esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Analysis and Processing Method  
### Data Extraction using the CSP Filtering Method  
After extraction and processing of the original EEG and CSP-filtered data, regularize the data by channel to increase readability, and the data will have a value between 0 and 1. For analysis, the brain waves of subjects are tested from 20 sets, and 160 data are analyzed for each channel on the left and right. All data are acquired with a sampling rate of 1 kHz and divided left and right into 1000 three-dimensional matrices. Divide between right and left to form a filter matrix of \(8\times 8\) to enable the CSP filter application of \(8\times 1000\times 80\). The data of the CSP filter are organized based on the maximum and minimum variances of each matrix component. Multiply the CSP filter by the three-dimensional data and separate it from the original data to create a secondary data set. The formulas for the construction and application of filters are shown in the following Equations (1)-(5). Defining each sample of data on N channels as S gives the matrix of N\(\times\)S in each experiment, and assuming the number of experiments is 30 (N\(\times\)S), the total number of data samples obtained is 30 (N\(\times\)S). This matrix allows the value of the covariance matrix by the number of experiments to be expressed as Equation (1).  
\[C=\frac{XX^{\prime}}{transpose(XX^{\prime})} \tag{1}\]  
Obtain the covariance with the number of experiments for each class and calculate the average of the covariance by class. Since the number of experiments for the first class is 160, the mean value can  
Figure 7: EMG synchronization and EEG output and data analysis procedures.  
be obtained by adding the covariance to each of the 160 samples and dividing by the number of objects in the class.  
\[\overline{C_{i}}=mean(C_{i}) \tag{2}\]  
Adding an average covariance matrix for all classes allows the creation of a composite covariance matrix, which is calculated by Q through the eigenvector (U) and eigenvalues (\(\lambda\)) of the matrix. This white-colored conversion sequence has a unique vector with the same mean covariance matrix of the experimental data as Equation (4). When a passed brain signal is defined as Z, it can be represented as Equation (5).  
\[\text{Q}=\sqrt{\lambda^{-1}}\;U^{\prime} \tag{3}\]  
\[S_{i}=\text{Q}\overline{C_{i}}Q^{\prime},\;\text{W}=\text{Q}^{\prime}\text{B} \tag{4}\]  
\[\text{Z}=\text{W}\text{X} \tag{5}\]  
The diagram of the CSP filter applied in the previous content is briefly described in Figure 8.
## 2 Methodology
### Consensus Ranking of the Combined Docking Scores  
A total of five different consensus methods were used to combine the results of the seven docking scores and to generate a better ranking of the top lead hits. The five consensus methods that were used are:  
Rank by number (\(RbN\)) that combine the docking score of each molecule (\(s^{i}\)) using the average values over the number of the docking programs (\(n\)) [1; 29].  
\[RbN_{i}=\frac{1}{n}{\sum_{j}s^{j}_{i}}\]  
Rank by rank (\(RbR\)) makes use of the rank of each molecule (\(r^{i}\)) and averages it over all of the docking programs (\(n\)) for each of the molecules [1; 29].  
\[RbR_{i}=\frac{1}{n}{\sum_{j}r^{j}_{i}}\]  
Average of auto-scaled scores (\(AASS\)), that first normalizes each score between 0 and 1, using the minimum (\(s^{j}_{min}\)) and maximum (\(s^{j}_{max}\)) scores per each program and then averages over the number of the docking programs [1; 30].  
\[AASS_{i}=\frac{1}{n}{\sum_{j}\frac{s^{j}_{i}-s^{j}_{min}}{s^{j}_{max}-s^{j}_{ min}}}\]  
Z-score (\(ZS\)) that scales the score of each molecule (\(s^{j}\)), using the average (\(\mu^{j}\)) and standard deviation (\(\sigma^{i}\)) of the scores per each program and averages over the number of the docking programs used [1; 31].  
\[ZS_{i}=\frac{1}{n}{\sum_{j}\frac{s^{j}_{i}-\mu^{j}}{\sigma^{j}}}\]  
Exponential Consensus ranking (\(ECR\)) makes use of the exponential score of the rank of each molecule (\(r^{j}\)) and the expected value of the exponential distribution (\(\sigma\)) and them sums over all of the docking programs (\(n\)) for each of the molecules [1].  
\[ECR_{i}=\frac{1}{\sigma}{\sum_{j}\exp\frac{-r^{j}_{i}}{\sigma}}\]
## 5 Conclusions  
NOMA is an effective mechanism to accommodate a higher number of users without an increase of spectrum. This translates in a higher spectral efficiency, being especially useful in 5G scenarios where the spectrum is scarce, such as with extremely high number of IoT devices, like in future smart cities or autonomous vehicles. Nevertheless, as the number of NOMA users increases, a degradation of the BER performance tends to occur, especially for users that have higher receive power levels. Cooperative NOMA brings special added value for users with received higher power levels. With conventional NOMA, the SIC only detects, regenerates and cancels users' signals with power levels higher than those of the reference one and those with less received power levels are not cancelled, representing residual interference and degrading the performance. Since Cooperative NOMA allows the cancellation of the other interfering users and provides diversity, the performance tends to improve, especially for users with higher received power levels.  
When Cooperative NOMA is adopted, associated with m-MIMO, the MRC receiver tends to outperform the ZF and its complexity is lower than that of ZF. It was viewed that the degradation of performance that results from the increase of NOMA users can be mitigated by the increase of MIMO diversity.  
Finally, it was concluded that the combination of NOMA/Cooperative NOMA with m-MIMO, using SC-FDE signals and with the low complexity MRC receiver, is a good combination to achieve future evolutions of 5G.  
Conceptualization, M.M.d.S.; Formal analysis, M.M.d.S.; Methodology, M.M.d.S. and R.D.; Writing--original draft, M.M.d.S. All authors have read and agreed to the published version of the manuscript.  
This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020.  
We acknowledge the support of FCT/MCTES, as described above in funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Analysis of ANN
#### 3.2.1 Discussion of Parameters for ANNs and Comparison with Other Works  
Generally, the data shows better results for ANN with more data-inputs from the _enhanced_ model. This was expected from a data-quantity-dependent process such as ANN, where correlation models often vastly improve as the quantity of data increases.  
The objective of the calibration of the chosen parameters was to achieve good results while avoiding overfitting. As mentioned previously, the extent of overfitting can be judged by the difference in test and training errors. According to columns one to four of Table 1 in the Appendix A, the differences confirm a good fit with mostly no observable overfitting. For some implementations, the test error is even smaller than the training error, which is caused by the randomization of the split of the data into the two datasets. This reinforces the good fit of the implemented ANNs.  
Even if data points are removed according to the implemented strategies, no significant improvement of the predictability can be observed. The ANNs are able to tolerate these outliers and continue to deliver usable results. This is caused by the consistently occurring penetration of the weld bead, although the root formation is mostly smaller and less distinctive than these outliers. Thus, the more extreme data points, where the weld root formation was significantly higher, do not have such a detrimental impact on the data sets as a whole. Having some of these data points in the training data even helps predict the real distortion and penetration welding to some degree, as it shifts the data points as a whole towards the formation of weld roots for certain welding parameters, which reflects reality.  
Looking at similar published research, the created ANNs error rate of up to 3% for test data also proves to be comparable. [PERSON] et al. describe the errors for ANNs with a  
Figure 9: Accuracy of prediction with unedited data of output distortion with the input: (**a**) All welding parameters and amount of beads (standard). (**b**) All welding parameters and current geometry plus distortion (enhanced).  
somewhat similar method of measuring distortions at 8-9% as relatively low [24]. [PERSON] et al. achieved an error rate of 2.4-5.3% with a prediction method for a different method of distortion measurement for the joining of steel plates [25]. [PERSON] et al. shows mean errors for test data in a range from 1-14% depending on the output data with experimental outliers removed by hand and a combined approach with FEM [18]. In Summary, good to very good results have been achieved for a prediction model without large datasets or assistance of a simulation model. The focus on transferability of the presented methodology has no negative effect on the accuracy compared to existing research.
## 5 Design, Structure, and Initial Results of the R&D Agenda
### Initial Findings and Practical Insights from Development Stages  
The early stages of the R&D agenda have generated initial findings and insights that demonstrate alignment with the primary RQs. Development-Oriented Projects have resulted in practical consulting tools and resources that support small business cybersecurity, while Research-Oriented Projects have produced preliminary insights into effective Human-AI collaboration and iterative refinement.  
While Human-AI collaboration offers substantial benefits for productivity and scalability, several limitations must be acknowledged. These include the risk of overreliance on AI-generated content, the opaque reasoning process of LLMs, and the potential for contextual misinterpretation in nuanced cybersecurity scenarios. To mitigate these challenges, we ensure human consultants retain oversight, validate outputs, and iterate on AI-suggested content. Prompt transparency, stakeholder co-design, and feedback loops are embedded in the workflow to ensure the AI's role remains assistive rather than directive.  
These initial outcomes provide a foundation for continuous improvement in both consulting practice and academic research, supporting the iterative development of the agenda. They also validate the agenda's structure as a flexible framework that can adapt to the evolving needs of small businesses while contributing to capstone education by engaging students in real-world cybersecurity challenges.
However, minimal work has been reported discussing the influence of numerical aperture of objective lens on molten area specifically, while it has been reported that experiments with spherical aberration correction can get a stable and larger molten area than those without spherical aberration correction [19]. Besides, in order to adapt fusion micro-welding of glass material by ultrashort pulsed laser for industrial applications, stabilizing the welding process is required. In this study, a picosecond pulsed laser of 1064 nm in wavelength and 12.5 ps in pulse duration was tightly focused inside the borosilicate glass by objective lenses of various numerical apertures with spherical aberration correction. The numerical apertures of objective lenses were 0.45, 0.65, and 0.85, respectively. Influence of numerical aperture on molten area formation was experimentally investigated through analysis of focusing situation in glass, movement of absorption point, and molten area characteristics. Firstly, the analysis results of ray tracking for numerical apertures and the measurement results of diameter of laser spot focused in glass were discussed. Then, the formation phenomena of molten area were recorded by high-speed observation of laser beam irradiated area, and the absorption rate was measured by the power meter. Finally, molten area characteristics such as the state, size, and shape were characterized.
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
## 4 Experimental Setup
### Real-Time Implementation of KNN-Based Demodulator on Low-Cost FPGA  
The implementation of the algorithms was done on a low-cost and high-performance platform. The design space exploration was performed on a low-cost architecture because this stage implies long development times. Also, the designs are small scale with relatively short synthesis times, allowing a broad exploration and verification of the algorithm's parameterization. Additionally, the high-performance platform used in this work was AWS-FPGA, which implies costs by use.  
The selected low-cost platform was the Ultra96. The board has a chip Xilinx Zynq UltraScale+ MPSoC ZU3 EG A484 with a heterogeneous architecture integrating a Quad-core ARM Cortex-A53 MPCore microprocessors and programmable logic based on FPGA. Specifically, 141 K Flip-Flops (FF), 71 K Lookup Table (LUT), 1.8 M of maximum distributed RAM, and 360 DSP slices. This architecture facilitates the comparison with the offline experimental results because the ARM quickly loads the stored data. The microprocessor read the training data and the symbols from the files and controls the start and the stop of the KNN IP core. The KNN was implemented in the FPGA using Vivado HLS (High-Level Synthesis).
# Evaluation of Tooth Movement Accuracy with the F22 Aligner System: A Retrospective Study
## 3 Results
measurements, with the exception of the mandibular measure Ip2 (\(p\) = 0.002), although this difference was minimal (0.24 mm) (Table 5). Figure 6: Graphical representation of CHAID regression trees investigating the accuracy of tooth movement achieved for inclination (**A**), angulation (**B**) and rotation (**C**) movements by sex and age. All splits were identified with significance thresholds set at 0.05. n = number of observations; Y = average accuracy. Figure 7: Graphical representation of percentage of movements prescribed achieved for inclination, angulation and rotation, considering age variable (**A**,**B**). (**B**) represents the percentage of rotation movements achieved between 33.5 and 64 years of age where both sex and age have an influence. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Initial**} & \multicolumn{4}{c}{**Prescribed**} & \multicolumn{4}{c}{**Achieved**} & \multicolumn{4}{c}{**Imprecision 1 (T2 – T1)**} \\ \hline \multirow{2}{*}{**Arch**} & **Linear Mean-** & **N. of Obser-** & **Min** & **Max** & **Mean** & **SD** & **Min** & **Max** & **Mean** & **SD** & **Min** & **Max** & **Mean** & **SD** & **Min** & **Max** & **Mean** & **SD** \\  & **surements** & **vations** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline \multirow{4}{*}{**Maxilla**} & **Ic** & 117 & 28.62 & 40.39 & **33.50** & 2.54 & 29.72 & 40.63 & **34.38** & 2.09 & 30.09 & 40.51 & **34.07** & 2.08 & 0.00 & 7.04 & **0.73** & 0.86 \\  & **Ip1** & 118 & 28.26 & 50.38 & **40.24** & 3.43 & 3.35 & 50.83 & **41.51** & 2.78 & 33.38 & 50.71 & **41.18** & 2.90 & 0.00 & 5.93 & 0.57 & 0.71 \\  & **Ip2** & 115 & 34.03 & 56.76 & **45.84** & 3.46 & 54.72 & **51.66** & 46.35 & 33.42 & 54.21 & 57.11 & **46.43** & 3.32 & 0.00 & 2.14 & **0.34** & 0.36 \\  & **Im** & 118 & 37.74 & 58.86 & **48.55** & 3.76 & 38.06 & 58.95 & **49.10** & 3.62 & 37.90 & 58.89 & **48.95** & 3.59 & 0.00 & 1.73 & **0.32** & 0.38 \\  & **Ic** & 120 & 21.21 & 31.71 & **25.56** & 2.06 & 2.326 & 2.988 & **26.79** & 2.00 & 2.124 & 30.70 & **26.39** & 1.74 & 0.00 & 10.72 & 0.77 & 1.18 \\  & **Ip1** & 118 & 27.07 & 44.00 & **33.21** & 2.77 & 2.94 & 2.07 & **24.92** & 2.21 & 28.77 & **42.91** & **34.58** & 2.21 & 0.00 & 4.26 & **0.64** & 0.61 \\  & **Ip2** & 117 & 30.39 & 51.29 & **38.77** & 3.15 & 33.26 & 52.97 & **39.86** & 2.77 & 33.19 & 51.45 & **39.55** & 2.78 & 0.00 & 2.13 & **0.48** & 0.44 \\  & **Im** & 113 & 36.52 & 54.71 & **42.23** & 3.11 & 37.46 & 54.14 & **42.82** & 3.02 & 37.05 & 53.61 & **42.59** & 2.97 & 0.00 & 1.84 & **0.37** & 0.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive analysis of initial (T0), prescribed (T1) and achieved (T2) inter-arch distances and imprecision of the same (T2 – T1) for each arch.
## 4 Analysis on Air Distance, Road Distance and Road Time
### Assymetric Costs  
Another interesting challenge is to determine the differences in costs between the outbound and return trips. The cost differences of the outbound trip from A to B and the return trip from B to A are measured. The difference ratio is calculated, dividing the larger cost by the smaller cost. The results are provided in Figure 10.  
As expected, the difference ratio decreases when the distance range increases. In the shortest range where the distance range is only within 1 km, the average difference ratio can be as high as 2.13 times for road distance and 2.59 times for road time. Within the range of {0-10}, there exists a 20.6% difference in distance and a 32.9% difference in time, on average. The difference ratio in road time is computed to be larger than road distance across all boundaries, cities, and averages.  
Figures 9 and 10 illuminate the point that different cities, or different areas in a city, can have different road network characteristics. In the analyses, Busan tends to produce higher difference rates than Seoul, especially within smaller boundaries. As can be seen in Figures 5 and 6, Busan is covered with more forested areas, fields and farmlands than Seoul. In addition, the difference in the number of one-way streets can be a factor.
# Power Quality Impact and Its Assessment: A Review and a Survey of Lithuanian Industrial Companies
## 4 Results
Obviously, economic losses are experienced.\"
* \"The company has a server room, the work of which is greatly affected by voltage sags.\"
* \"The servers are protected with UPS. We are not afraid of voltage sags and supply interruptions that last up to 5-20 min.\"* \"We have a server room with 6 kW UPS. We had no losses due to voltage sags, and we did not register them.\"
* \"Shorter voltage sags do not significantly affect the work. Our server room has UPS.\"
* \"If electricity is unavailable for a longer period of time, our server room may stop.\"
* \"The servers have UPS, but the switching cabinets and computer network is fed directly [from the mains].\"
2. Comments about stationary computers and telecommunications equipment: * \"After a longer voltage sag, the work of stationary computers is disrupted and unsaved data is lost.\"
* \"Voltage sags are very dangerous to computers, may damage electronic components.\"
* \"If the computers are shut down during ship loading, the process is interrupted and data can be lost. The exact duration [of voltage sag] is unknown.\"
* \"Administration work may be suspended due to computer shutdowns, although some of them are protected with UPS.\"
* \"A certain part of our equipment is controlled via Profinet network. Problems with internet connection disrupts the work of factory, although most of the equipment has UPS.\"  
The answers about welding equipment are summed up in Table 23. In addition, two contradictory comments were received:  
* \"Our company has about 60 welding machines, and voltage sags greatly affect welding quality.\"
* \"The company's welding and soldering equipment are resistant to voltage sags. They have no influence on the work.\"  
Some companies have added additional categories of equipment--automated assembly lines (Table 24), technological process analysis and control equipment (Table 25), autoclaves (Table 26), vacuum coating machines (Table 27), and plastic production equipment (Table 28). Autoclaves are used for sterilization by using high temperature pressurized steam to kill microorganisms such as bacteria, viruses, fungi, and spores on items that are placed inside. Vacuum coating is a method of creating thin protective layers on object surfaces. One company reported about the automated assembly lines that \"assembly line robots are sensitive to voltage sags\", and the other reported about the autoclaves that \"in the case of a longer RMS voltage variation, cases of spoilage of products cooked under high pressure are probable, the quality of the cooking is affected\". One company separately answered about its EV charging stations, and marked all voltage sags as dangerous (see Table 29). However, it probably did this due to subjective interest in order to draw attention and press the DSO to take actions.
## 6 Synergy with Hybrid Communication Systems  
Combining terahertz-bandwidth FSO with other communication modalities provides a sound approach to overcome FSO's weaknesses. THz-FSO can be integrated with mi crowave or millimeter-wave systems, making an increased reliability, range, and system efficiency possible, as depicted in Figure 7.
## 1 Introduction
The new flood simulation method which is a combination of previous numerical models and field-estimated parameters, developed in this paper gives a more detailed representation of the surface flow and makes a reasonable approximation of the hydrology. Moreover, the model can predict the distribution of water height in a catchment area, as well as the height and flow discharge at the outlet. To build the model, the finite volume method (FVM)-Godunov type is adopted to solve the 2D shallow-water equation, and the monotonic upstream-centered scheme for conservation laws (MUSCL)-Hancock scheme has been associated with the model to provide high-accuracy numerical solutions to the partial differential equations, which can involve solutions that exhibit shocks or discontinuities. The main goal of using the MUSCL scheme in the model is to replace the piecewise constant approximation of [PERSON]'s scheme with reconstructed states, derived from cell-averaged states obtained from the previous time step. The structured grid was chosen for its ease of implementation, and the fluxes were computed using the Harten Lax and van Leer (HLL) approximation Riemann solution. The model is sensitive to the topography of the free surface of water flow, which results in the integration of roughness or the Manning coefficient. A virtual boundary cell is also used to compute the flux in different cells by coupling the Riemann invariant with a head-flow boundary at time M and N respectively in the x and y direction [36]. Furthermore, the model also considers the daily rainfall or rainfall data recorded in a given space, along with the infiltration parameters specific to each type of soil. Estimates of surface evaporation of the basin and the flow rate will then be integrated into the numerical equations to add further realism. The Courant number used in the stability of unstable numerical methods and the simulation time have also been incorporated into the model. To demonstrate the model's power, this work models the response of the Tongo-Bassa catchment in Douala-Cameroon by using a new model integrating the parameters from the field and validating the model by means of another catchment called Grand-Quaga in Burkina Faso. The following sections briefly discuss the 2D governing equation of the surface flow problem, before providing general information on the research area, the numerical strategy for solving it, and an examination of how well the different numerical approaches perform in the field.