esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Machine Translation (MT) systems are now being improved with the use of an ongoing methodology known as Neural Machine Translation (NMT). Natural language processing (NLP) researchers have shown that NMT systems are unable to deal with out-of-vocabulary (OOV) words and multi-word expressions (MWEs) in the text. OOV terms are those that are not currently included in the vocabulary that is used by the NMT system. MWEs are phrases that consist of a minimum of two terms but are treated as a single unit. MWEs have great importance in NLP, linguistic theory, and MT systems. In this article, OOV words and MWEs are handled for the Punjab to English NMT system. A parallel corpus for Punjab to English containing MWEs was developed and used to train the different models of NMT. Punjab is a low-resource language as it lacks the availability of a large parallel corpus for building various NLP tools, and this is an attempt to improve the accuracy of Punjabi in the English NMT system by using named entities and MWEs in the corpus. The developed NMT models were assessed using human evaluation through adequacy, fluency and overall rating as well as automated assessment tools such as the bilingual evaluation study (BLEU) and translation error rate (TER) score. Results show that using word embedding (WE) and MWEs corpus increased the accuracy of translation for the Punjabi to English language pair. The best BLEU score obtained was 15.45 for the small test set, 43.32 for the medium test set, and 34.5 for the large test set, respectively. The best TER rate score obtained was 57.34% for the small test set, 37.29% for the medium test set, and 53.79% for the large test set, repectively.  
neural machine translation; multi-word expressions; out of vocabulary; word embedding; technological progress; communication technologies; technology transfer; deep learning
# Coupling Motion and Energy Harvesting of Two Side-by-Side Flexible Plates in a 3D Uniform Flow
## 3 Results and Discussion
### Flow around Two Flexible Plates in Side-by-Side Arrangement
#### 3.2.1 Coupling Motions and Vortical Structures
The three distinct modes are categorized as single-plate mode, symmetrical flapping mode and decoupled mode, respectively. To show the features more clearly, Figure 17 plots the three typical vortical patterns in the wake of two side-by-side flexible plates. In this figure, the vortical structures are plotted by the \(\lambda_{2}\) criterion, including the top view and side view in each subfigure. As shown in the top views (\(XY\) plane), all of the plates' wakes exist in a chain of hairpin-like vortical structures, which are arrayed in order along the streamwise direction. However, the wake patterns are obviously different in the side views (\(XZ\) plane). In the single plate mode, the vortex is extremely structured like the result of an isolated plate. In the symmetrical flapping mode and decoupled mode, the two rows of vortical structures are symmetrical and parallel, respectively. Figure 9.2 Time evolutions of the vortical structures of two side-by-side flapping plates at \(G_{z}=4.0\).
## 5 Conclusions  
Stress and deformation analysis and topology optimization were performed in this paper to solve the deterioration of PEMWE performance caused by deformation--a problem for the end plate--and reduce the weight of the two types of conventional circular and square end plates. Thus, light end plates were designed using the derived parameters. The weights of the light circular and square end plates were reduced compared with conventional end plates by 22.9% and 23.3%, respectively. Furthermore, strain analysis was performed to confirm the uniformity of the clamping pressure between cells. The stacks with the light circular and square end plates had confirmed strains similar to the stack applied with the conventional end plates.  
Figure 8: Maximum strain at each cell at the 21-cell stack.  
Figure 7: Strain deviation of middle and end cells of the stacks for conventional circular (black), CN0510 (red), conventional square (green), and CN10 (blue) end plates.  
Furthermore, even if the square end plates have large stress distributions compared with the circular end plates, the cell clamping pressure was distributed uniformly in each cell because of the nonlinearity of the square end plate pressure distribution. The results verified that structural strength was required and that the square end plates exhibited reduced strain due to the large stress concentration compared with the circular end plates. Thus, FEA was performed to examine the end plate weight reduction and the uniformity of the clamping pressure between the stack cells. The pressure sensing experiments validated simulation results qualitatively. The comparative analysis of the circular and the square end plates with dummy cell simulation can improve the performance of the high-pressure PEMWE by using end plates to provide gas-tight sealing and uniform clamping pressure.  
[PERSON] wrote the manuscript, conceptualized, and analyzed; [PERSON]-S.C. analyzed and validated results; and [PERSON] analyzed and supervised. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (grant number NRF-2019M3E6A1064703). The authors gratefully acknowledge partial financial support for this work by the Technology Development Program to Solve Climate Changes of the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (NRF-2015M1A2A2074657).  
The authors declare no conflict of interest.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 5 Use Cases
### Healthcare  
Since the Smart Health concept was coined as a context-aware health paradigm within Smart Cities [56], the adoption of information and communication technologies (ICT) within the healthcare sector has unstoppable grown over the years [57]. Current well-established use cases emerged from this IoT-based concept, such as remote health monitoring, patient identification and tracking, drug management, or hospital asset management [58].  
Latest IT advances such as ultra high video resolution, Big Data, and artificial intelligence will take Smart Health to a next level, enabling a plethora of innovative applications in remote diagnosis (telediagnosis), treatment (telesurgery), and recovery (telemonitoring, telelehabilitation, exoskeletons, and prosthetic hands) for a wide set of diseases. WiFi 7 and 5G will here again play an important role as enablers of novel medical wearables and devices intended for use in smart health care and home environments.  
As for the specific use cases, some of them share common characteristics with the multimedia sector (e.g., telediagnosis, telemonitoring, and telelehabilitation). Due to their criticality, some others additionally impose extremely stringent network requirements in terms of end-to-end reliability, latency, and security (e.g., telesurgery). Last, a third group involving remote motion control with relatively low traffic load requires a fully deterministic approach (e.g., exoskeletons and prosthetic hands).
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 5 Performance Evaluation
### Simulation Environment  
The simulation parameters are presented in Table 3. The simulation scenario is a 3D space of 2000 m \(\times\) 2000 m \(\times\) 1000 m. The transmission range of UAVs is set to 300 m. It means that the receiver nodes can decode the received signals correctly, when the distance between the sender and the receiver nodes is less than 300 m. The number of UAV nodes varies from 20 to 100, and the speed of UAV nodes ranges from 10 to 30 m/s. We employ the IEEE 802.11n radio standard [31], operating at the 2.4 GHz and the 5 GHz frequency band for intra-cluster and inter-cluster communications. In the radio energy dissipation model, the energy parameters of communication consumption are set as the following: \(E_{elec}=50\) nJ/bit, \(\varepsilon_{fs}=100\) pJ/bit/m\({}^{2}\), and \(\varepsilon_{mp}=0.01\) pJ/bit/m\({}^{4}\). The energy parameters of flying consumption are set as the following: \(m_{UAVs}=0.7\) kg, \(n_{w}=4\), and \(r_{w}=0.15\) m. The simulations are run on a Windows 10 operating system, with a 2.30 GHz Intel Core i7-10875H CPU and 3.2 GHz DDR4 RAM.
## 4 Conclusions  
The presented research aims to underscore the current significant deficiencies in preparing filaments for FDM devices from recycled polyvinyl butyral and the absence of standards for producing and preparing fibers or samples for various mechanical tests. Through gradual changes and the optimization of the entire process, the goal is to create a plan to develop a unified and reproducible procedure for preparing recycled polyvinyl butyral. The advantages of the presented research are optimizing the process and eliminating the resulting shortcomings; a fiber made of PVB material with a diameter of 1.75 mm (\(\pm\)0.06 mm) was created, which can be used in most FDM devices. The length of the created fiber was approx. 20 m. These will subsequently be utilized for in-depth research into their mechanical properties that are directly related to the issues of FDM technology and the subsequent preparation of samples for the still absent mechanical properties of samples produced by layering thermoplastic material.  
However, achieving this stable temperature in the conditions under which the device was placed is debatable. For that reason, we suggest the following possibilities for the further optimization of the process in the future:  
* Create a stable environment or one with a constant temperature. Ideally, the location and design of the extrusion equipment should be changed;
* Using a different arrangement of cooling unit fans and increasing their performance or alternative cooling methods;
* Support of circular economy in the manufacturing process.  
Conceptualization, J.K.; methodology, J.K. and R.T.; software, J.K. and R.T.; validation, R.T. and M.M.; formal analysis, L.K.; investigation, J.K. and R.V.; resources, R.T. and R.V.; data curation, E.S. and M.M., writing--original draft preparation, J.K.; writing--review and editing, L.K.; visualization, R.T.; supervision, L.K.; project administration, L.K. and E.S.; funding acquisition, L.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by project VEGA 1/0268/22, which was granted by the Ministry of Education, Science, Research and Sport of the Slovak Republic.  
Figure 15: Graphic representation of a tensile test of a PVB filament with a diameter of 1.75 mm at 175 \({}^{\circ}\)C.
### Research Objectives  
The primary objective of this study is to investigate the correlation between low-frequency time-code signal strength and C-class solar flares (XRA). By analyzing lag time and correlation, this study explores the linear and nonlinear relationships between XRA and signal strength across different lag intervals, as well as the disturbance patterns caused by solar flares on signal propagation paths. However, while correlation analysis reveals statistical dependencies between variables, it does not establish causality. Therefore, this study further incorporates Granger causality testing to determine whether changes in XRA can predict variations in low-frequency signal strength. Granger causality testing not only quantifies the temporal precedence of XRA events but also uncovers the deeper influence of solar flare intensity and ionospheric conditions on signal propagation. By integrating correlation analysis with Granger causality testing, this study aims to provide new insights into the mechanisms by which solar flares disturb the ionosphere and offer a theoretical reference for future research on space weather forecasting using low-frequency signals.
## 2 Materials and Methods  
### Research Material  
The selection of the 13 plant species was based on a multi-criteria approach combining ethnobotanical relevance, particularly their traditional use in herbal infusions or food preservation; reported antimicrobial properties in the scientific literature; availability in the commercial or local market; and diversity in phytochemical profiles to ensure a broad representation of chemical groups potentially involved in microbial inhibition. The research material consisted of 13 plant-based raw materials selected based on previous reports (Table 1, Figure 2). These materials included various plant parts, such as leaves (_Urtica dioica_ L.), flowers (_Tilia cordata_ Mill. ), bark (_Quercus robur_ L., _Betula pendula_ Roth, _Salix alba_ L., _Quercus suber_ L.), fruits (_Rosa anima_ L.), roots (_Taraxacum officinal_ F.H. [PERSON]), shoots (_Rubus ideas_ L., _Pinus sylvestris_ L.), herbs (_Verbena officinalis_ L.), and waste products--coffee grounds (_Coffea_ spp.) and tea grounds (_Camellia sinensis_). These plants have not yet been used in the control of opportunistic pathogens isolated from water. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{N°} & \multicolumn{1}{c}{**Name**} & \multicolumn{1}{c}{**Common Name**} & \multicolumn{1}{c}{**Material**} & \multicolumn{1}{c}{**Traditional Usage**} & \multicolumn{1}{c}{**Examples of In-Vitro**} & \multicolumn{1}{c}{**References**} \\ \hline
1 & _Coffea_ L. & Coffee & grounds & common cold, diarrhea, wound treatment & _Salucoma!a enterica, Streptococcus mtdans, K. oxyduca, E. coli_ & [28, 29, 30, 31] \\ \hline
2 & _Camellia sinensis_ & Tea & grounds & diarrhea, streptococcal pharyngitis, & _E. faecalis_, _S. aureus_, _E. coli_, _S. typhimurium_, _Bacillus cereus_, _S. Shigella absentaire_ & [32, 33, 34, 35] \\ \hline
3 & _Urtica dioica_ L. & Common nettle & leaves & skin diseases, urinary disorders, respiratory diseases & _Bacillus cereus_, _S. aureus_, _S. aureus_, _Satiphylococcus epidermidis_, _E. coli_ & [36, 37, 38, 39] \\ \hline
4 & _Tilis contact_ Mill. & Small-leaved lime & flowers & pneumonia, sore throat, diarrhea & _Candidia glaucatus_, _S. aureus_, _S. aureus_, _Bacillus subtilis_ & [40, 41] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of reports on the antibacterial properties of selected plant materials. The study on antibacterial activity utilized six isolates of opportunistic bacteria obtained from the collection of the Department of Microbiology and Plant Ecology (Bydgoszcz University of Science and Technology, Bydgoszcz, Poland): _P. aeruginosa_, _E. faecalis_, _K. oxy
## 3 Methodology
### Local Region Reclassification  
The above anomaly detection only refines the extraction accuracy of the building layer, but in fact the mixed layer still contains a large number of building objects due to the weak generalization ability of the pre-training model. Therefore, we design the local reclassification process for the mixed layer in order to extract the building objects from it.  
The random forest method is chosen as the core method for this local reclassification process. This is because it has a strong noise immunity and high classification accuracy [43]. The random forest method is a supervised classification algorithm. In this paper, the refined building layer is selected as the positive samples and the non-building layer is selected as the negative samples for the random forest method. Specifically, the sample set is randomly selected from the refined building layer and the non-building layer through an unordered sampling with a replacement strategy. This sample set is then divided into a training set and a validation set in a 2:1 ratio. Then, a corresponding decision tree model is set up for each training sample set and continues to split until all the training samples at that node are of the same type. Furthermore, the generated multiple decision trees are formed into a random forest and the best classification of the mixed layer is decided based on the voting probability. Finally, the combined random forest is used to extract building objects from the mixed layer and the newly extracted building objects are fused with the objects in the refined building layer as the final result.
# Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method
## 3 Improved 3D-VO Method for UAV Collision Avoidance
### Formation of 3D-Velocity Obstacle Set
When the obstacle avoidance mode is nearing the end, it is often the moment that the distance is close to the minimum, which can easily distort the VO cone, resulting in failing to find the correct obstacle avoidance velocity and even causing the control commands of the UAV chattering. Consequently, we introduce a \(\lambda\) angle, which is the angle between two vectors. The stage shown in Figure 2(a) is the initial stage of obstacle avoidance \(\lambda_{1}>\lambda^{*}\), where \(\lambda^{*}\) is a design parameter. Figure 2(c) shows \(\lambda_{3}<\lambda^{*}\), indicating that the avoidance mode has ended. The UAV is between the obstacle and the destination point. The stage shown in Figure 2(b) is the critical stage and \(\lambda_{2}=\lambda^{*}\). Hence, the judgment basis of the avoidance mode is redefined as  
\[\left\{ \begin{aligned} d_{UIO}\leq& d_{safe}\\ [v_{UAV}-O_{vo}]k_{vo}>&\cos\eta_{vo}\|v_{UAV}-O_{vo}\| \end{aligned} \right. \tag{15}\]
## 3 Materials and Methods
### Reducing the Classification Space by Applying a Linear Data Transformation to PCA  
PCA is one of the classical statistics methods. The main idea of this method is to perform a linear transformation and reduce the number of dimensions of the data by discarding a part of the components found after the transformation, which have the smallest variance. The PCA method is often referred to as the discrete K-L transformation because it reduces the number of data or dimensions that describe the features of the sample. In particular, the principal component analysis descriptor looks for the principal component P1 with the highest variance. This component crosses the central point of the data and is closest to all data points because the average distance from the component to the points is minimal. The second principal component P2 also crosses the central point of the data and is perpendicular to the first principal component. Each principal component is described by an eigenvector and an eigenvalue. The real vector indicates the direction of the principal component, and the real value describes how the data are distributed in that direction.  
The PCA algorithm is performed in four steps:  
* Ensure that the data have the same mean and variability.
* Find the covariance matrix \(C\).
* Calculate the real vectors \(E_{k}\) and real values \(\lambda_{k}\) of the covariance matrix \(C\).
* Sort the true values of \(\lambda_{k}\) in descending order and form the matrix \(A\) of the main components from the true vectors \(E_{i}\) according to the list obtained.  
Each gesture is measured by 8 EMG sensors, and the temporal signal is estimated using 10 statistical estimators. A total of 80 dimensional vectors are used to describe one movement. To reduce the number of dimensions and simplify the structure of the gesture classification algorithm, the PCA method was applied to the EMG data.
# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 3 Results
### ANN Model Configuration
\begin{table}
\begin{tabular}{c c c c c} \hline
**Attribute** & **model\_v01** & **model\_v02** & **model\_v03** & **model\_v04** \\ \hline API Used & TensorFlow/Keras & PyTorch [21] & PyTorch & PyTorch \\ \hline Network Type & (MLP) & (MLP) & (MLP) & (MLP) \\ \hline Model Architecture & 2-\(>\)7-\(>\)13-\(>\)2 & 2-\(>\)32-\(>\)23-\(>\)2 & 2-\(>\)25-\(>\)10-\(>\)2 & 2-\(>\)28-\(>\)18-\(>\)2 \\ \hline Activation Function & ReLU & Sigmoid & LeakyReLU & LeakyReLU \\ \hline Loss Function & (MSE) & (MSE) & (MSE) &
\begin{tabular}{c} Weighted MSE \\ alpha = 2.55 beta = 1 \\ \end{tabular} \\ \hline Optimizer & Adam (lr = 0.001) & RProp (lr = 0.01) & Adam (lr = 0.001) & Adam \\ \hline Batch Processing & Batch size = 8 & Whole batch & Mini batch (size = 8) & Yes, batch size = 8 \\ \hline Early Stopping & Not implemented & No & Yes, patience = 90 & Yes, patience = 5000 \\ \hline \end{tabular}
\end{table} 
Table 2: ANN models’ characteristics and attributes. \begin{table}
\begin{tabular}{c c c c c} \hline
**Metrics** & **model\_v01** & **model\_v02** & **model\_v03** & **model\_v04** \\ \hline Weighted MSE (scaled) & 0.14565 & 0.062322 & 0.104295 & 0.04118834 \\ \hline MAE on I [A] & 0.480575 & 0.222459 & 0.403485 & 0.08838405 \\ \hline MAE on P [kW] & 0.139786 & 0.068018 & 0.130191 & 0.04480091 \\ \hline Best Validation Loss & 0.236316 & 0.069592 & 0.204775 & 0.02009488 \\ \hline R\({}^{2}\) Score for I [A] & 0.844709 & 0.937193 & 0.893362 & 0.99090905 \\ \hline R\({}^{2}\) Score for P [kW] & 0.958116 & 0.978576 & 0.965621 & 0.99525561 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance metrics of the models. Each iteration in this development pathway contributed to progressively improving model accuracy and robustness. The incorporation of advanced activation functions, adaptive optimizers, and custom loss formulations demonstrates a systematic refinement process tailored to the specific characteristics of machining data. These enhancements collectively led to superior predictive capabilities, setting the stage for effective deployment in real-time monitoring and adaptive control scenarios.
## 3 Results and Discussion
### Tests with Two Modules with 16-bit SAR Converters and Two Chassis
For 2 m and 10m lengths, \(\pm\)63 ns and \(\pm\)68 ns were obtained, respectively. In both cases, 100% of the measurements were below 80 ns. Using the probability density plotted in Figure 13, it was observed that the time series obtained did not correspond to a normal distribution. This occurred mainly in the tests carried out with two consecutive channels for the same module with SAR converters. The tests with TSN synchronism did evolve with a curve very close to a Gaussian bell. The mode in both cases was close to the median. In Table 5, all results with an uncertainty equal to 5% for the two types of architectures under equivalent test conditions have been grouped. Simultaneous sampling with cDAQ-9185 chassis was achieved for the SAR architecture with average values of 25 to 31 ns, and for the Sigma-Delta with average values of 20 to 55 ns. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Test: 50 Hz, 20 Vpp, and Sampling Fs = 50 kS/s**} \\ \cline{2-5}
**Phase Errors for TSN (ns)** & \multicolumn{4}{c}{**16-bit SAR Converters**} \\ \cline{2-5}  & **Slot: 1** & **Slot: 1–2** & **Chassis: 1–2** & **Chassis: 1–2** \\  & **C0–C1** & **C0–D0** & **Cable 2 m** & **Cable 10 m** \\ \hline Median (Me) & 10.3 & 14.95 & 25.63 & 30.72 \\ \hline Mode & 2.03 & 10.33 & 28.18 & 27.35 \\ \hline Variance (\(\sigma^{2}\)) & 82.37 & 113.71 & 168.43 & 185.66 \\ Standard Deviation (\(\sigma\)) & 9.07 & 10.66 & 12.98 & 12.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Phase errors for SAR converters. Figure 12: Box-and-whisker plot for SAR converter tests. As a conclusion, a comparison with the phase errors for a 95% confidence interval has been included in Table 5 and it was observed that it rigorously complies with the specifications for the synchro-phasor standard. It achieved a degree of accuracy well above the specifications for a distributed measurement system with equipment that is synchronised to the TSN standard and that can be separated by 10 m.