models available at [[https://huggingface.co/asas-ai/](https://huggingface.co/asas-ai/)]([https://huggingface.co/asas-ai/](https://huggingface.co/asas-ai/)) (accessed on 19 July 2024).  
**Acknowledgments:** We would like to acknowledge the support from the Center of Excellence in AI and Data Science and the Center of Excellence of High-Performance Computing at King Abdulaziz University.  
**Conflicts of Interest:** Authors [PERSON], [PERSON] and [PERSON] was employed by the company ASAS AI Lab. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.