esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design and Method
### System Performance Comparison
Therefore, on a sunny day, we will improve the duty cycles of sensors with more energy and consume the energy as soon as possible. Figure 22 shows that the data generation rate for each sensor converges to maximum value in our algorithm when the number of sensors are \(50,100,200\), respectively. For fewer nodes, the algorithm converges faster to the maximum generation rate. As the number of nodes increase, convergence of iterations needs to increase gradually. This is because, with the expansion of a network scale, the total sample space is growing, and algorithms often need to go through more iterations to achieve convergence. As it can be seen from Figure 22, when the number of nodes is \(50,100,200\) and \(250\), the number of times to reach 95% of maximum generation rate value needs: \(55,120,220,300\) iterations to  
Figure 21: Energy consumption with the number of sensor nodes increasing when the duty cycle is 30%. Figure 22: The iterations for data generation rate converges to maximum value. be performed, repectively. Overall, it will converge to maximum value with iterations no more than double the size scale of the networks in all scenarios, which is affordable in terms of the computing ability of the sensors. The connectivity graph is provided in Matlab for expressing the routing paths for all sensors with connection to any one sink, where 100 sensors and 5 sinks are deployed in a 500 m \(\times\) 500 m square field, which is shown in Figure 23. By considering the limited energy supplement, a source node still chooses a closer base station as its destination. However, the packets from all sources may travel through all intermediate nodes in the network area before they reach a sink, and the communication energy consumption will be balanced to all nodes in order to improve sensor utilization efficiency.
## 3 Materials and Methods
### Methodology Framework
#### 3.2.5 Error Evaluation  
This metric quantifies the proportion of variance in the observed groundwater levels that is explained by the model predictions [2]. It is defined as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{obs,i}-y_{pred,i}\right)^{2}}{\sum_{i=1}^ {n}(y_{obs,i}-\overline{y_{obs}})^{2}} \tag{8}\]  
RMSE provides the magnitude of average prediction errors, emphasizing larger errors due to squaring [19]. It is calculated by:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{pred,i}-y_{obs,i}\right)^{ 2}} \tag{9}\]  
MAE indicates the average absolute prediction error without emphasizing outliers [12]. It is expressed as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|y_{pred,i}-y_{obs,i}\right| \tag{10}\]  
MAPE quantifies prediction accuracy as an average percentage error [19] useful for normalized error comparison:  
\[\text{MAPE}=\frac{100}{n}\sum_{i=1}^{n}\left|\frac{y_{pred,i}-y_{obs,i}}{y_{ obs,i}}\right| \tag{11}\]This metric measures the strength and direction of linear association between observed and predicted groundwater levels:  
\[r=\frac{\sum_{i=1}^{n}\big{(}y_{obs,i}-\overline{y_{obs}}\big{)}\Big{(}y_{pred,i }-\overline{y_{pred}}\Big{)}}{\sqrt{\sum_{i=1}^{n}\big{(}y_{obs,i}-\overline{y_{ obs}}\big{)}^{2}}\sqrt{\sum_{i=1}^{n}\big{(}y_{pred,i}-\overline{y_{pred}}\big{)}^{2}}} \tag{12}\]  
Moreover, an evaluation based on the Taylor diagram is presented to visualize model performance by combining correlation, standard deviation, and RMSE. It enables clear comparison of deep learning and transformer models in capturing groundwater variability.
## 2 Materials and Methods
### Supercontinuum Generation  
A particular challenge was efficiently coupling the high intensities of pump light necessary for SC into the cladding modes of the fiber. However, a tapered-fiber method was developed [16], which aided significantly in the process. Figure 2a shows a tapered section of the ESF which was created with the arc of a fusion splicer, while the fiber was fixed at each end but not under initial tension. The fiber was then cleaved at the center where the air holes had completely collapsed. At the in-coupling side, this taper formed a solid piece of glass which allowed the light to be pulled from a Gaussian focused, solid profile into the more complex mode-intensity profiles of the cladding modes. The light of a diode seeded picosecond fiber laser (FL) (IPG Laser, Burbach, Germany), with a pulse length of 180 ps operating at 1060 nm (up to 330 kW peak power and 22.5 W average power) was launched into to the cladding. A small numerical aperture lens was selected to match the taper angle of 2.99 degrees and the laser, M\({}^{2}\) = 2, was focused with a 1/e\({}^{2}\) spot size of about 45 \(\upmu\)m (w\({}_{0}\) = 22.59 \(\upmu\)m, NA = 0.06 and a Rayleigh length of 187 \(\upmu\)m) onto the collapsed part of the fiber. Figure 2b,c shows the output of the fiber at the pump wavelength after adjusting the system to couple into the core-guided mode and cladding modes.  
Figure 1: The interferometric setup used for measuring the group-velocity dispersion (GVD), similar to that used in Ref. [12]. SC, supercontinuum light source; BS, nonpolarizing beam splitter; DL, delay line; M, mirror; TF, test fiber; AT, attenuator; F, bandpass filter; L, lens; CCD, camera; and Spec, spectrometer.  
When the light was coupled into the cladding of the PCF, some fiber modes showed a large loss after a few centimeters of propagation in the fiber. After a fiber length of 5 meters, 45.6% of the laser power was still measured, which corresponded to a coupling efficiency of 50.4% measured at 0.46 meters. The core-guided mode and fundamental cladding mode (seen in Figure 2c and calculated in Figure 3c) were the primary modes that were coupled into due to their spatially flat phase profiles and the fact that the pump light also had a flat phase profile at its focus. A schematic of the setup used to selectively launch the light into the core or cladding is shown in Figure 2d. The measurement of the spectral power densities was performed with two spectrometers (AvaSpec-ULS3648 and AvaSpec-NIR256-1.7 from Avantes) with a cosine corrector in a calibrated setup. For the investigation of the spatial intensity pattern in the core and cladding of the PCF at 1060 nm, the monochrome CCD camera (acA2040-90 um, Basler, Ahrensburg, Germany) was applied (Figure 2b,c and at 850 nm in Figure 6e). An camera with color filter array (acA2040-90 uc, Basler, Ahrensburg, Germany) was used to examine the SC at defined wavelengths in the visible (Figure 5 inset and Figure 6a-d) and a indium gallium arsenide camera (Bobcat-640-GigE, Xenics, Leuven, Belgium) was used in the infrared (Figure 6f-h).
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
## 2 Scalable Multicore KNN-Based Demodulator
### Custom Processing Units for KNN-Based Demodulator  
The custom processing units (CuPUs) helps to define more complex functions or high-level DSP operations. In hardware design the term Processing Element (PE) is often used as those elements that perform clock-independent operations and simple memoryless mapping in signal processing such as: add/sub, add/sub-and-shift, multiply, multiply-and-accumulate, butterfly, etc. [26]. The CuPUs are not simple arithmetic operations on input data. They are complete routines that would imply in RTL an FSM with datapath. In this sense, the modules are closer to processing units than to processing elements. The word Custom was added to specify that these units already have optimization directives previously defined by the authors. The CuPUs perform basic independent operations, allowing the use of multiple components in parallel on hardware to improve the performance of the algorithm. For the KNN algorithm, a Calculate Distances CuPu calculates \(L\) distances between the symbols and the training data. The \(L\) training labels are sorted in ascending order according to the distance by a Sort Distances CuPU. Finally, the first \(K\) labels are selected from the sorted label array, and the input symbol is labeled with the modal value, also inside a Calculate Mode CuPU.
## 3 Experimental Results and Analysis  
In the experiment, the free stream flow velocity was 20 m/s, and the corresponding Reynolds number based on the airfoil chord length was \(0.8\times 10^{6}\), although wind turbines often operate at wind speed below 20 m/s, but the magnitude of the Reynolds number was up to an order of \(10^{6}\). Therefore, the experimental value of the Reynolds number was chosen to approach an order of magnitude corresponding to those obtained from full-scale wind turbines. Two types of grilles generate different turbulence levels in the wind tunnel, such that the experiments were mainly separated into low and high turbulence cases. To be specific, a grille was placed at the upstream of the airfoil test section which results in different aerodynamic characteristics of flow over the airfoil with and without a Gurney flap.
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 2 Lattice and Code-Based Post Quantum Cryptography
### Learning with Error Cryptography
These algorithms are known as key encapsulation mechanisms (KEM) and a few examples from NIST are SABER [18], Classic McEliece [14; 15], CRYSTALS-Kyber [19], and NTRU [20]. The process of using encapsulation with LWE/LWR is described below: * The public and private keys of both parties are constructed as described in Figure 1. * Person A sends Person B their public key. * Person B randomly generates a symmetric key and encapsulates it in a ciphertext with the public key of person A. * Person B sends the ciphertext to person A. * Person A decapsulates the ciphertext with their private key. * Both parties now have the symmetric key in their possession. In summary, LWE schemes are now relatively mature, and very well documented. The methods selected by the NIST standardization program, presented here, are straightforward to use. The codes are widely available online for download, and we successfully deployed them in our research environment to study the use of PUFs for key generation.
# Virtual Fire Evacuation Drills through a Web-Based Serious Game
## 5 Results and Discussions
### Effect of Evacuation Signs  
To further discuss the effect of the signs on evacuation, evacuation signs were added to the scene after three-time drills. Thus, the trainees participated in the fourth drill with evacuation signs. In detail, the signs were placed along the corridor to indicate the evacuation paths, as shown in Figure 12. The trainees can follow the signs and choose paths to evacuate.  
Figure 11: Safety scores of three drills.  
Figure 12: Evacuation drill process with signs. (**a**) Start evacuation; (**b**) In evacuation; (**c**) Evacuation exit.  
Figure 10: Evacuation time of three drills.  
The average evacuation time and safety scores are shown in Figure 13. The results of the drill with signs were compared with the third drill (without signs). It can be seen that the average evacuation time of trainees reduced from 54.32 to 45.98 s, and the safety scores increased from 81.21 to 83.80. It suggests that the evacuation efficiency was further improved with evacuation signs. Evacuation signs can facilitate the occupants' evacuation and improve evacuation safety.
# Electronic Life Cycle Monitoring System for Various Types of Lead Acid Batteries
## 4 Conclusions  
A prototype system working in automatic mode for the research of battery performance characteristics and degradation process was developed. The system control was developed on the basis of microcontrollers. The prototype system consists of a main control board that ensures the data collection, transmission to the central computer and automatic control process. In automatic the mode, four batteries can be tested simultaneously under the preprogrammed test conditions. For that purpose, four separate measurement modules connected to the central system module were designed and manufactured. PCB were designed and manufactured for each measurement module and main system module. A network of RS485 modules was used for data transmission. For the analysis and evaluation of the results, a computer application was created using the Delphi programming language. The application is programmed in such a way that it can independently perform all monitoring of the results and, in the case of deviations, show for the operator a possible scenario for solving the problem. Different types of lead-based batteries were studied while testing the developed system. As shown by the results of all the tested batteries, the fastest charging occurs up to 80% of the charge level. When studying the battery degradation process, it was observed that deep cycle liquid electrolyte batteries can withstand up to 500, AGM-type batteries up to 600 and GEL-type batteries up to 750 cycles. However, it is worth noting that, after reaching the mentioned number of cycles, the capacity of the batteries decreases to 4-5 Ah. Analyzing the dependence of the predicted service life of batteries on the length of the charge-discharge cycle revealed that, when charging and discharging batteries in the 0.3 C mode, GEL batteries have the longest expected service life (about 11,000 h). According to the research results, while designing and producing the electrical energy storage for, e.g., renewable energy sources, it is recommended to use GEL-type batteries, as they have the highest number of life cycles. This battery testing system is universal and can be applied to any type of battery (lithium-ion, etc.) simply by entering the experiment conditions according to the type of battery being tested. Such a system can be successfully applied to ensure the quality process for battery manufacturers.  
Methodology, G.B.; Software, I.S.; Resources, G.G.; Writing - original draft, Z.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 15: Dependence of the predicted service life of batteries on the length of the charge–discharge cycle.
## 3 Modelling of the High Sensitive Overcurrent Relay  
This protection switchgear block contains a current transformer, a circuit breaker (52) and an IDMT overcurrent relay (51P) that operate when the current exceeds the relay's predetermined value. This relay operates based on the IEC 60255 standard for normal inverse IDMT trip characteristics, as seen in Figure 1. It also contains a phasor measurement unit (PMU) and two supply and switching units (SSU) connected to a separate bus with 10 kA, 6 kA and 4 kA faults. These fault values are obtained from Tables 1 and 2. The input voltage was a 330 kV high voltage transmission line, and an output display of the relay status showed the tripping time and fault current.  
The overcurrent relay activates when the fault current exceeds the relay pickup current. The pickup current is calculated in operating time, and the inverse definite minimum time (IDMT) is defined as the amount of time taken before the circuit breaker trips when an overcurrent is initiated in a circuit or transmission line are also calculated. The operating time is defined as a fixed parameter such that an instantaneous overcurrent relay is produced when the operating time is set to zero [14]. This can be illustrated in Figure 2 below and, to calculate the trip time, the IEEE C37.112-1996 equation for the trip time used is given as:  
\[t(I)=TD\Bigg{(}\frac{A}{(\frac{1}{I_{e}})^{p}-1}+B\Bigg{)}, \tag{5}\]  
where \(A\) is the time factor for the overcurrent trip, \(I\) is the actual current, Is is the relay pickup setting, \(p\) is the exponent for inverse time, and \(B\) is the time coefficient for the overcurrent trip. While the IEC 60255 IDMT trip curve equation is given by  
\[t(I)=TMS\Bigg{(}\frac{k}{(\frac{1}{I_{e}})^{\alpha}-1}\Bigg{)}, \tag{6}\]  
where \(\alpha\) and \(k\) are the curve constants and seen in Table 3 below.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fault Types** & **Threshold of \(I_{a}\) (kA)** & **Threshold of \(I_{b}\) (kA)** & **Threshold of \(I_{c}\) (kA)** & **Threshold of Ground** \\  & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** \\ \hline A-G & & & 307.6007 & 39.5083 & 307.6007 & 20.8305 & 18.3399 \\ \hline B-G & 199.6292 & 32.6032 & & & 199.6292 & 28.3545 & 24.7174 \\ \hline C-G & 364.3873 & 15.1679 & 364.3873 & 28.6647 & & & 15.5710 \\ \hline No-Fault & 23.0105 & 10.6870 & 23.0105 & 15.1958 & 23.0105 & 23.0105 & 13.0455 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 1: Instantaneous overcurrent relay block model.
# Intelligent Fault Diagnosis of Unbalanced Samples Using Optimized Generative Adversarial Network
## 3 Results
### Case I: CWRU Dataset
#### 3.1.3 Fault Classification Experiment  
The learning rate was set to 0.0002 in the Adam optimizer for the classifier. The multi-class confusion matrixes of the output and the three IRs are displayed in Figure 12. The train samples are augmented as a sufficient dataset. For exploring the advantages of the proposed algorithm in fault classification, three compared classifiers were trained separately with the augmented samples for comparison. The data were normalized before training. Table 2 compares the four classifiers with various imbalance ratios in four general metrics: recall, precision, F1, and accuracy. For the classifier, the average diagnostic accuracy reached more than 99.10%. When IR = 8:1, the accuracy of the proposed method matched ResNet18, but the precision of the proposed method was slightly higher. Table 2 demonstrates that the proposed method is superior, as determined by all the metrics, to other methods for 10 fault states. Therefore, the proposed method can effectively improve the accuracy and stability of fault diagnosis under the condition of imbalanced samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**Classifier**} & \multicolumn{3}{c}{**Imbalance Ratio**} \\  & & **4:1** & **8:1** & **16:1** \\ \hline Recall & CNN & 0.9930 & 0.9894 & 0.9884 \\  & ResNet18 & 0.9945 & 0.9920 & 0.9905 \\  & AlexNet & 0.9934 & 0.9905 & 0.9900 \\  & Proposed & 0.9949 & 0.9920 & 0.9910 \\ \hline Precision & CNN & 0.9930 & 0.9896 & 0.9887 \\  & ResNet18 & 0.9945 & 0.9919 & 0.9905 \\  & AlexNet & 0.9935 & 0.9905 & 0.9901 \\  & Proposed & 0.9950 & 0.9920 & 0.9910 \\ \hline F1 & CNN & 0.9929 & 0.9894 & 0.9884 \\  & ResNet18 & 0.9944 & 0.9919 & 0.9904 \\  & AlexNet & 0.9934 & 0.9904 & 0.9899 \\  & Proposed & 0.9949 & 0.9919 & 0.9909 \\ \hline Accuracy & CNN & 0.9930 & 0.9895 & 0.9885 \\  & ResNet18 & 0.9945 & 0.9920 & 0.9905 \\  & AlexNet & 0.9935 & 0.9905 & 0.9900 \\  & Proposed & 0.9950 & 0.9920 & 0.9910 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of classifier performance for Case I.  
Figure 11: Comparison of generated samples with histogram distance in Case I.
# Recommendation Algorithm Based on Survival Action Rules
## 3 Methods  
In survival models, when the data lacks complete information on the timing of an event, we are dealing with so-called censored data [57]. There are three main types of censoring [58]: right censoring occurs when the true event time exceeds the observation period; left censoring happens when the event takes place before the observation begins without a precise time; and interval censoring arises when the event is known to have occurred within a specific period, but the exact moment remains unknown.  
Right-censored data are most commonly used in survival analysis. There are several reasons for right censoring: the observation period of the phenomenon only covers a certain period of time, the object of observation is removed from the study, or the observation of the object is interrupted for external reasons.  
The main applications of survival analysis include estimating and interpreting the survival function, comparing survival curves across different groups of observations, and evaluating how conditional attributes affect survival time.  
The survival function, together with the hazard function, is of particular interest in the analysis of survival data. To estimate a survival function from observed survival times without assuming an underlying probability distribution, the Kaplan-Meier estimator is recommended [6]. For each time interval, the probability of survival is calculated as the number of surviving subjects divided by the number of subjects at risk.
## 4 Validation of the Numerical Model  
In this section, the validation methodology used in this work is presented, in which the experimental campaign was numerically simulated and was then compared with the thermocouple values measured in the surfaces. The finite element analyses were realized using ABAQUS [51], and all the parameters adopted are addressed below. For all examples, surface convection and radiation were admitted, with the upper bond parameters provided by the standard [52], resulting in coefficients of convection of \(h_{f}=35\) W\(/(\mathrm{m}^{2}\,\mathrm{K})\) and \(h_{f}=4\) W\(/(\mathrm{m}^{2}\,\mathrm{K})\) for the firebrand exposed surface and unexposed side of separating members, respectively. For the emissivity coefficient, due to firebrands being black at the flashover, it was admitted \(\varepsilon=1.0\). For the Stefan-Boltzmann constant, \(\sigma=5.67\times 10^{-8}\) W\(/(\mathrm{m}^{2}\mathrm{K}^{4})\) was adopted, and for all examples, the numerical thermal analysis was performed considering Kelvin as the unit for temperature; therefore, the admitted absolute temperature was zero. Since all thermal properties vary with temperature, a nonlinear transient thermal analysis was performed using the incremental Newton-Raphson iterative method. In all analyses, in order to regulate the flux transfer between nodes, the maximum allowed temperature change per increment was 10 K. This precaution indicates that reducing the time step increment because of the maximum allowed temperature can result in a more reliable numerical solution for nonlinear thermal analyses [53].