esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 6 Conclusions  
We proposed a novel link-to-system (L2S) mapping method based on machine learning (ML) for 5G new radio (NR) simulators, which reduces the computational complexity as well as improves the prediction accuracy of block error rates (BLERs) prediction. The performance of the proposed ML-based L2S mapping technique was validated by utilizing link-level simulator (LLS) of the 5G K-simulator. In particular, we adopted the optimizers such Adagrad and RMSProp for obtaining parameters for effective exponential signal-to-noise ratio mapping (ESM).  
As a further study, we will investigate the ML-based decision process for the system-level simulator (SLS) to determine whether a received frame is successfully decoded without the L2S mapping procedure.  
[PERSON] performed conceptualization, methodology, and formal analysis; [PERSON] performed computer simulations; [PERSON] created the basic idea and designed the experiments; [PERSON], [PERSON] and [PERSON] wrote the paper.  
This work was supported in part by the Basic Science Research Program through the NRF funded by the Ministry of Science and ICT under Grant NRF-2016R1A2B4014834, in part by \"The Cross-Ministry Giga KOREA Project\" grant from the Ministry of Science, ICT and Future Planning, Korea, [GK 18S0400, Research and Development of Open 5G Reference Model], and in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2017-0-01635) supervised by the IITP (Institute for Information & communications Technology Promotion).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 10: Effective SNR mapping results of the proposed ML-based EESM method under 5G TDL-A \(2\times 2\) MIMO for all CQIs.  
Figure 9: Effective SNR mapping results of the proposed ML-based EESM method under 5G TDL-A \(2\times 2\) MIMO (for CQ11, CQ15, CQ110, and CQ115).
# Country-Based COVID-19 DNA Sequence Classification in Relation with International Travel Policy
## 2 Data and Method
### Method  
Figure 3 demonstrates the workflow of deep learning models used for classifying COVID-19 DNA sequences based on their country of origin. During the data processing phase, the sequences were initially downloaded and stored in nine separate FASTA files, corresponding to the countries they originate from. We conducted a two-class classification to distinguish sequences from a specific country from those of others. This is referred to as 'one-vs-all', where 1000 sequences from the country of interest were randomly selected versus 1000 mixed sequences from other countries (each contributing 125 sequences). For the country of interest that has a number of samples less than 1000, we included all available sequences in our analysis. These two groups of sequences were respectively saved in two separate TXT files. Subsequently, the sequences in each group were dissected into 200-bp units, labeled accordingly (1: unit from the country of interest, 2: unit from mixed countries), and integrated into a single file.  
In the model design phase, we trained three models: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). The nucleotides were encoded using one-hot encoding, where A is represented as [0,0,0,1], T as [0,0,1,0], G as [0,1,0,0], and C as [1,0,0,0]. The architectures of these three models are detailed in Table 3. The primary distinction among the models lies in their main layer structures. MLP employs six layers of Dense layers with a decreasing number of units through the layers, implementing a dropout rate of 0.3. CNN consists of three Conv1D layers with 100, 100, and 80 filters, respectively, each followed by a MaxPooling1D layer and a dropout of 0.2. BiLSTM employs one layer of Bidirectional LSTM, with 80 units and a dropout of 0.2.  
Figure 3: A diagram illustrating the workflow of deep learning models, including data processing, model design, model training, and model testing.  
The data are divided into training and testing sets with a split ratio of 90:10. The training set is further divided into 95% for training and 5% for the validation process. This study implemented the model using Keras based on the TensorFlow deep learning library. The loss function was optimized during model training through the Adam optimizer, with a batch size set to 1000 and the epoch set to 100. By applying the early stop strategy, training was stopped if the loss of the validation set did not decrease for 6 epochs. Performance metrics for classification evaluation for each model included accuracy, precision, recall, and F-1 score. For our implementation, we utilized the Kaggle workspace with GPU T4 \(\times\)2 accelerators to execute the code. Additionally, we employed the cross-validation technique with a 10-fold and a validation split of 0.2 to calculate classification accuracy for double assurance in evaluating the performance of the MLP model.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 4 Simulated Annealing  
The Simulated Annealing (SA) search technique is described by [PERSON] et al. (2018). The SA metaheuristic method is formulated based on the ascent of a hill. In the first attempt, a random solution is produced. In every iteration, an attempt is made to reach a solution close to the present solution. The solution which has a better fit when compared to original solution is accepted and the juxtaposition of the solutions is permitted using the Boltzmann probability (\(p\)). The Boltzmann probability, \(p\), is given as \(p=e^{-\Theta/T}\), where \(\Theta\) measures the fit between the best solution and the generated solution. The temperature parameter is represented by \(T\) and is decreased systematically in the search process according to a cooling scheme. The beginning temperature is defined as \(2*|N|\). Here, \(|N|\) embodies the total number of attributes in the data set. T is the cooling schedule and is taken to be \(0.93*T\).  
_Tournament Selection_  
[PERSON] et al. (2012) proposed tournament selection. This method starts with the selection of n random solutions from the population. After that, comparisons of the solutions are made, and then the winner is announced. For selection, the comparisons are always conducted based on probability. This results in a suitable method for accommodating a selection pressure set at 0.5. A solution with high fit value is selected, otherwise, a feeble solution is selected. The tournament selection process utilizes probability to select the most suitable solution.
## 2 Results
### Cell Viability Assay  
The effect of different SM extract concentrations, ranging between 12.5 \(\upmu\)g/mL and 200 \(\upmu\)g/mL on cell viability after incubation for 24 h, was investigated (Figure 3). The percentage of cell viability did not change significantly compared to the control (CTRL0) except for the treatment with SM at 100 \(\upmu\)g/mL, in which a significant increase (\(p<0.001\)) in cell viability (%) of NHDF (normal human dermal fibroblasts) was observed.  
Figure 3: Effect of SM extract concentrations (between 12.5 \(\upmu\)g/mL and 200 \(\upmu\)g/mL) on viability of NHDF after 24 h. CTRL0—untreated cells; CTRL—positive control (cells treated with allantoin 50 \(\upmu\)g/mL). Data are expressed as the mean \(\pm\) SD from three individual experiments. ***\(p<0.001\) versus CTRL0.  
Evaluation of the Wound-Healing Effect of SM Extract Using In Vitro Scratch Assay 2.4.1. Image Analysis  
According to the image analysis, processed images were obtained that allowed the highlighting of the migration and proliferation of NHDF and the evolution of wound healing depending on the time and the treatment applied (Figure 4).  
SM extract at a concentration of 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, and 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL indicated visually faster cell wound coverage at 24 h, while the concentrations of 25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL and 12.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL appeared similar to the controls. The ability of fibroblasts to migrate into wounds in the presence of allantoin (positive control) was similar with the low concentrations of SM extract at 12 and 24 h but was slower compared with other SM concentrations. Based on the image analysis, it can be seen that at 36 and 48 h, the wound is closed depending on the treatments used.
# Forming Difficulty Evaluation for Curved Hull Plates Based on Grey Relational Analysis
## 1 Introduction
GRA transforms the discrete behavior observations of system factors into piecewise continuous broken lines by the linear interpolation method, and then constructs a model to measure the degree of correlation according to the geometric characteristics of the broken lines. The unknown relationship between the process parameters and forming difficulty of complex curved plates was evaluated by GRA [12]. Through GRA, multiple influencing factors are transformed into the weight of the single factor of the correlation degree between each influencing factor and the difficulty of curved plate forming. [PERSON], [PERSON] et al. analyzed the influence of important parameters affecting the manufacturing process of aluminum sheet on the forming effect of aluminum sheets by using grey relational analysis [13]. This analysis verified the feasibility of grey relational analysis in the analysis of curved plate forming. In this paper, the geometric data and forming parameter data related to the forming of complex curved hull plates are calculated based on the offset data, plate data, plate seam data files and the outer hull plate forming parameter forecasting system provided by shipyards. The influencing factors of forming difficulty of curved hull plates are analyzed. The correlation between the influencing factors and the forming difficulty of curved hull plates is studied based on grey relational analysis. The weight of each influence factor is calculated. According to the correlation between the influencing factors and the forming difficulty, the difficulty coefficient of each influencing factor on the forming difficulty is quantified. Considering the weight and difficulty coefficient, the mathematical model for the difficulty evaluation of forming complex curved hull plates is established. The evaluation results of the forming difficulty of curved plates are thus calculated and the rationality of the evaluation results is analyzed. The forming difficulty evaluation for curved hull plate based on grey relational analysis can reasonably quantify the forming difficulty of different types of curved hull plates. This method can avoid the work of subjective evaluation such as expert scoring. The calculationsare relatively accurate. Ship designers can calculate the difficulty of forming curved plates during plate division. The evaluation results provide a reference for the plate division, which can reduce unreasonable plate division, save manufacturing time and construction costs. This study is very important for improving the efficiency and quality of ship construction, reducing labor and energy consumption, and enhancing the competitiveness of shipyards. It can also provide ideas for research about the evaluation of plate forming difficulty in the fields of aerospace manufacturing, chemical pressure vessels and precision manufacturing.
## 4 Discussion
### Strengths and Limitations  
The limitations of this study include the absence of data on athletes' anabolic response, as salivary testosterone was not measured, unlike cortisol and alpha-amylase. Additionally, there is a lack of data regarding athletes' psychological responses, such as anxiety or self-confidence before the race, which could potentially influence biomarker levels. Furthermore, baseline values for the studied biomarkers on non-race days were not included, despite baseline salivary and blood biomarker values being measured upon arrival. Finally, the greatest limitation of the study could be seen through the small sample consisting of only 11 rowers, due to the lack of Croatian junior and senior club rowers in general.  
The professional athlete profiles, simultaneous evaluation of three biomarkers, and measurements performed at three time points (baseline, post-warm-up, and post-race) are important strengths of this research. Moreover, the study was integrated into regular early season testing procedures, which kept athletes highly motivated, since the quality of their performance determined their chances of making the top crews in the upcoming season. The main strength comes from the fact that most scientific work is focused on a 2 km race distance, with not many studies including simultaneous measurement of salivary cortisol and alpha-amylase. Our study examined the mentioned biomarkers during a 6 km race with a greater aerobic component, allowing us to draw conclusions about hormonal and metabolic responses to specific demands of this rowing test mostly used during the early season.  
Future lines of research should aim to include certain psychological and anabolic variables to complement the current findings of the situational stress of a 6 km ergometer race, as well as include the wider sample and female rowers.
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 2 Materials and Methods  
### System Architecture  
An ultrasound measurement hardware usually consists of three components: A transducer, which converts electrical signals into sound waves and vice versa. Electronics can be used to excite the piezoelectric elements of the transducer to oscillate and, conversely, to amplify, digitize and pre-process small received signals. Finally, the data are transferred to a PC, where the main data processing, image reconstruction, visualization and analysis takes place.  
In order to realize the volumetric imaging, we used 1024-element matrix arrays, which can be controlled individually via the developed, fully integrated 1024-channel electronics. The transmit and receive beamforming was performed by a single PC, which was installed together with the four ultrasound electronics units into a single rack housing (Figure 1).  
The 1024-channel ultrasound electronics consist of four DiPhAS ultrasound research systems, each of which integrates 256 parallel transmitter and receiver elements. A dedicated timing circuit ensures high-precision synchronicity between the individual subsystems for transmit signal generation and parallel echo detection. Each of the four systems is connected to the PC via a PCIe interface. To connect the ultrasound transducer there are four connectors of the type ITT Cannon DLM6-360. Transducers with other pin assignments or connectors can be adapted via pin-out converters.  
Figure 1: Simplified system architecture of the overall 1024-channel platform.
## 4 Simulation
### The Performance of the Proposed Scheme
Figure 13: Average queue size of the re-allocation for different distances between the corresponding transmitter and receiver in a platoon \(d^{V}\left(K=3,V=10,N=5\right)\). Figure 15 shows the average queue size of the service-guaranteed users for different control parameter values \(V\). The channels are shared among multiple users and as the number of service-guaranteed users increases, the wait time increases. Figures 16 and 17 show the rate of re-allocation and the average queue size of the service-guaranteed users for different values of \(N\). With the increase in \(N\), it can be seen that the rate of re-allocation and average queue size of the service-guaranteed users increase. This is because re-allocation is necessary to provide adequate service for the increased number of users, and the service rates of users decrease with the increase in \(N\) due to the limited resources. Figure 6:0 Rate of re-allocation for different control parameter values \(V(d^{V}=10,K=3,N=5)\). Figure 6:1 Average queue size of the service-guaranteed users for different control parameter values \(V(d^{V}=10,K=3,N=5)\). From Figures 10, 12, 14 and 16, it is observed that the proposed scheme results in a lower rate of re-allocation than that achieved using Scheme 1. This is because the proposed scheme selects only some of the users to service, dropping others when the scale of demand exceeds the service capability of the resources. In contrast, Scheme 1 requires more re-allocation among all users as none of users are dropped. In addition, it is observed that the rate of re-allocation is lower at \(\lambda=1/500\) than at \(\lambda=1/1000\). As shown in Figures 2 and 3, as \(\lambda\) increases, the availability probability remains almost  
Figure 6:2 Rate of re-allocation for different numbers of vehicles in a platoon \(N(d^{V}=10,K=3,V=10)\). Figure 6:3 Average queue size of the service-guaranteed users for different numbers of vehicles in a platoon \(N(d^{V}=10,K=3,V=10)\). constant and the non-outage probability decreases. Therefore, the re-allocation at \(\lambda=1/500\) decreases with the decrease in the number of channels that satisfies \(\xi_{nk}(t)=1\).
###### Abstract  
The priority of modern dentistry is to keep patients' teeth for as long as possible. Tooth extraction is a procedure performed as a last resort when conservative methods and endodontic surgery procedures have not brought the expected results. As a consequence, the number of patients in dental offices, who require first and repeated endodontic treatment, is increasing. Thus, the development of new technologies in the medical industry, including microscopy, computer tomography (CT), as well as diode and neodymium-YAG-erbium lasers, enables dentis to increase the percentage of successful treatments. Moreover, mixed reality (MR) is a very new technology, in which the 3D view can help plan or simulate various types of tasks before they will be carried out in real life. In dentistry, 3D holography can be applied to display CT data to plan endodontic treatment. The most important element in effective root canal treatment is the precise imaging of the root canal. The CT scans allow dentis to view the anatomy of the patient's tooth with much higher precision and understanding than using 2D radiography (RTG-radiographic photo) pictures. Recently, the development of new 3D technologies allows dentis to obtain even more data from existing CT scans. In this paper, the CT scan data were applied to generate patient teeth in 3D and simulate the view of the root canal's anatomy in MR devices, i.e., Microsoft HoloLens 2. Using DICOM RAW data from the CT exam, we generated a 3D model of the jaw with a tooth. In the next step, the crown of the tooth was removed in a similar way to how a dentist would do this using a dental handpiece. Furthermore, all root canals were cleaned of everything inside. This way we achieved empty tunnels, namely root canals. Finally, we added appropriate lighting, similar to the type of lighting that dentists use. The proposed approach enables to display of the root canals in the same way as during the endodontic procedure using a microscope. It allows for the visualization of the root canal and changing its direction, in which dimensional accuracy is crucial. It turns out that mixed reality can be considered a complementary method to the traditional approach, which reduces the amount of time for the root canal treatment procedure by up to 72-25%, depending on the complexity of the case, and increases its effectiveness. Thus, the mixed reality-based system can be considered an effective tool for planning dental treatment.  
**Keywords: 3D holography; root canal treatment; tooth; augmented reality; mixed reality**
## 2 Experimental Method and Results
### Normal Dispersion Region  
Next, we discuss the characteristics in the large normal dispersion region, where the net cavity dispersion \(D\) ranged from +0.0415 to +0.1010 ps\({}^{2}\). When film A was used and the spectral filter was not used, dissipative soliton mode-locking was obtained, and pulse spectra with steep edges were observed, as shown in Figure 4a. The spectral width was at its widest in this condition. The operation was a little unstable for \(D\) = +0.0415 and +0.0503 ps\({}^{2}\). When the spectral filter was used, stable mode-locking was obtained in these two dispersion conditions. The highest output power of 75.7 mW was obtained with the spectral filter when \(D\) = +0.0503 ps\({}^{2}\). When film B was used, passive mode-locking was not obtained in this dispersion region.  
Figure 8 shows the representative characteristics of output pulses for this large normal dispersion condition. Here, the net cavity dispersion was +0.0503 ps\({}^{2}\). When the filter was not used, the spectral width was 24.4 nm, and a long-term stable operation was not obtained.  
Figure 7: Characteristics of output pulses when \(D\) = +0.0153 ps\({}^{2}\) and spectral filter was not used, (**a**) optical spectra, (**b**) autocorrelation trace, (**c**) pulse train, (**d,e**) RF spectra of (**d**) fundamental frequency and (**e**) wide range.  
Figure 9 shows the characteristics of output pulses when the spectral filter was used. In this case, stable mode-locking was obtained, and the highest output power of \(\sim\)80 mW was achieved. In the RF spectra, the SNR was about 80 dB, as shown in Figure 9d. The temporal width of the autocorrelation trace was 5.4 ps, as shown in Figure 9b, and the corresponding pulse width was 3.5 ps, under the assumption of a sech\({}^{2}\) shaped pulse. The spectral filter was effective in obtaining a stable mode-locking operation in this condition.