esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Circuit Design of AC/DC Converter for T8 LED Tube
## 4 Experimental Results
The variation of the output current is less than 1%, which is great performance, since the power consumption tolerance of the LED tube is usually \(\pm\)10%. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multicolumn{4}{c}{**Reference**} & \multicolumn{4}{c}{**Proposed**} \\ \cline{2-10}  & \multicolumn{3}{c}{**Light Output (mV)**} & \multicolumn{2}{c}{**Percent**} & \multicolumn{2}{c}{**Light Output (mV)**} & \multicolumn{2}{c}{**Percent**} \\ \cline{2-10}  & \multicolumn{3}{c}{**Flicker**} & \multicolumn{3}{c}{**Flicker**} & \multicolumn{3}{c}{**Flicker**} \\ \cline{2-10}  & **Hz** & **MAX** & **AVG** & **MIN** & **(\%)** & **MAX** & **AVG** & **MIN** & **Flicker** (\%)** \\ \hline \multirow{3}{*}{100} & 50 & 450 & 393 & 336 & 14.5 & 401 & 393 & 385 & 2.0 \\ \cline{2-10}  & 60 & 443 & 394 & 345 & 12.5 & 402 & 395 & 388 & 1.8 \\ \hline \multirow{3}{*}{240} & 50 & 451 & 394 & 337 & 14.5 & 401 & 393 & 385 & 2.0 \\ \cline{2-10}  & 60 & 443 & 394 & 345 & 12.4 & 400 & 393 & 386 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of percent flicker. Figure 8: Power efficiency and efficiency difference according to the input voltage. Figure 10 shows the measured power factor in 100-240 VAC at 60 Hz. There is no large difference between the reference and proposed LED converters, which means the variation of the power factor is not affected by the output ripple eliminator. Due to the influence of the input current harmonics, it slightly decreases as the input voltage increases. Nevertheless, the power factor is still higher than 0.84 under all input voltages. Figure 11 shows the THD of the proposed LED converter at 50 Hz and 60 Hz, which is less than 14.3% under all input voltages. The values of THD at 100 VAC are 8.52% and 8.36% at 50 Hz and 60 Hz, respectively. At 240 VAC, the values are 11.44% at 50 Hz and 14.26% at 60 Hz, respectively. The measured results are slightly high at 60 Hz, but they are very suitable for a high-quality LED tube. Figure 10: PF at 60 Hz. Figure 9: Output current according to the input voltage. Figure 12 shows the measured results of the harmonic current at 100 VAC at 50 Hz and 60 Hz. The maximum permissible harmonic current from the IEC 61000-3-2 Class D standard for less than 25 W of lighting is presented with a gray bar [28,29]. The harmonic current is markedly lower than the IEC standards.
## 2 Instrumentation Methods
### Turbine Instrumentation  
The turbine is instrumented to determine the total adiabatic efficiency, the total degree of expansion or a certain inlet or outlet turbine temperature, depending on the type of test  
Figure 1: Bellmouth instrumentation method scheme, model (**a**), bellmouth instrumentation method scheme, model (**b**).  
Figure 2: Compressor instrumentation method scheme, model (**a**); compressor instrumentation method scheme, model (**b**).  
requested. Turbine total adiabatic efficiency [9] is the ratio between the total specific actual work (\(l_{T.t}\)) and the total specific ideal work (\(l_{T.t.id}\)). The total specific actual work [9] (\(l_{T.t}\)) is the difference between the inlet total specific enthalpy (\(h_{3.t}\)) and the outlet total specific enthalpy (\(h_{4.t}\)).  
\[\eta_{T.t}=l_{T.t}/l_{T.t.id}=(h_{3.t}(T)-h_{4.t}(T))/(h_{3.t}(T)-h_{4.t.id}(T)) \tag{3}\]  
According to the engine instrumentation, in the vicinity of the turbine gas temperature probe location, an accessible instrumentation method, as presented in Figure 3, is used to determine the total adiabatic efficiency. The outlet total pressure (\(p_{4.t}\)) must be measured, and it is also necessary to measure the total outlet temperature (\(T_{4.t}\)), since the total inlet temperature (\(T_{3.t}\)) can be determined from the energy equation applied on the combustion chamber. This happens if the total inlet temperature (\(T_{3.t}\)) is not measured.  
The method presented in Figure 3a requires only the total outlet turbine pressure and temperature parameters, and it is not necessary to know the outlet turbine cross-section in the calculation algorithm. The method presented in Figure 3b requires for the static outlet turbine pressure to be measured. It is mandatory to know the outlet turbine cross-section, used in the calculation algorithm.
## 2 Materials and Methods
### Procedures
#### 2.4.3 Maximum Punching Velocity Evaluation  
To assess maximum punching velocity (PV_max), both rear arm (RA) and lead arm (LA) punches were analyzed using the validated Tendopower analyzer (Sorinex Technologies, Burton on Trent, UK). The device recorded punch velocity data at a high sampling frequency of 1000 Hz to capture rapid movement dynamics accurately. A fourth-order, zero-lag Butterworth low-pass filter with a cutoff frequency of 12 Hz was applied to the raw data to reduce high-frequency noise while preserving the essential components of punch velocity signals, consistent with standard biomechanical data processing practices [40,41]. The cutoff frequency was chosen based on prior research demonstrating that upper-limb movement frequencies during punching generally do not exceed 15 Hz [42], thereby minimizing the attenuation of true peak velocities. Before testing, participants completed a standardized warm-up consisting of 20 shadow punches followed by introductory punches on the Hardcore heavy bag (Charlie Equipment, Madrid, Spain) [43]. To capture punch velocity, an accelerometer was securely affixed to the wrist inside the boxing glove using a Velcro strap, ensuring minimal movement artifacts. Each boxer executed three maximal-effort repetitions of both RA and LA punches. Reliability analyses within this sample revealed intraclass correlation coefficients (ICCs) of 0.898 for LA PV_max and 0.910 for RA PV_max, indicating excellent test-retest reliability [44]. These results align with previous validation studies confirming the Tendopower system's accuracy and precision in measuring punch velocity in boxing [45]. The application of this validated sensor technology and data processing method provided accurate and reproducible PV_max measurements, which are essential for evaluating punch effectiveness, a critical determinant of competitive success in boxing [46].  
Reliability: Both lead arm (LA) and rear arm (RA) PVmax were evaluated using the validated Tendopower analyzer (Sorinex Technologies, UK). Each punch was repeated three times. The accelerometer recorded punch velocity at a 1000 Hz sampling rate. Within this cohort, test-retest reliability was ICC = 0.898 for LA and ICC = 0.910 for RA, indicating strong internal consistency [21].
# Study of High-Frequency Pulsating Flow Control Valves
## 1 Introduction
The experimental results show that the delay time of the valve is always within 3.5 ms, and the final amplitude frequency results show that the cutoff frequency is about 15 Hz [15]. [PERSON] et al. developed a constant differential pressure fuel flow control servo valve utilizing the 2D principle, in which the valve spool can make linear motions along the axial direction while rotating. The valve has high linearity and a fast step response, and its dynamic response frequency can reach 38 Hz [16]. [PERSON] et al. used a fuzzy PID controller [17] with an integral separation fuzzy proportional-integral differential controller [18] and utilized these two methods to improve the valve's static-dynamic characteristics and frequency response. [PERSON] et al. used the position control non-minimum phase model inverse feedforward controller on a multistage flow valve, and the results showed that the controller had the smallest phase delay, and the frequency of the phase lag of 90\({}^{\circ}\) was about 46 Hz [19]. In addition, a number of scholars use the magnetorheological body, piezoelectric ceramics, and super magnetostrictive and other emerging smart materials instead of the traditional proportional solenoid [20; 21; 22]. They use these materials to develop flow valves with the advantages of high control accuracy and high-frequency response; however, these output flows are very small and only suitable for ultra-precision machining and other fields [23]. In this paper, a HPFCV (high-frequency pulsating flow control valve) consisting of a constant differential pressure-reducing valve and a sinusoidal flow valve is proposed to generate sinusoidal flow signals at higher frequencies. The paper is organized as follows: in Section 2, the working principle of the high-frequency pulsating flow control valve is presented; in Section 3, its mathematical model is developed; in Section 4, an experimental prototype is developed to test the performance of the valve, and a comparative study is carried out with respect to the simulation results.
# Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
## 4 Experiments and Results
### Experiments with Different Training-Test Dataset Rates  
In order to evaluate the performance of the proposed model, we conducted experiments with different training-test dataset ratios. 10 sets of experiments were performed for each ratio with randomly selected image sets according to the selected training/test ratios. By calculating the arithmetic mean of the 10 different \(m\)AP results, the final \(m\)AP value was obtained. Table 1 shows the arithmetic mean results of the experiments by adjusting the dataset according to these ratios.  
When the results in Table 1 were examined we observed that with the further reduction of the number of images in the train dataset, the performance was significantly reduced (success is reduced almost 40%). However, it can be seen from the results that the model we proposed shows very successful performance with little data (0.812 \(m\)AP rate with 30% training data). The most successful rate is 70%-30%, which is widely adopted and used frequently in the literature. Increased training data is expected to increase success, while overfitting and lack of test data reduce success (e.g., 90-10 and 80-20 rates). The PR Curves obtained arithmetic mean results of the experiments performed according to the ratios in Table 1 are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Training–Test Rate (\%)** & **90–10** & **80–20** & **70–30** & **60–40** & **50–50** & **40–60** & **30–70** & **20–80** & **10–90** \\ \hline
**Arithmetic Mean \(m\)AP** & 0.870 & 0.871 & 0.923 & 0.907 & 0.892 & 0.841 & 0.812 & 0.677 & 0.587 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect of different training-test data rates on performance.  
Figure 8: Precision-Recall curve for different train/test rates.  
When the PR curves are examined, it is seen that the lack of data directly affects the performance. This effect becomes more pronounced in the F-measurement results. The proposed faster R-CNN model is stable, despite significant changes in dataset rates. This can be attributed to the fact that the deformable convolution structure extracts attributes from more domains and the FPN gives the attribute from different levels to the detection network.
## 2 Related Works
For example, in the article [51], the authors proposed a research method that was based on combining three different time-frequency representations of signals by computing the continuous wavelet transform, mel spectrograms and gammatone spectrograms. Then, they were combined into 3D channel spectrograms. Thanks to the conducted research, applications were found in the automatic detection of speech deficits in users of cochlear implants. The second application turned out to be the recognition of phoneme classes to extract phone-attribute features. The paper [52] proposed an approach to the problem of classification of noisy nonstationary time series signals based on the class of their Cohen time-frequency representations (TFR) and deep learning algorithms. The authors of the research presented an example of detecting gravitational wave (GW) signals in intense, real, nonstationary, nonwhite and non-Gaussian noise. The test results showed that the approach in which deep CNN architectures were combined with [PERSON]'s class TFR allowed to obtain satisfactory algorithm performance. Article [53], however, is similar in scope to the problem discussed in this article. It also analyzed EEG signals. An approach based on the automatic extraction and classification of features using various convolutional neural networks was used. The results of the research showed that a configurable CNN required much less learning parameters and gave a better accuracy. As in this article, the EEG signals were also filtered; however, in that study, they were converted into an image using the time-frequency representation. Table 1 summarizes the related work and the keywords that can be associated with it. This article is divided into several sections that effectively discuss all issues related to the topic of interest. Initially, a set of components is presented, as well as the purpose of the research and the structure of the expert system. Next, the preclassification part of the EEG signal is discussed. The details of the artificial intelligence algorithm implemented in the system are also explained. Subsequently, the results of the classification are presented. The article ends with conclusions and a discussion of future plans and work related to this research.
# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 4 Case Studies and Applications
The study integrated feedback to improve bus service reliability and real-time adaptability, which highlights the role of user-centered approaches in enhancing DT effectiveness. A study in Mountain View, California testing personalized adaptive cruise control by applying a Mobility DT (MDT) based on an AI data-driven cloud-edge framework demonstrated an improvement in the level of driver comfort on the road by approximately 8.4% (events of drivers applying the brake or acceleration to take over the cruise control occurring 86.4% less frequently) in 2021. End-to-end testing demonstrates that the proposed MDT framework can efficiently handle complex cloud computing tasks using bulk data, with minimal delay. This improves the performance of vehicle applications. For real-time applications, the MDT framework demonstrated that the latency is under 80 milliseconds, which is crucial for safety and time-sensitive operations [35; 63]. In another study, where authors implemented a DT for an electric vehicle to study the interaction with an urban DT environment, real-time and static data were collected from various sensors to analyze performance, energy usage, and sustainability. The study found that there was a distance estimation average percent error of only 1.8%, corresponding to 5 m. This indicates high accuracy in the mapping module, which can be very beneficial when implemented by the bus transit system for real-time route optimization. The authors also concluded that the incorporation of a LIDAR-IMU algorithm can further reduce the error to 1% (less than 2.75 m distance variation). While the implementation of such technologies is still pending, the integration of DTs can enable decision-makers to promote smart urban planning [64; 114]. In summary, the deployment of digital twin technology in bus transport systems has led to measurable improvements in operational efficiency and maintenance practices. The DT case studies discussed were selected based on their relevance to practical and real-world implementations. By leveraging real-time data and predictive analytics, DTs can enhance service delivery and contribute to a passenger-friendly urban transportation system.
## 6 Conclusions and Future Scope  
The results obtained allow us to affirm that the selected machine learning techniques presented an efficient predictive capacity, mainly due to the linear relationship between academic features and student performance. Eleven different models were used. However, among all these, the XGBoost is recommended because it shows better performance indicators, both in Accuracy as well as in Precision and Sensitivity. The key factors for classifying the academic performance of a student at the Peruvian university are the number of Failed Courses, together with the grades of the first and second year, which are decisive--that is, if we want the student to have a performance. Optimal action must be taken in the first two years and corrected so that in the following years, a good academic performance can be maintained according to the forecast of the model. This visual-predictive model represents a promising alternative for the identification of factors involved in academic performance for the Peruvian university, which does not have any identification method yet.  
There are studies that have demonstrated the existence of factors external to the academic trajectory, influencing in an important way the academic performance of the students [46]. In this sense, another look at the contribution of this study corresponds to the fact of introducing additional factors, such as financial and demographic indicators, to the analysis. In this way, for future work, information on the academic curriculum, and demographic and socioeconomic data of the students can also be incorporated. The incorporation of these factors enriches the mechanism for detecting students with academic problems, providing valuable information to develop strategies and activities for students who need to increase their academic performance. Analogously, the tools presented in this study make it possible to measure academic performance under categories, and this places universities at a higher level of maturity, as referred to in [47]. Finally, the proposed visual-predictive approach can be considered for an initial LMS for the Peruvian university in order to systematize the academic support to their students.  
Conceptualization, D.O.G. and J.L.L.-G.; methodology, D.O.G., J.L.L.-G. and R.S.; software, R.S. and J.U.; validation, J.L.L.-G. and R.S.; formal analysis, J.L.L.-G., R.S. and R.T.; investigation, D.O.G. and J.L.L.-G.; resources, D.O.G., J.L.L.-G., R.S. and R.T.; data curation, J.U.; writing--original draft preparation, D.O.G., J.L.L.-G., R.S., J.U. and R.T.; writing--review and editing, D.O.G., J.L.L.-G., R.S., J.U. and R.T.; visualization, D.O.G., J.L.L.-G. and J.U.; supervision, J.L.L.-G. and R.S.; project administration, J.L.L.-G. and R.S.; funding acquisition, J.L.L.-G. and R.S. All authors have read and agreed to the published version of the manuscript.  
This research was partially founded by Chilean ANID FONDECYT grant number 1221938 and ANID-Millennium Science Initiative Program ICN2021-004.  
Not applicable.  
The authors declare no conflict of interest.
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 3 The Proposed Fingerprinting Acoustic Localization Approach
### Two-Stage RPs Matching  
The vocal target can be located in the positioning area after the RPs sampling and the database construction. In the positioning phase, a two-stage matching algorithm is proposed to compare the feature vector of vocal target \(\mathbf{F}\)=\([f^{1},f^{2},\cdots,f^{M}]\) with each simple in database \(\mathbf{D}\) to find the adjacent RPs with the minimum matching error.  
The Euclidean distance between target point \(\mathbf{F}\) and cluster center \(\mathbf{\mu}_{i}\) of cluster \(\mathbf{C}_{i}\) can be defined by:  
\[Dis_{i}\)=\(\left\|\mathbf{F}-\mathbf{\mu}_{i}\right\|_{2},\quad i=1,2,\ldots,K. \tag{1}\]  
The adjacent cluster can be chosen through:  
\[\mathbf{C}_{a}=\mathbf{C}_{\operatorname*{arg\,min}_{i\in\left\{12,\ldots,K\right\}}Dis _{i}} \tag{2}\]  
Then, as shown in Figure 2(b), the adjacent RPs can be searched according to the Euclidean distance \(dis_{j}\) between the target point and each sample of the adjacent cluster in feature space.  
The distance can be defined as:  
\[dis_{j}\)=\(\left\|\mathbf{F}-\mathbf{F}^{j}_{a}\right\|_{2},\quad j=1,2,\ldots,n_{c}. \tag{3}\]where \(\textbf{F}_{a}^{j}\) is the feature vector of the \(j\)th RP in adjacent clustering \(\textbf{C}_{a}\), and \(n_{c}\) denotes the total number of samples in \(\textbf{C}_{a}\). Adjacent RPs set \(\textbf{D}_{a}\) can be gathered by:  
\[\textbf{D}_{a}=\textbf{D}_{a}\cup\textbf{S}_{\text{arg}\min_{j\in\{12,...,n_{c} \}}dis_{j}} \tag{4}\]  
Because the complexity of matching process is far greater than the other parts of the location process, and the computation complexity of the other parts in the positioning process is almost the same, the computational complexity of matching operations is investigated in this paper. \(N\) denotes the total number of RPs in the database and \(K\) is the number of clusters. The complexity of linear matching process is \(O\left(N\right)\), and the average complexity of matching process based on cluster analysis is \(O\left(N/K\right)\). Comparing with the conventional matching method, the proposed approach can reduce the complexity of matching process to its \(1/K\).  
Firstly, as Figure 3a shows, adjacent cluster is determined based on the Euclidean distance between the target point and each cluster center in feature space.
## 4 Methodology
### Proposed Multi-Objective Hybrid NSGA3
The population diversity of the NSGA is twisted and first uses the forward selection then the best subsets of features are used as backward elimination within the non-dominated concept; specifically, NSGA2 uses its adaptive ability via a distance crowding operator. On the other hand, the NSGA3 maintains solution diversity. Lastly, they are fed into the bi-directional wrapper-based approach for both NSGA3 and NSGA2. The complete stages in the framework for the proposed multi-objective hybrid NSGA3 are depicted in Figure 3. To further understand the performance of the proposal, the study compared it to the performance of other feature selection algorithms outside the NSGA family to verify the advantages and novelty of the proposed feature selection algorithm. To be fair in the comparison, we used multi-objective optimization algorithms on the same dataset for the selection of the features influencing vehicle collision in the IoV. We selected the multi-objective algorithms that are well established in the literature for the performance comparison. The algorithms selected and used for the evaluation of the performance are as follows: Pareto envelope-based selection algorithm II [34], multi-objective evolutionary algorithm based on decomposition [35], strength Pareto evolutionary algorithm 2 [36], niched pareto genetic algorithm [37] and multi-objective genetic algorithm [38]. The algorithms were used on the same dataset to select subset features influencing collision in the IoV and detect vehicle collision. The results were recorded and included computational time.