esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Model
### Computational Grid and Numerical Method  
As shown in Figure 6, an unstructured tetrahedral grid was generated in the computational domain using Mesh from Ansys Workbench (v.2021) softwis (Ansys, Canonsburg, PA, USA). Since the blade of wind turbine is an irregular curved body model, a tetrahedron was selected for mesh division. The quality of the grid was checked and the independence was verified, and it was determined that the total number of grids was approximately 5.34 million.  
Figure 4: Yaw curves over time.  
Figure 5: Induced velocity of a yawed wind turbine.  
The one-way fluid-solid coupling method was applied in this study to calculate the simulation results.  
In the aerodynamic calculation, the effective CFD (Ansys, Canonsburg, PA, USA) calculation code was used for non-steady-state simulation calculation. The double-precision SIMPLE algorithm and standard _k-epsilon_ turbulence model were used. The rated wind speed of the wind turbine was set as the inlet wind speed, and the velocity inlet boundary condition was specified as the inlet of the computational zone. The relative pressure was set to 0 MPa at the pressure outlet, and the symmetric boundary conditions were chosen as the boundaries of the computational domain. The blade surfaces assumed a non-slip wall. The time-step size was equivalent to an azimuthal angle increment of \(5^{\circ}\), coupled with 50 pseudotime subiterations, and the value residual error was set to \(1\times 10^{-4}\).  
The interpreted UDF was imported into Fluent, and the compiled custom functions were used to control the cylindrical deflection domain in the model. The \(z\) axis was regarded as the rotating center in the cylindrical rotating domain. The \(y\) axis was regarded as the rotation center of the wind turbine. When the rotating domain of the wind turbine rotated normally, it also followed the rotation of the cylindrical rotation domain to achieve dynamic yaw. The simulation calculation of the aerodynamic load under dynamic yaw was completed.  
The structural field incorporated the aerodynamic data while accounting for the centrifugal force rotating at a certain clockwise speed and the gravity stress directed vertically downward along the \(z\)-axis. The wind turbine's rotating axis was the fixed constraint, and the stress distribution of the blades was determined after that.
## 4 AI-Enabled Intelligent 6G Network Applications
### UAV Communication  
Based on their weight, UAVs are divided into four categories: micro (less than 100 g), very small (between 100 g and 2 kg), small (between 2 and 25 kg), medium (between 25 and 150 kg), and giant (greater than 150 kg). Small unmanned aerial vehicles (UAVs) carry out routine duties similar to those found in civilian applications, whereas big UAVs perform mission-critical or defense missions. Cellular networks provide the finest support for inter-drone communication, boosting the efficacy and security of drone operations. The 6G communication network improves the reliability and connection of UAV operations outside of the direct line of sight. The denser IoT deployment of 6G facilitates IoT acceptance, as well as IoT application in blind regions. UAVs require low latency, high throughput, and dependability, which a 6G connection provides. UAV networks benefit from the enormous coverage of 6G networks, since they can host roughly ten times the number of devices per square kilometer as 5G networks [23].
## Related Methodology
### Combined Forecasting Method  
The combined forecasting method, which assigns a weighted coefficient to each individual method proportional to its past forecasting performance, can improve the final forecasting performance by taking advantage of individual forecasting methods that perform differently depending on the datasets, the forecast horizons, and their capability of capturing nonlinearity. The combined forecast model can be represented as  
\[\hat{F}_{t}=\sum_{i=1}^{n+1}w_{i}\hat{f}_{t|i}, \tag{7}\]  
where \(\hat{F}_{t}\) is the final forecast at time \(t\), \(\hat{f}_{t|i}\) is the forecast value of \(i\)th model at time \(t\), \(w_{i}\) is the corresponding weight assigned to the \(i\)th model, and \(m\) is the number of the individual models utilized. The formulation of the combined forecast model can be realized in various ways. In this study, the weights are determined based on an artificial intelligence method. Figure 1 depicts the flowchart of the proposed combined forecasting model based on the cuckoo search algorithm to optimize the weights.
## 3 Materials and Methods
### Corpus  
Although Spanish is one of the world's five most widely spoken languages, training large language models in Spanish is a significant challenge. The lack of large datasets or resources for training or evaluating models in Spanish makes this task inherently tricky. This section presents the datasets used to train and evaluate the MedicoBERT language model. These include three datasets for adaptive learning and one for fine-tuning the model in the question-answering task.  
The datasets utilized to adapt the model to the Spanish medical vocabulary are BioAsq [24], CORD-19 [25], and CoWeSe [26]. This adaptability ensures the relevance and applicability of our model. The initial dataset utilized is derived from the international competition focused on biomedicine, Biomedical Information Extraction and Retrieval (BioAsq), from the year 2021. It includes 249,497 scientific article abstracts and 45,322,119 words in JSON format, with labels such as id, title, abstract text, and year. The second dataset is CORD-19, a repository of more than one million full-text scientific articles, including content on COVID-19 and historical research related to coronavirus. The documents are  
Figure 1: Overview of the pre-training and fine-tuning of MedicoBERT.  
structured in Comma-Separated Values (CSV) format and are divided into the following labels: _cord_uid_, _source_x_, _title_, _doi_, _abstract_, _authors_, _journal_, _and url_. To adapt to the medical model, 814,402 abstracts from the CORD-19 dataset translated into Spanish were utilized. The final dataset for the pre-training of MedicoBERT is CoWeSe, which, according to its creators, represents the largest biomedical corpus in Spanish to date, encompassing nearly two million pre-processed texts and 750 million words, derived from 3000 Spanish sources. Table 2 presents a list of the corpora and the vocabulary of each dataset used for pre-training the medical model.  
The MedicoBERT model was adapted and trained on the BioAsq10 dataset from 2022 to answer medical questions. The dataset consists of 39,680 biomedical questions of a factoid type and is divided into the following sections: context, question, ID, title, and answers. Figure 2 illustrates a representative sample of the questions included in this dataset.
## 2 Preliminaries and Background
### Limited-Memory Warping LCSS Gesture Recognition Method
#### 2.4.3 Limited-Memory Warping LCSS  
LM-WLCSS instantaneously produces a matching score between a symbol \(s_{c}(i)\) and a template \(\bar{s}_{c}\). When one identical symbol encounters the template \(\bar{s}_{c}\), i.e., the \(i\)th sample and the first \(j\)th sample of the template are alike, a reward \(R_{c}\) is given. Otherwise, the current score is equal to the maximum between the two following cases: (1) a mismatch between the stream and the template, and (2) a repetition in the stream or even in the template. An identical penalty \(D\), the normalized squared Euclidean distance between the two considered symbols \(d(.,.)\) weighted by a fixed penalty \(P_{c}\), is thus applied. Distances are retrieved from the quantizer since a pairwise distance matrix between all symbols in the discretization scheme has already been built and normalized. In the original LM-WLCSS, the decision between the different cases is controlled by tolerance \(\epsilon\). Here, this behavior has been nullified due to the exploration capacity of the metaheuristic to find an adequate discretization scheme. Hence, modeled on the dynamic computation of the LCS score, the matching score \(M_{c}(j,i)\) between the first \(j\) symbols of the template \(\bar{s}_{c}\) and the first \(i\) symbols of the stream \(W\) stem from the following formula:  
\[M_{c}(j,i)=\begin{cases}0,&\text{if $i=0$ or $j=0$}\\ M_{c}(j-1,i-1)+R_{c},&\text{if $W(i)=\bar{s}_{c}(j)$}\\ max\begin{cases}M_{c}(j-1,i-1)-D,&\\ M_{c}(j-1,i)-D,&\text{otherwise}\\ M_{c}(j,i-1)-D,&\end{cases}\end{cases} \tag{9}\]where \(D=P_{c}*d(W(i),\bar{s}_{c}(j))\). It is easily determined that the higher the score, the more similar the pre-processed signal is to the motif. Once the score reaches a given acceptance threshold, an entire motif has been found in the data stream. By updating a backtracking variable, \(B_{c}\), with the different lines of (9) that were selected, the algorithm enables the retrieving of the start-time of the gesture.
# Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning
## 4 Experiments and Discussion  
### Motion Blur Restoration Experiment  
In this paper, a dataset of 500 pairs of blurred images was generated from the collected normal images. The generated blurred image datasets were used to train the DeblurGAN algorithm and the improved DeblurGAN algorithm in this paper. The peak signal-to-noise ratio (PSNR) and structural similarity ratio (SSIM) of the images were used as evaluation metrics for image motion blur restoration. The larger the PSNR and SSIM values of an image, the smaller the distortion of the image. Table 1 shows the comparison of the improved model and the original model metrics in this paper. It can be clearly seen from Table 1 that the original network structure has a PSNR index of 25.534 and SSIM index of 0.770 for blurred image recovery, while the improved model in this paper has a PSNR index of 26.562 and SSIM index of 0.861. Obviously, the improved network outperforms the original network in blurred image recovery. Meanwhile, Figure 10 shows the effect of the original network and the improved network on the recovery of the blurred digital image. It is obvious from Figure 10c that the improved network is significantly more effective than the original network in terms of blurred image recovery.  
Figure 8: The region of the LED digital meter is corrected by perspective transformation.  
Figure 9: Diagram of CRNN framework.  
Figure 10: (a) Original blurred image. (b) DeblurGAN recovered image. (c) Improved DeblurGAN recovered image.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Phase Field Modeling (PFM)  
The employment of PFM is on the rise for investigating fatigue crack propagation in materials. This method provides a sturdy framework for replicating the intricate mechanisms associated with crack initiation and propagation under cyclic loading circumstances [156; 157; 158; 159]. PFM is an advanced computational technique that effectively simulates fatigue crack growth in materials. By treating cracks as continuous fields rather than discrete surfaces, PFM allows for a more flexible approach to understanding complex crack behavior. The primary benefits of this approach include the capability to simulate complex crack patterns and interactions seamlessly, eliminating the necessity for remeshing [160; 161]. It can also monitor the progression of numerous cracks concurrently, making it well-suited for use in diverse materials and composite structures [162; 163]. Additionally, it can effortlessly integrate factors like plasticity, phase alterations, and the development of damage, facilitating a comprehensive understanding of material performance [164; 165].
###### Abstract  
System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality. These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems. Empirical Gramians are an extension to the system Gramians for parametric and nonlinear systems as well as a data-driven method of computation. The empirical Gramian framework - emgr - implements the empirical Gramians in a uniform and configurable manner, with applications such as Gramian-based (nonlinear) model reduction, decentralized control, sensitivity analysis, parameter identification and combined state and parameter reduction.  
Keywords:model reduction; model order reduction; decentralized control; sensitivity analysis; parameter identification; empirical gramians; nonlinear systems; reduced order systems; controllability; observability : 02.30.Yy
## 2 Diagnostics
### Beryllium Spectroscopy  
The beryllium line (Be II, \(\lambda\) = 527 nm) is a visible spectroscopic diagnostic [35] based on a photomultiplier tube (PMT) with a narrow band pass filter (around 2 nm). At JET, there are two Be II lines of sight, looking, respectively, at the inner and outer parts of the divertor. The sampling rate of the diagnostics is 10 kHz. The Be II is the most common diagnostic used to detect and analyse ELMs at JET considering the ITER-like wall installation. In the Be signals, the ELMs are characterised by narrow and high pulses, as it is shown by the blue lines of the top plots of Figure 2. Such a diagnostic is, however, sensitive to other atomic physical processes characterizing the SOL that cannot be separated when characterizing the shape of the ELMs.
## 3 Result and Discussion
### Rutherford Backscattering Spectrometry  
Rutherford backscattering spectrometry (RBS) was employed to study elemental compositions, concentrations, and thickness of green synthesized CuO nanocoatings. RBS is the most precise quantitative and versatile technique of ion beam analysis for elemental surface analysis and depth profiling of thin films without the need for standards [51; 52; 53]. Figure 8a-d shows the RBS spectra of CuO nanocoatings deposited on SS substrate at RS 700, 800, 900, and 1000 rpm, respectively, using a beam of 3.6 MeV alpha particles. The fitting of RBS spectra was simulated using SIMNRA 7.03 software to analyze the elemental composition and thickness of the coatings. For all coatings, two peaks of alpha particles backscattered from Cu and O atoms at channels 2424 and 1112, respectively, are observed as shown in Figure 8a-d. The elemental contents of Cu and O atoms are 54 and 46%, respectively; the results did not show remarkable changes in the content of the elements as the RS increased from 700 to 1000 rpm. The thickness of coatings was obtained in RBS unit (atoms/cm\({}^{2}\)) and calculated to be 1.406 \(\times\) 10\({}^{18}\) atoms/cm\({}^{2}\) (296.3 nm), 1.286 \(\times\) 10\({}^{18}\) atoms/cm\({}^{2}\) (271.0 nm), 1.138 \(\times\) 10\({}^{18}\) atoms/cm\({}^{2}\) (239.8 nm), and 0.985 \(\times\) 10\({}^{18}\) atoms/cm\({}^{2}\) (207.5 nm), respectively, at 700, 800, 900, and 1000 rpm.  
Figure 7: XPS spectra of (**a**) CuO (**b**) Cu2p, (**c**) O1s, and (**d**) C1s associated with 700 rpm.
## 3 Results and Discussion
### Method Comparison
#### 3.1.2 Limits of Detection  
IDT  
The investigated IDTs are designed to detect amphetamine, methamphetamine, and MDMA. Therefore, IDTs are a suitable example to examine the chosen target compounds. The information gathered from a measurement is that one of the investigated substances is present or not. No statement is made what specific target compound is found if a positive result is obtained. To gain further information about the capabilities of IDTs, the LOD was determined for amphetamine, methamphetamine and MDMA separately. The results of Table 1 reveal that clear visible indication/bar is possible down to 2 ng total target compound. In some cases, lower substance amounts down to 1 ng resulted in positive signals (slightly visible bar) as well, but were discarded due to the inconsistency of the results. In addition, all three substances yielded the same LOD. This is interesting because in most cases of on-site analysis this test only informs about a sum parameter. The knowledge that all investigated compounds can be detected, at a similar amount, eases the practical use for first responders. Cross-reactions were tested with all listed drug precursors and caffeine. No false positive results were obtained up to the investigated limit of one thousand times the LOD (2000 ng) per substance.
## 3 Results and Discussion
### Backbone Curves
The backbone curve showed three stages: elastic, elastic-plastic, and shear strength degradation. NEWRCF exhibited an elastic response, as the backbone curve is straight in the initial stage. After reaching the peak load, it showed an elastic-plastic response due to no strength degradation at a 2-6% drift ratio. However, after a 6% drift ratio, prominent longitudinal rebar buckling was observed in both the piers, leading to sudden shear strength degradation, ultimately leading to the specimen's failure.
## 4 Conclusions  
In this work, a compact rectangular patch antenna, surrounded by WCS with an inverted-L strip connected to one side of the slot, and fed by 50-\(\Omega\) CPW, was developed for triple band operations--2.4 GHz and 5 GHz WLAN, and 15.5 GHz Ku-band--with less than \(-10\) dB \(|\)S\({}_{11}\) |. Both the simulated results at the design stage and the measured results from experiments indicated satisfactory average gains--2.34, 4.22 and 4.32 dB--for the lower, middle and upper band, respectively. All of these results demonstrate that the proposed antenna fulfills the design goal completely and can be readily used in practical applications of these three frequency bands.  
Conceptualization, S.L. and S.D.; methodology, S.L., W.T. and S.D.; validation, S.L.; design and analysis, S.L. and W.T.; investigation, S.L. and W.T.; data and graph, W.T.; writing--original draft preparation, S.L.; writing--review and editing, S.L., S.D. and C.P.; supervision, C.P.; funding acquisition, S.L. and S.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Rajamangala University of Technology Isan with Contract no. Eng16/65. This work was financially supported by Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation under Grant No. RGNS 63-091.  
Not applicable.  
This research project was supported by Rajamangala University of Technology Isan.  
The authors declare no conflict of interest.
## 3 Results
### In Vitro Cytotoxicity Study  
For future commercial applications, the potential toxicity should be taken into careful consideration as the contact lenses can affect the cornea and the surrounding tissue [47]. Biocompatibility was examined by quantitative analysis of cell proliferation activity by CCK-8 assay and observation of cell morphology from confocal micrographs as referred to the literature [48; 49; 50]. The pHEMA contact lens and series of nanogel-embedded contact lenses without drug loading (pHEMA/NGs) were incubated with mouse embryo fibroblast (3T3) cell lines for between 1 and 4 days, and the calculated OD (absorbance at 450 nm) of cell cultures was in direct proportion to the numbers of viable cells. Cells cultured on TCP were used as the negative control. As shown in Figure 10a, the OD values of cells cultured onto each contact lens showed a significant increase from day 1 to day 4, with only a small gap with native control TCP, indicating that the cell proliferation activity was not significantly affected by the introduction of contact lenses. Furthermore, the OD values of cells cultured on nanogel-embedded contact lenses (pHEMA/NGs) showed no obvious difference from that of pHEMA contact lenses. The results illustrate that the embedded nanogels did not significantly influence cell viability either, in agreement with the biocompatibility of PSBMA nanogels reported in the literature [37].  
Furthermore, the morphologies of 3T3 cells grown on contact lenses for 1, 2, 3 and 4 days were observed through CLSM after staining with green fluorescence calcin-AM, with TCP as negative control. As shown in Figure 10b, the growth situation and growth velocity of 3T3 cells displayed no obvious difference when the cells were incubated on different contact lenses, which was in accordance with the CCK-8 cell proliferation results. The 4 th-day growth conditions of all 3T3 cell groups displayed a regular spindle phenotypic shape, which was similar to the nature of the morphology of fibroblasts cultured on TCP. Hence, our prepared nanogel-embedded contact lenses possess good biocompatibility, verifying their promising application as drug delivery systems.  
Figure 9: In vitro cumulative release profiles of levofloxacin. (**black**) Free drug-soaked contact lens. (**red**) Drug loaded in PSBMA nanogels. (**blue**) Contact lens embedded with the drug-loaded PSBMA nanogels. (Data were expressed as mean \(\pm\) SD, \(n\) = 3).
## 3 Prediction Models
### Decision Tree (DT)  
The Decision Tree (DT) approach is a supervised machine learning technique with the ability of being used for classifications (for non-continuous output values) and regression (for continuous output value) problems. The name of this method is inspired from the shape of a tree, where the class labels are the leaves and the features (or conditions) are the branches. The most notable asset of the DT approach is its being simple to understand, interpret, and visualize. Moreover, it has the possibility of incorporating decision techniques into the Decision Tree. Modeling the datasets with a high degree of nonlinearity in the relationship between the output and the input variables can also be performed through this method. Its drawbacks which should be taken into account are its being prone to overfitting and its difficulty in classifying multiple output classes [42; 43; 44].