esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments, Results, and Analysis
### Implementation Details, Computational Resources, and Model Complexity  
We used MATLAB 2022b to synthesize the raw composite signals and Python 3.9.18 with the SciPy 1.11.3 library [27] to fit distributions to the generated composite signals. The deep learning models were built and trained using TensorFlow 2.10.1 and Keras 2.10.0 libraries [32; 33] in Python 3.9.18. All experiments were performed on a HP ENVY Desktop PC TE02-000 oi (Hewlett Packard, Palo Alto, CA, USA) equipped with an Intel Core i7-12700 CPU (Intel, Santa Clara, CA, USA) at 2.10 GHz, 32 GB of RAM, and an NVIDIA GeForce RTX 3060 TI GPU (NVIDIA, Santa Clara, CA, USA). Table 5 lists the training time for a training set of 90,000 samples and the total number of floating point operations (FLOPs) required per inference for the two models.  
The modest number of FLOPs, especially for the S-DNN, combined with manageable training times, indicates that the proposed models are practical for implementation in real-world SDR systems. The reduced computational complexity of the S-DNN makes it particularly suitable for systems with limited computational resources or when rapid adaptation is required.
# Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children
## 6 Conclusions  
There is currently an underestimation of people with FASD. This syndrome affects a significant percentage of the world population. However, its diagnosis requires a certain consumption during embryonic development. It could be undiagnosed in the control population due to the clinical similarities with other neuro-development diseases. This issue is a critical factor for developing a new technique for FASD diagnosis since image-based Artificial Neural Networks (ANNs) and Computer-Aided Detection tools (CADs) studies are invasive and cannot be repeated. For this reason, the use of numerical data from non-invasive tests can be an excellent alternative to diagnose FASD. This paper developed ANN algorithms to determine FASD likelihood in children using numerical psychometric, DTI, and Saccadic eye movement data on children/young people diagnosed with FASD. It is essential to mention that the best results were obtained using psychometric data. It is crucial to carry out a more in-depth study of this test and even incorporate data from children with a neuro-behavioral disorder that helps to classify better and identify some syndromes.  
In conclusion, we suggest that ANN algorithms improve their performance with a suitable network configuration by increasing the number of neurons, hidden layers, changing optimization functions, adding a feature layer, and the activation algorithm used to incorporate new data into training. The combination with other data groups even allows us to increase its performance, indicating that ANNs can be a good alternative to detect FASD. This paper evaluated the usage of computational algorithms based on ANN to classify children with FASD for an improved medical diagnosis. As a result, this paper estimated the prediction model using numerical psychometric data, DTI, and saccadic eye movement. Although other studies, like [PERSON] et al. (2019) [15], collect and use the same data, our results show a better performance using ANN instead of SVMR or other methods. However, a deeper study of the different network configurations must be undertaken to improve performance.  
**Author Contributions:** All authors contributed to this study. Conceptualization, V.D., P.L., S.C.; Algorithm, V.D., P.L., S.C. and H.F.; validation, V.D., P.L., and S.C.; writing--original draft preparation, V.D., P.L., S.C.; writing--V.D., S.C., and P.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to express our great appreciation to Dr. [PERSON] for his valuable and constructive suggestions to this research project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### PLA-GO Dataset  
A second experiment has been carried out on a 3D structure of PLA including GO. The obtained map is composed of \(111\times 17\) pixels with 1015 spectral bands between 996 cm\({}^{-1}\) and 2111 cm\({}^{-1}\). In this case an AANN having a 1015-500-25-500-1015 topology has also been used to project the original data into a reduced dimensionality linear feature space. Differently from the previous experiment, we bypassed the N-FINDR algorithm and instead used reference spectra as endmembers to estimate the fractional abundances in each pixel of the map. The RMSE scores of the obtained results have been compared with those obtained through a linear unmixing approach and reported in Table 2. As can be noted, the map reconstructed using the nonlinear approach presents a lower RMSE if compared with the map reconstructed with the linear approach. This can be also appreciated by analyzing the error maps in Figure 8, where the difference values between the original Raman spectral map and the reconstructed map obtained using the proposed nonlinear approach are lower than the difference values between the original Raman map and the reconstructed map obtained using the linear approach.  
Similarly, to the previous experiment, we applied a SAD-based technique to detect and identify all the different materials present in the sample. The identification is carried out by deriving a similarity index obtained by comparing each pixel of the Raman map with a set of reference spectra based on the SAD value. Figure 9 reports the comparison between the most similar spectra and the reference spectra for PLA and GO, respectively. In this case, while it is possible to correctly identify the PLA, which is the dominant material, this approach does not allow the detection of GO, which is only present in a mixed solution.  
Figure 8: RMSE reconstruction errors for the linear (**left**) and nonlinear (**right**) unmixing algorithms applied to the PCL-GO structure.  
Figure 9: Comparisons between reference spectra (GO on the **left**, PLA on the **right**), and the spectra most similar in terms of SAD.
## 5 Results
### Bird Results  
According to Figure 3, the GHI was overestimated at Agadir (\(-\)3.5%), Ouarzazate (\(-\)10.5%), Tangier (\(-\)3.7%), and Marrakech (\(-\)3.2%), but it was underestimated at Tantan (19.2%). According to Figure 4, rRMSE was less than 10% at four stations, namely Agadir (5.9%), Fes (3.0%), Tangier (4.2%), and Marrakech (4.2%), indicating low bias between McClear and Bird. The rRMSE at the Tantan station was greater than 25%, indicating poor agreement between McClear and Bird at this station. Figure 5 shows that four stations, namely Agadir (4%), Fes (2.6%), Tangier (3.8%), and Marrakech (3.4%), had rMAEs less than 5%, indicating low bias between the datasets and very good agreement between the [PERSON] and Bird models at these four locations. While rMAE was less than 15% in Ouarzazate (11.0%), indicating average performance, rRMSE was (21.8%), indicating poor agreement as indicated by high bias. Table 5 shows the coefficient of determination (R\({}^{2}\)) at each of the six stations, with R\({}^{2}\) > 0.99 in all cases, indicating excellent performance based on Table 4, with the exception of Tantan whose coefficient was (0.96), indicating a poor relationship between the Bird and McClear models. The performance of Bird clear sky model with stations is also summarised in the supplementary material Tables S1 to S6 and Figure S1.  
Figure 5: Hourly relative mean absolute error (rMAE) of modelled clear sky GHI against McClear clear sky GHI.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{\
\begin{tabular}{c} **Mean GHI** \\ **(W/m\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**SOLCAST**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{
\begin{tabular}{c} **[PERSON]** \\ **and [PERSON]** \\ \end{tabular} } & \multirow{2}{*}{**ABCG**} & \multirow{2}{*}{**[PERSON]**} \\ \hline Marrakech & 518.1 & 0.997 & 0.999 & 0.998 & 0.99 & 0.998 & 0.99 & 0.99 \\ \hline Fes & 494.7 & 0.999 & 0.985 & 0.999 & 0.997 & 0.998 & 0.996 & 0.996 \\ \hline Agadir & 485.9 & 0.988 & 0.995 & 0.997 & 0.99 & 0.997 & 0.99 & 0.99 \\ \hline Tangier & 545.2 & 0.998 & 0.999 & 0.999 & 0.996 & 0.998 & 0.996 & 0.998 \\ \hline Ouarzazate & 518.3 & 0.99 & 0.96 & 0.99 & 0.99 & 0.99 & 0.98 & 0.99 \\ \hline Tantan & 379.5 & 0.95 & 0.97 & 0.97 & 0.98 & 0.97 & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hourly mean observed GHI (W/m\({}^{2}\)) and coefficient of determination (R\({}^{2}\)) of clear sky models against GHI measured in situ.
# Safety Risk Assessment of Highway Bridge Construction Based on Cloud Entropy Power Method
## 3 Evaluation Method
### Cloud Entropy Weight Calculation Model  
The cloud model uses three parameters, \(E_{x}\), \(E_{n}\), and \(He\), to describe the magnitude, oscillation degree and dispersion degree of the values, with essentially the same idea as the entropy concept, but the data connotation is richer and more consistent with the actual distribution [28]. In this paper, we adopt the cloud model and improve it with reference to the entropy method for the variability of indicators, and obtain the improved model for calculating the indicator weights. The subjective judgments of experts are processed into key representative parameters representing uncertainty by the cloud model, and then the improved calculation model obtains a weight distribution that reflects the importance of risk factors as scientifically as possible, through the organic combination of subjective and objective measures [29].  
With \(m\) experts and \(n\) evaluation metrics, the formula for calculating the numerical eigenvalue of the cloud model for the \(j\)th evaluation metric is obtained according to the cloud model inverse generator formula, as follows [30]:  
\[Ex_{j}=\overline{x}_{j}=\frac{1}{m}\sum\limits_{i=1}^{m}x_{ij} \tag{9}\]  
\[En_{j}=\sqrt{\frac{\pi}{2}}\times\frac{1}{m}\sum\limits_{i=1}^{m}\left|x_{ij}- Ex_{j}\right| \tag{10}\]  
\[He_{j}=\sqrt{\frac{1}{m-1}\sum\limits_{i=1}^{m}\left(x_{ij}-Ex_{j}\right)^{2}- En_{j}^{2}} \tag{11}\]  
where \(i=1,2,\cdots,m;j=1,2,\cdots,n\), \(Ex_{j}\), \(En_{j}\), \(He_{j}\) are the expected value, entropy value, and super-entropy value of the \(j\)th indicator, respectively.  
The traditional weight calculation method is used to solve the \(j\)th evaluation index weight \(\beta_{j}\) as shown in Equation (12).  
\[\beta_{j}=\frac{Ex_{j}}{\sum\limits_{j=1}^{n}Ex_{j}}\ (j=1,2,\cdots,n) \tag{12}\]  
As can be seen from Equation (12), although the traditional weight calculation method is easy to use, it does not make full use of the entropy change in the cloud model and cannot guarantee the comprehensiveness and objectivity of the results. Based on this finding, we propose an improved entropy weighting method to replace this traditional weighting calculation method.  
The cloud entropy weighting method is used to solve the \(j\)th evaluation index weight \(\omega_{j}\), with the following formula [31]:  
\[\omega_{j}=\left\{\begin{array}{ll}\frac{Ex_{j}}{\ln(1+En_{j})+1}\bullet \frac{1}{\sum\limits_{j=1}^{n}\frac{Ex_{j}}{\ln(1+En_{j})+1}}&(\text{En}_{j} \
eq\,0)\\ \frac{Ex_{j}}{\sum\limits_{j=1}^{n}Ex_{j}}&(\text{En}_{j}=\,0)\end{array}\right. \tag{13}\]
## 3 Results
### Feature and Predictor Importance  
Having the model trained and all features ranked with Reciprocal Ranking, valuable information about which features are meaningful for such problems can be extracted. Moreover, the variable that refers to a predictor may be derived using a mathematical formula. For example, 'temperature Surface April mean' is a feature that refers to the predictor 'temperature'. Our feature engineering procedure constructed various features for each predictor. The herein produced features belong to several categories. As an example, the aforementioned feature belongs to the categories'mean','month', and'surface.'  
It is difficult to know _a priori_ which categories perform better, so through feature engineering all possible features were created, knowing that feature selection will be able to select the top ones. However, after the experiment an importance analysis for each feature category may be performed, based on its Reciprocal Ranking score. In order to do this, all features that fall within each category were collected, and a descriptive statistic was calculated. One option for this statistic would be their mean score. However, the mean is prone to outliers and is not representative. Another option would be to pick the single best (minimum score) feature for each category. This is also not a good choice, because some feature categories might have more features than others. So, the 2% quantile was selected as the most appropriate descriptive statistic to extract a metric for the overall performance of each category. The 2% quantile is compatible with the best model which uses the top 2% of features. The feature relative strength analysis is depicted in Figure 4. Lower numbers have greener colors and represent the feature categories with the lower 2% quantile of Reciprocal Ranking. In addition, Figure 5 depicts the strengths of the top-10 predictors for each species.  
Figure 4: Importance (as indicated by Reciprocal Ranking) of feature categories generated by the feature engineering procedure. The lower the value, the better. It is evident that some categories are superior than others, and some are to be avoided.
## 2 Materials and Methods  
### Study Site  
We observed the CO\({}_{2}\) flux and canopy spectral reflectance of a planted evergreen coniferous forest at Qianyanzhou Experimental Station (QYZ) in southeastern China. As a part of the Chinese Ecosystem Research Network (CERN) and the China FLUX network, QYZ station is located in the subtropical continental monsoon region (26\({}^{\circ}\)44\({}^{\prime}\)52\({}^{\prime\prime}\)N, 115\({}^{\circ}\)03\({}^{\prime}\)47\({}^{\prime\prime}\)E, elevation 102 m). According to long-term records of an adjacent weather station (1985-2004), the mean annual air temperature and precipitation were 17.9 \({}^{\circ}\)C and 1485.1 mm, respectively. The flux tower is built, in late August 2002, on a gentle undulating terrain with slopes ranging from 2.8\({}^{\circ}\) to 13.5\({}^{\circ}\). The plantation, which was planted in 1985, is dominated by _Pinus elliotti, Pinus massoniana_ and _Cunningham lancoelata_[49, 50]. The understory shrub includes mainly _Loropetalum Chienense_ and _Lyonia compta_. The soil is red soil, which is weathered from red sand rocks [51]. A field survey made in early August 2013 around the flux tower indicates that the mean canopy height, mean diameter at breast height, and effective green leaf area index (LAIe) of the top canopy were about 15.5 m, 18.8 cm and 3.3, respectively. LAIe was the mean value of about 60 samples measured with Li-cor LAI2200 (LI-COR, Inc., Lincoln, NE, USA) covering the spectral observation area, and showed little temporal variation during the study period. Thus, LAIe was defined here as a constant for the mature evergreen forest [3], and the effect of clumping index (\(\Omega\) = 0.5) was considered on true LAI (True LAI = LAIe/\(\Omega\) = 6.6).
## 1 Introduction
Then at the MAC layer, a variation of the RTS/CTS handshake mechanism is used to forward data continuously from the higher grade to lower grade nodes and finally to the sink. P-MAC does not need an extra independent routing mechanism to support it, thus the communication overhead in the network can be reduced considerably without increasing delivery latency and sacrificing energy efficiency. In addition, each node only maintains a grade and a schedule, which improves the scalability with respect to network topology changes such as nodes dying over time, the later addition of new nodes or nodes moving to different locations. And multiple sinks can be used to partition a large-scale WSN into several independent sub-networks to increase the network manageability and balance energy dissipation. The remainder of the paper is organized as follows. In Section 2, we discuss the related work in the area of duty cycle MAC designs for WSNs. In Section 3, the details of P-MAC are presented, including the network scalability discussion. Section 4 gives the performance evaluation and analysis based on simulation using the OPNET modeler. Finally, Section 5 concludes the paper.
## 4 Experiments
### Evaluation Metrics  
In this paper, six evaluation metrics are used to judge the algorithm's effectiveness: precision, recall, mAP_0.5, mAP_0.5:0.95, number of parameters (_Params_), and model volume (_Flops_). The first four types of indexes are used to judge the detection effect of the model, and the number of parameters and model volume are used to judge the size of the model and the operational efficiency, and the formulas for the various types of indexes are shown in Equations (11)-(14).  
\[P=\frac{TP}{TP+FP} \tag{11}\]  
\[R=\frac{TP}{TP+FN} \tag{12}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{13}\]  
\[mAP=\sum_{i=1}^{M}\frac{AP_{i}}{M} \tag{14}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Name** & **Configuration Parameters** \\ \hline Operating System & Ubuntu 18.04.6 TTS \\ CPU & Intel Xeon(R) Silver 4210 CPU @ 2.20 GHz \(\times\) 40 \\ GPU & NVIDIA GeForce RTX 2080 Ti/PCIe/SSE2 \\ Memory & 125.6 GiB \\ Software & Anaconda, Pycharm2021 \\ Deep Learning Framework & Pytorch 1.13.1 \\ GPU Acceleration Library & CUDA 11.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment configuration table.  
where \(P\) is Precision, \(R\) is Recall, \(TP\) is the number of positive samples correctly predicted as positive by the model, \(FP\) is the number of negative samples incorrectly predicted as positive by the model, and \(FN\) is the number of positive samples incorrectly predicted as negative by the model. \(AP\) is the Average Precision, \(mAP\) is the average across all categories, and \(M\) is the number of categories.  
Equation (7) shows the formula for \(IOU\), and the mapping is referred to as mAP_0.5 if the \(IOU\) threshold is set to 0.5, and mAP_0.5:0.95 if the B-threshold is set to 0.95. Additionally, as stated in Equations (1) and (2), the model parameter (\(params\)) and model volume (\(Flops\)) are determined.
## 2 Materials and Methods
### Determination of Antioxidant Activity (AA) by DPPH  
In order to determine the antioxidant activity of honey by the DPPH method, the extracts were prepared as follows: we weighed 1 g of honey, diluted it with 10 mL of ethanol 60% and then we filtered it through filter paper. The extracts thus obtained were left to incubate for 30 min. For the negative control, we used ethanol 60%; the sample was read on a UV-VIS spectrometer (Analytical Jena Specord 205, Jena, Germany) at an absorbance of 518 nm. For the positive control, we added 1 mL of DPPH solution (3 mM), 2.5 mL extract, to a test tube, we left it at incubation for 30 min and then we read it on the spectrometer (absorbance 518 nm). For the blank, we placed 1 mL of ethanol, 2.5 mL extract in a test tube, left this to incubate for 30 min, then read it at the absorbance 518 nm using a spectrometer. The antioxidant activity was calculated according to the formula:  
\[\text{AA}=100-\left\{\frac{\left[\left(\text{ABS}_{\text{sample}}-\text{ABS }_{\text{blank}}\right)\cdot 100\right]}{\text{ABS}_{\text{control}}}\right\}\]  
where:  
AA--represents the antioxidant activity of the analyzed sample;  
\(\text{ABS}_{\text{sample}}\)--represents the absorbance of the sample measured at a wavelength of 518 nm;  
\(\text{ABS}_{\text{control}}\)--represents the absorbance of the DPPH sample measured at a wavelength of 518 nm;  
\(\text{ABS}_{\text{blank}}\)--represents the absorbance of the alcohol sample measured at a wavelength of 518 nm [44].
# Leakage Detection Method of Coal Mine Power Supply System Based on 3 AC
## 3 Phase Selection of Leakage Fault Based on 3 AC Detection Method
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Insulation Resistance**} & \multicolumn{4}{c}{**Current Leakage**} \\ \cline{2-9}  & **A Phase** & **B Phase** & **C Phase** & **AB Phase** & **BC Phase** & **AC Phase** & **ABC Phase** \\ \hline
1000 \(\Omega\) & 0 & 4342 & 6268 & 2171 & 5305 & 3134 & 3536 \\
2000 \(\Omega\) & 0 & 3379 & 7230 & 1689 & 5305 & 3615 & 2536 \\
10 k\(\Omega\) & 0 & 4318 & 14,927 & 2159 & 5304 & 7463 & 3536 \\
20 k\(\Omega\) & 0 & 1 of the 'complex resistance' whose imaginary part is close to '0' is the insulation resistance when the leakage fault occurs in the system. In this way, not only is the insulation resistance value obtained, but also the phase selection of leakage fault is realized. By analogy, the insulation resistance calculation results of several other leakage conditions can be obtained, which is no longer repeated.
# Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems
## 3 Vehicle Speed Estimation
### Fusion Algorithms  
In this work, there are Recursive Filter (RF), Threshold Voting (TH), and WA in fundamental fusion algorithms. These three algorithms are the most fundamental sensor fusion algorithms using homogeneous sensors. As we employ four homogeneous wheel speed sensors, we check the results when the three algorithms are applied to estimate the vehicle speed. RF calculates precisely the present estimated values by reflecting the past estimated values. The equation of RF is as follows:  
\[y_{t}=\alpha\cdot y_{t-1}+(1-\alpha)\cdot x_{t}, \tag{2}\]  
where \(y_{t}\) is the estimated vehicle speed at time \(t\) and \(x_{t}\) is the merged sensor values by average at time \(t\). \(\alpha\) is a weight of RF, and the range of it is \(0\leq\alpha<1\).  
TF includes a sensor value for average calculation unless the distance between the sensor values is greater than a fixed threshold denoted as \(T\). The formula of TF is as below:  
\[\begin{gathered} d_{ij}=\big{|}s_{i}-s_{j}\big{|},\;\forall i,\;j: i\
eq j,\\ If\;d_{ij}\leq T,\;then\;sensors\;shall\;be\;considered\;for\;the\;fusion\; process\end{gathered} \tag{3}\]  
where \(s_{i}\;(i=1,\;2,\;3,\;4)\) is a value of \(i\)th wheel speed sensor (FL, FR, RL, RR) and \(d_{ij}\) is the difference between \(s_{i}\) and \(s_{j}\).  
WA calculates an average by assigning different weights to each sensor data. WA is expressed as follows:  
\[y_{t}=\sum_{i=1}^{n}w_{i}\cdot s_{it}, \tag{4}\]  
where \(y_{t}\) is the estimated vehicle speed at time \(t\) and \(s_{it}\) is a value of \(i\)th sensor at time \(t\). \(w_{i}\) is a weight of \(i\)th sensor and the sum of all values of \(w_{i}\) is 1. \(n\) is the total number of sensors considered in the calculation.