esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Metrics and Model Evaluation  
Various performance metrics were used for model comparison, including the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), model accuracy, precision, recall, and F1-score. The AUC-ROC is a metric that separates true positives from true negatives. An AUC-ROC of 1 indicates that a model can perfectly separate positive classes from negative classes, whereas 0 indicates the model will predict all positives as negatives and vice versa. This metric is constructed by plotting the true positive rate against the false positive rate at various thresholds. Accuracy (Equation (1)) is a metric that shows the proportion of observations that are identified correctly. An accuracy of 1 indicates perfect prediction, where all positive and negative classes are predicted 100% accurately. Precision (Equation (2)) refers to the proportion of positives which are predicted correctly, whereas recall (Equation (3)) estimates the portion of actual positives that were identified correctly. The F1-score (Equation (4)) is estimated by combining precision and recall in order to quantify the tradeoff between precision and recall. After obtaining the mean values of these performance metrics, a 95% confidence interval (CI) (Equation (5)) at \(\alpha\) = 0.05 was calculated to provide a measure of the reliability and precision of the estimated parameters. Next, we ranked each model from 1 to 5, with 1 being the best-performing model and 5 being the least-performing model, based on the reported performance metric values and the calculated confidence intervals. Additionally, the time needed to finish the hyperparameter optimization (HO) and performance metrics evaluation (ME) was used as the model efficiency metric. All the HO and ME analyses were performed in the high-performance computing (HPC) environment at the University of Connecticut (AMD EPYC 7713, cores = 126, nodes = 1).  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{F1}=2\times\frac{\text{precision}\times\text{recall}}{\text{precision}+ \text{recall}} \tag{4}\]  
95% Confidence Interval(CI) = sample mean \(\pm\) 1.96 \(\times\) Standard Error  
\begin{table}
\begin{tabular}{c c c} \hline
**ML Algorithm (Classifier)** & **Main Hyperparameters** & **Search Space** \\ \hline \multirow{6}{*}{XGBoost} & n\_estimator & [100:2000] \\ \cline{2-3}  & max\_depth & [5,46] \\ \cline{2-3}  & learning\_rate & [0.01:1] \\ \cline{2-3}  & subsample & [0.5:1] \\ \cline{2-3}  & colsample\_bytes & [0.5:1] \\ \hline \multirow{6}{*}{RF} & n\_estimator & [100:2000] \\ \cline{2-3}  & max\_depth & [5,46] \\ \cline{2-3}  & min\_sample\_split & [2,11] \\ \cline{2-3}  & min\_samples\_leaf & [1,11] \\ \cline{2-3}  & max\_features & [’auto’, ‘sqrt’, ‘log2’, None] \\ \cline{2-3}  & criterion & [“gini”, “entropy”] \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 1 Introduction
In order to close this gap, this paper empirically analyzes and summarizes the literature on inverter sizing ratios based on the various types of solar PV panel technologies in use worldwide. Moreover, this study focuses on the issues of different PV component sizing methodologies, including the PV/inverter power sizing ratio, and recommendations for PV-inverter systems by summarizing the power sizing ratio, related derating factor, and sizing formulae approaches. In addition, the presented study recommends a Deep  
Figure 1: Explanation of the oversizing ratio of the DC solar PV-to-inverter AC power output over a whole day. Learning-based technique that might provide fully automatic computation for the DC/AC sizing ratio and effectively lower the whole return on investment (ROI) over a variety of circumstances and climatic changes. This study also introduces a novel inverter sizing strategy using the Deep Learning network technique that can provide the best value for the sizing ratio. In order to formulate the existing problem, including the sizing ratio frameworks, based on numerous studies, the articles were analytically reviewed by compiling and dividing them from outside sources into several of the most important and diverse topics. This also included analyzing the sizing ratio, which was compiled and divided into several main climatic types according to the Koppen-Geiger climate classification [31], which is freely accessed. A comparison with similar review work is listed in Table 1.
# BDS Satellite Clock Prediction Considering Periodic Variations
###### Abstract  
The periodic noise exists in BeiDou navigation satellite system (BDS) clock offsets. As a commonly used satellite clock prediction model, the spectral analysis model (SAM) typically detects and identifies the periodic terms by the Fast Fourier transform (FFT) according to long-term clock offset series. The FFT makes an aggregate assessment in frequency domain but cannot characterize the periodic noise in a time domain. Due to space environment changes, temperature variations, and various disturbances, the periodic noise is time-varying, and the spectral peaks vary over time, which will affect the prediction accuracy of the SAM. In this paper, we investigate the periodic noise and its variations present in BDS clock offsets, and improve the clock prediction model by considering the periodic variations. The periodic noise and its variations over time are analyzed and quantified by short time Fourier transform (STFT). The results show that both the amplitude and frequency of the main periodic term in BDS clock offsets vary with time. To minimize the impact of periodic variations on clock prediction, a time frequency analysis model (TFAM) based on STFT is constructed, in which the periodic term can be quantified and compensated accurately. The experiment results show that both the fitting and prediction accuracy of TFAM are better than SAM. Compared with SAM, the average improvement of the prediction accuracy using TFAM of the 6 h, 12 h, 18 h and 24 h is in the range of 6.4% to 10% for the GNSS Research Center of Wuhan University (WHU) clock offsets, and 11.1% to 14.4% for the Geo Forschungs Zentrum (GFZ) clock offsets. For the satellites C06, C14, and C32 with marked periodic variations, the prediction accuracy is improved by 26.7%, 16.2%, and 16.3% for WHU clock offsets, and 29.8%, 16.0%, 21.0%, and 9.0% of C06, C14, C28, and C32 for GFZ clock offsets.  
BDS satellite clock offsets; clock prediction; frequency domain; periodic variations; STFT
## 4 Simulation and Experimental Results
### Residual Energy  
The total residual energy of the network considers the remaining energy of all nodes in each round. This metric is reported in Figure 5, where it is evident that the proposed method manages to save more energy than the other protocols. The total residual energy is reported considering 100% of the residual energy in round 0, and 0% of the residual energy in the last round. As it was expected, our approach finds the network configuration that spends the lowest energy in every round. Therefore, the residual energy of the network is higher than the obtained with the other methods. From the figure, we can conclude that the proposed protocol achieves the lowest energy expenditure and lengthens the life of the nodes.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 3 Methodology
### Covariation Matrix and Eigenvalue Calculation  
Since the signal after NLOS cancel still has direct path and multi-path residual components, it can be expressed as  
\[s_{\text{NC}}(n)=\sum\limits_{i=0}^{N_{\text{NC}}-1}\beta_{i}s_{\text{T}}(n- \tau_{i})+\omega(n) \tag{25}\]  
where \(N  
where, **U** is the matrix composed of the eigenvectors of **R**, and \(\mathbf{\Lambda}\) is the diagonal matrix composed of the eigenvalues of **R**.  
\[\mathbf{\Lambda}=\left[\begin{array}{cccc}\lambda_{1}&0&\cdots&0\\ 0&\lambda_{2}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\lambda_{N}\\ \end{array}\right] \tag{34}\]  
Let \(\mathbf{\lambda}=[\lambda_{1}\,\lambda_{2}\ldots\,\lambda_{N}]\) be the set of eigenvalues of **R**. The next subsection describes the method of unsupervised multipath number estimation of received signal.
# Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
## 6 Models for the Latency
### Bluetooth Park Low Power Mode  
Taking Table 1 as reference, the latency from end-to-end in Bluetooth multi-hop networks when links are always in park low power mode can be modeled as in Equation (1), where some of the time contributions described in [40] are included, namely the amount of hops (N), the inactivity period of the mode (T\({}_{PARK}\)), the ratio of inactivity period affecting the latency (\(\alpha\)), and the minimum hop latency (\(\delta_{\min}\)), which summarizes not only the time for processing the data-packets but also other minor contributions. Furthermore, the probability of a retransmission in the multi-hop communication happening is also included in Equation (1): when one retransmission happens (\(p_{1}\)(_tetzx_)), two (\(p_{2}\)(_tetzx_)), and more. It happens not only with ping data-packets but also with pingback data-packets.  
\[\delta_{PARK}=N\cdot(\alpha\cdot T_{PARK}+\delta_{min})+p_{1}(retx)\cdot\alpha \cdot T_{PARK}+p_{2}(2 retx)\cdot 2\cdot\alpha\cdot T_{PARK}+... \tag{1}\]  
After that, the values for the coefficients in Equation (1) were calculated through linear multiple regression. Since the probability values (\(p_{1}\)(_tetzx_), \(p_{2}\)(_tetzx_), etc.) depend on the actual deployment of the home automation application, they have not been included in the regression. Then, the curves that best fit to the results of the tests summarized in Table 1 for the park mode were defined.  
The performance in terms of latency of the multi-hop communication network for the ping data-packets when links are in the park mode can be modeled with Equation (2). When the communication is from slave to master, i.e., with pingback data-packets, the performance is similarly modeled with Equation (3).  
\[\delta_{PARK}(ping)=N\cdot(0.34\cdot T_{PARK}+0.249)+0.191 \tag{2}\]  
\[\delta_{PARK}(pingback)=N\cdot(0.33\cdot T_{PARK}+0.265)+0.283 \tag{3}\]  
After obtaining the values for the coefficients in Equation (1), the models for the multi-hop communication when working with links in the park mode were validated with R\({}^{2}\) statistical datum. The values of R\({}^{2}\) factor for the results summarized in Table 1 and the models defined by Equations (2)and (3) are 0.9527 when considering ping data-packets, and 0.9396 when considering pingback data-packets. Moreover, the MSE for Equation (2) is 0.091 and for Equation (3) is 0.089.  
As a summary, the latency in asynchronous links of Bluetooth multi-hop networks can be defined with just one equation for each one of the directions, i.e., for ping and pingback data-packets, in park low power mode. Furthermore, the two models were validated with the R\({}^{2}\) statistical parameter and the MSE.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design
### Improved Dual Population Genetic Algorithm
#### 4.1.4 Parallel Evolution of Two Populations  
Parallel evolution of multiple populations entails evolving distinct populations concurrently, each governed by different parameters. These populations evolve independently and periodically exchange their best individuals. This process helps preserve population diversity, improves solution quality, accelerates convergence, and reduces the risk of the algorithm becoming trapped in local optima.  
* Selection operation We use roulette and modified elite strategy for operator selection. First, the top 10% of the best individuals in the population are retained according to their fitness values and  
Figure 4: Chromosome structural coding.  
passed directly to the next generation as elite individuals. The roulette method is used to select the remaining 90% of the population. This can prevent the optimal individual from being eliminated, ensure the quality of the offspring population, and improve the global convergence ability of the algorithm.
* Crossover operation The population was divided into two sub-populations, one with a fixed crossover probability \(P_{cro}\) and the other with an adaptive crossover probability. The adaptive crossover probability is calculated as follows: \[P=\vartheta P_{1}+(1-\vartheta)P_{2}\] (18) \[\vartheta=1-K/K_{max}\] (19) where \(P_{1}\) and \(P_{2}\) denote the larger and smaller crossover probabilities, \(K\) represents the current iteration number, and \(K_{max}\) represents the maximum number of iterations. The crossover mechanism employed here is a two-point crossover method, which randomly sets the starting point and end point of the crossover in the chromosome string, exchanges the structure between the parents' exchange points, and generates two new individuals, as illustrated in Figure 5.
* Mutation operation Mutation in genetic algorithms alters specific gene values on the chromosome string through different techniques to generate new individuals. These mutation operators are essential for maintaining population diversity and avoiding premature convergence. However, traditional mutation operators are prone to local optima issues. To overcome this, enhancements to the standard random mutation method have been introduced, incorporating Gaussian mutation for chromosome alterations. The mutation probability \(P_{mut}\) determines whether a mutation occurs. If a mutation takes place, the original gene value in the chromosome is replaced by a random number generated from a normal distribution with a mean \(\mu\) and variance \(\sigma^{2}\). Through multiple iterations, the algorithm identifies the optimal edge node placement that satisfies the criteria of minimum delay and cost.
## 3 Results
### Discussion of the Results  
Difficulties arise when correlating FEA models with physical test results, highlighting the challenges that occur when simulating additively manufactured lattice structures. Parts exhibit material and geometry imperfections that are challenging to account for in advance, and their influence can result in vastly different model behaviour. The tensile properties of the tested material samples varied significantly, with ultimate tensile strengths ranging from 32 MPa to 51 MPa and maximum strains ranging from 3.5% to 11%. Using their average properties might not necessarily be transferrable to a different batch of samples from a later date or be transferrable to singular samples. Printing orientation also influences the final part properties, as the undersides of surfaces exhibit higher porosity than those of the top surfaces, and tolerances vary depending on features being oriented horizontally, vertically, or at any angle in between. While samples were all manufactured with identical orientations, angled structures can differ from their horizontally or vertically oriented counterparts in the same sample, influencing its global response in FEA models. Moreover, possible geometrical deviations such as warping, material accumulation at lattice nodes due to discretization from meshing the geometry, and feature size tolerances from sandblasting during postprocessing and when nearing the production method's limits exacerbate accurate modelling even further. The buckling response of the investigated unit cell types reacts very sensitively to any imperfections occurring in test samples, as evidenced by these findings.  
Beyond these issues, the assumptions regarding the general setup of the FEA models were confirmed to be adequate and accurate for the models, as evidenced by our results. Models with the necessary changes applied to them closely matched both the reaction force curves of the test samples and the accumulated average energy absorption values. The specific energy absorption reported from the FEA models was slightly underestimated since the reported sample weights were overestimated because of overlapping beam elements at unit cell corner nodes. This can be corrected by using the non-normalized absorbed energy of the lattice instead of SEA while still limiting the structure's maximum weight and calculating the SEA value based on the physical sample weights. The choice of using  
Figure 18: Comparison of optimised Simple Cubic FEA model with quadratic thickness distribution and 0.1 mm constant diameter reduction vs. the test samples.  
SEA as the optimization metric was made to allow the study to also explore lower lattice weights, but the optimizer mostly operated above or very close to the upper limit and did not significantly venture in the opposite direction.
## 4 Results  
For the experiments, the tests were performed in the IM through direct start-up activation; a torque scale was used with values ranging from 0 to 24 Nm. For each performed test, a constant load was applied to the system. The load is applied to the shaft through the variation of the field circuit voltage of the DC generator. For each load value, the shaft rotational speed of the motor was measured, and the line current values (\(I_{a}\),\(I_{b}\) ), the voltages between phases (\(V_{ab}\) and \(V_{ca}\)), the transducer torque signal and the sound signal emitted by the workbench set were acquired.  
Figure 7 displays a graph of the estimated speed from the sound, the measured speed, and the relative error between the two. In this case, the mean relative error between the two curves was 0.02%.  
The graph in Figure 8 presents the measured torques, the torques estimated from the sound, and the torques estimated by the AGT for different loads applied to the shaft. A deviation of the reference  
Figure 6: Assembly diagram of the workbench with controllable torque.  
Figure 7: Measured value vs. estimated value of speed and relative error between the two measurements.  
torque is observed in the low-slip region. For a load approaching zero, the absolute error for the estimation through the sound (proposed method) was 1.88 Nm, which is 9% of the nominal torque of the motor. The load approaching the nominal torque presented an absolute error of 0.30 Nm, which corresponds to 1.5% of the nominal torque. The air gap torque method presented an approximately uniform error in relation to the applied loads, with a mean absolute value of 0.26 Nm (1.25% of the nominal torque).  
Figure 9 displays the curves obtained for the measured and estimated (sound and AGT) efficiencies. As observed, the sound method (proposed method) fits the measured efficiency curve better than the air gap torque method for loads close to nominal, with a 0.1% error for the nominal load, while the air gap torque method exhibited a deviation of approximately 26% for the load approaching zero and 1% for the nominal load.  
It is noticed in Figure 9 the low accuracy for loads close to 0%, due to the fact that estimating efficiency for very low loads (up to 10% of the nominal power) is a reocurring problem for non-invasive  
Figure 8: Comparison between the measured values and the two estimated values.  
Figure 9: Curves of the measured, estimated (AGT), and estimated (sound) efficiencies.  
techniques. From the references utilized in the text, it is noted that [8; 9; 10; 15; 16] do not present results in this power range, from the references in the literature that surveyed curves for this power range [11; 13], they show an accuracy similar to the one proposed in this paper.  
The proposed method is based on the detection of peaks in the frequency domain of rotational speed harmonics of the motor that differ from the failure peaks in rolling bearings, broken bars, and eccentricity, for example. Then, it is expected that the system works even under the aging process.
## 2 Non-Autonomous Dynamic Systems
### Equation of Motion for Longitudinal Vibrations  
The equation of motion and boundary conditions ([PERSON], 1954) for a cantilevered pylon of length \(L\) with a time-varying, lumped mass attachment \(m_{L}(t)=m_{0}\pm\mu\cdot t\) at the top \(x=L\) and undergoing longitudinal vibrations, see Figure 1, are given below as follows:  
\[\begin{array}{c}\rho A\big{(}\ddot{u}+\ddot{x}_{g}\big{)}+\big{(}m_{L}\big{(} \ddot{u}+\ddot{x}_{g}\big{)}+\dot{m}_{L}\big{(}\dot{u}+\dot{x}_{g}\big{)} \big{)}\delta(x-L)=EAu^{\prime\prime}\\ u(0,t)=0,EAu^{\prime}(L,t)=0\end{array} \tag{1}\]  
In the above, \(E\) is the modulus of elasticity, \(A\) is the cross-section area, and \(\rho\) is the material density of the pylon for a continuous mass distribution. Next, \(u(x,t)\) is the displacement along the x-axis and \(x_{g}(t)\) is the time-dependent ground motion imparted at the base, with primes (\({}^{\prime}\)) and dots (\(\cdot\)) respectively denoting spatial and temporal derivatives. Moreover, initial conditions are assumed to be zero. It should be noted that the boundary conditions in Equation (1) have been rendered homogeneous by absorbing the lumped mass inertia effects \(m_{L}\big{(}\ddot{u}+\ddot{x}_{g}\big{)}+\dot{m}_{L}\big{(}\dot{u}+\dot{x}_{g }\big{)}\) in the equation of motion using the Dirac delta function \(\delta(x)\). This however changes the eigenvalue problem of the original stand-alone pylon to a more complicated one and requires an additional investigation as to the number of terms required for convergence for a modal analysis ([PERSON], 1954). Note that the presence of a point discontinuity at boundary \(x=L\) poses a more stringent convergence requirement as compared to the case of the discontinuity appearing in the interior \(0<x<L\) of the waveguide.