esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
This section summarizes the main achievements of the study. Thus, the main achievements concerning the UCS prediction are presented and discussed in Section 3.1, followed by ITS results in Section 3.2. In both sections, after an overall comparison of all four ML algorithms trained, a more in-depth analysis is presented for ANN and RF algorithms, which achieved an overall superior performance. For simplification purposes, the following notation is adopted for the models' names: ML algorithm (ANN, SVM, RF or MR) dot followed by the prediction type (UCS or ITS). For example, ANN.UCS refers to the developed model for UCS prediction based on the ANN algorithm.  
Figure 2: A correlation matrix of all variables considered in the study of UCS prediction of reinforced soil-binder-water mixtures with fibers (scatter plot of matrices (SPLOM), with bivariate scatter plots below the diagonal, histograms on the diagonal, and the Pearson correlation above the diagonal).  
The average hyperparameters and fitting time values (and respective 95% level confidence intervals according to t-student distribution) of the four ML algorithms trained for both mechanical properties prediction of soil-binder-water mixtures reinforced with fibers (i.e., ANN, SVM, RF and MR) are summarized in Table 2. The slowest one is the RF on UCS modelling, which takes an average of 6 s over the five runs. If excluding MR, SVM was the fastest one taking on average around 2 s over the five runs, followed by ANN with more than 4.7 s. As expected, MR was very fast to model UCS and ITS, taking less than 0.50 s. It should be noted that these computational times are related to the time that each algorithm took to fit the training data. In the future, when the proposed models (namely the ANN and RF models) are applied to predict new cases, the time required is very close to zero (the computation is almost instantaneous). In terms of hyperparameter, and particularly for the ANN, the optimized number of neurons in the hidden layer was 6 and 5, respectively, for UCS and ITS prediction.  
Table 3 compares the performance of the four ML algorithms in both UCS and ITS prediction of soil-binder-water mixtures reinforced with fibers based on MAE, RMSE and R\({}^{2}\) metrics (mean value and respective 95% level confidence intervals according to t-student distribution). Apart from MR, all other three algorithms present a particularly good and similar performance in both mechanical properties' prediction of soil-binder-water mixtures reinforced with fibers. Taken R\({}^{2}\) as a reference, all three algorithms (ANN, SVM and RF) achieved, on average, a value close to 0.95.  
A detailed analysis shows that ANN achieved an overall superior performance on both mechanical properties' prediction (best values in bold in Table 3 as described in Section 2.2), followed by RF and SVM. As expected, the lower performance is observed for MR, which evidenced clear difficulties in modelling UCS and ITS efficiently, which can be explained by the characteristic non-linear behavior of soil-binder-water mixtures reinforced with fibers.
# Scaling of FAPAR from the Field to the Satellite
## 4 Results and Analysis
### Estimating Fine-Resolution FAPAR and Spatial Scaling  
The linear regression is easily affected by the distribution of data. To identify such effects, the robust regression using IRLS algorithm was applied to the subset of data on each day and to the entire dataset sequentially, as shown in Figure 10. Clearly the fits with each subset and the entire dataset did differ in both the slope and the intercept of the regression line. The goodness of the fits were evaluated by \(R^{2}\) and RMSE (Table 4). The fit on data collected on 30 May and 15 June showed less favorable performance with a low value of \(R^{2}\) and a high value of RMSE. In all cases, the coefficient \(c_{1}\) passed the significance test of \(p<0.005\), but on three out of four subsets the coefficient \(c_{2}\) failed the test. This failure could be attributed to the very limited sample amount and the concentrated data distribution on each single day. Comparatively, the fit on the entire dataset showed stable and good performance in all evaluation metrics, and was finally selected as the function transferring from NDVI to FAPAR.  
Figure 11 shows the ASTER FAPAR derived from the empirical transfer function at 15 m resolution. Spatial aggregation was applied to the ASTER FAPAR at 15 m to derive the FAPAR truth data at 960 m. The mean value within each 64 \(\times\) 64 window was calculated as the value for the new 960 m resolution pixel. This process is feasible as FAPAR is a self-consistent physical quantity across scales. Then FAPAR truth data were available at both 15 m and 960 m resolution, based on which direct comparison is now applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & \(\boldsymbol{c_{1}*}\) & \(\boldsymbol{c_{2}*}\) & \(\boldsymbol{R^{2}}\) & **RMSE** & \(\boldsymbol{p(c_{1})}\) & \(\boldsymbol{p(c_{2})}\) \\ \hline
30 May & 0.839 & \(-\)0.055 & 0.619 & 0.052 & 0.000 & 0.148 \\
15 June & 1.241 & \(-\)0.164 & 0.816 & 0.094 & 0.000 & 0.017 \\
24 June & 1.290 & \(-\)0.148 & 0.896 & 0.095 & 0.000 & 0.013 \\
10 July & 1.436 & \(-\)0.204 & 0.967 & 0.079 & 0.000 & 0.000 \\ All & 1.359 & \(-\)0.169 & 0.934 & 0.077 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
* \(c_{1}\) and \(c_{2}\) refers to the regression coefficient in Equation (8).  
\end{table}
Table 4: Statistics of the fit using various subset of _in-situ_ data.  
Figure 10: Scatter plot of Normalized Difference Vegetation Index (NDVI) derived from ASTER and the _in-situ_ FAPAR measurements from different dates.
## Materials and Methods
### Calculation of CO\({}_{2}\) Flux  
Since CO\({}_{2}\) cannot be mapped directly by satellite images, it is calculated through the relationship between CO\({}_{2}\) and GPP. In general, CO\({}_{2}\) in the atmosphere is generated through the following three processes. First, through vegetation activity, i.e., the process of absorbing CO\({}_{2}\) in the atmosphere and producing oxygen (photosynthesis). Second, the process of vegetation biomass maintenance. Third, respiration according to dependent nutrition. Plants have a bidirectional flux that absorbs CO\({}_{2}\) from the atmosphere through photosynthesis and recirculates part of it to the atmosphere through respiration. The total amount of organic matter produced by plants by absorbing CO\({}_{2}\) through photosynthesis, or the total amount of carbon generated through the CO\({}_{2}\) circulation in the atmosphere, which is called GPP. Amid these processes, the relationship between CO\({}_{2}\) and GPP can be expressed by the following equation.  
\[\mathrm{NEE}=\mathrm{R_{e}}-\mathrm{GPP}, \tag{1}\]  
where NEE, (net ecosystem exchange) is a measure of the net exchange of carbon between an ecosystem and the atmosphere and is a primary measurement of the ecosystem's carbon sink strength [33; 34; 35; 36]. \(\mathrm{R_{e}}\) is the ecosystem respiration of plants. NEE is the CO\({}_{2}\) flux data measured in the flux tower applied in this study. The relationship between CO\({}_{2}\) flux and GPP can be calculated from the Re value. Re is calculated by the following Equation [37];  
\[\mathrm{R_{e}} =\mathrm{R_{ref}}\mathrm{exp}(\mathrm{E_{0}}\bigg{[}\frac{1}{ \mathrm{T_{ref}}-\mathrm{T_{0}}}-\frac{1}{\mathrm{T_{a}}-\mathrm{T_{0}}} \bigg{]}), \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Latitude (\(\mathbf{{}^{\circ}N}\))** & **Longitude (\(\mathbf{{}^{\circ}E}\))** & **Altitude (\(\mathbf{{}^{\circ}}\))** & **Land Cover** & **LAI** & **Terrain** \\ \hline Cheonmicheon (CMC) & 37.16 & 127.65 & 141 & Rice Paddy & 1–2 & Flat \\ Sulmacheon (SMC) & 37.94 & 126.95 & 293 & Mixed Forest & 6–8 & Mountainous \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the selected sites in study area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Product ID** & **Product** & **Spatial Resolution (km)** & **Temporal Resolution** \\ \hline MOD11A1 & Land surface temperature & 0.5 & Daily \\ MCD12Q1 & Land cover & 0.5 & Yearly \\ MOD13A1 & Vegetation indices & 0.5 & 16 days \\ MOD17A2H & Gross primary productivity & 0.5 & 8 days \\ MCD43A3 & Albedo & 0.5 & Daily \\ \hline \hline \end{tabular}
\end{table}
Table 2: Remote sensing data used in this study.  
where \(\rm R_{ref}\) is the reference ecosystem respiration, \(\rm T_{ref}\) the reference temperature (10 \({}^{\circ}\)C), \(\rm E_{0}\) the activation energy, \(\rm T_{0}\) -46.02 \({}^{\circ}\)C, and \(\rm T_{a}\) the air temperature. \(\rm R_{ref}\) and \(\rm E_{0}\) are the empirical constants obtained by regression with the air temperature [37].
# Dynamic Responses Measured by Optical Fiber Sensor for Structural Health Monitoring
## 4 Shock and Vibration Tests
### Impact Test  
The cantilever beam is impacted by a hammer, as shown in Figure 11. The dynamic strains of the cantilever beam measured by the FBG and strain gauge are plotted in Figure 12. It can be observed that the transient responses measured by the FBG sensor are in close agreement with the results measured by the strain gauge. The average difference between the FBG and strain gauge is 4.7%. The frequency response of the cantilever beam can be obtained by taking the fast Fourier transform (FFT) of the time response as shown in Figure 13. The natural frequencies (7.47 Hz and 46.98 Hz) deduced from the  
Figure 10: Vibrational response of a cantilever beam subjected to dual frequencies base excitation.  
Figure 9: Vibrational response measured by Mach-Zehnder interferometric optical sensor [43].
## 4 Analyses
### Q1: How Much Information Is Lost with Reduced Temporal Resolution?
#### 4.1.1 Comparison of Descriptive Statistics
* A corresponding effect for the minima, i.e., an increasing trend, could not be seen for those variables with a defined lower boundary, e.g., 0 m/s for the wind speed. Here, lower boundaries were preserved. For other variables, a similar but much less pronounced effect was existent, e.g., for the active power generation. The one exception was the voltages, as their value only dropped from the reference value in the case of disturbances. Figure 2: Illustration of the descriptive statistics of wind speed, active power generation, generator speed, temperature of gearbox shaft bearing 1, and voltage of phase A at different levels of temporal aggregation for wind turbine 5. On the top: box-and-whisker plots with the median depicted by the yellow bar, and the mean by the green triangles surrounded by \(\pm\) the standard deviation as green bars. The black box displays the interquartile range, the black whiskers denote the maximum and minimum, including outliers. On the bottom: scatter plots of skewness and kurtosis normalized to standard deviation with interconnecting lines for better visibility. Mean and median faintly declined for most signals with increasing temporal aggregation, though in the illustration no noticeable visual differences could be obtained. * The standard deviation and the interquartile range (IQR) behaved similarly, although both also experienced only small changes: For some values they faintly decreased, e.g., for the wind speed and the active power, whereas for others like generator speed and the temperature of the gearbox shaft bearings they made a small increasing step between 1 s and 10 s.
* The values of skewness were rather close to each other for different levels of temporal aggregation. Although the base values scattered a lot, the median of their changes for all turbines and signals lay below 5% with respect to the raw signal. An explicit decline could only be seen for the wind speed. * For the kurtosis a clear trend could only be observed for the wind speed and the voltages where there was a clear decline with increasing temporal aggregation. For the rest of the signals this was not as clear: for several signals there was almost no difference, some signal tended to increase for one turbine, but decreased for another. However, the median of all changes lay below 5% change in standard deviation with respect to its value at 1 s resolution. In summary, the statistics of typically fast changing signals such as wind speed, active power, generator speed, and electrical signal were the most affected by downsampling as the values of their statistics varied widely with the resolution of the signal. Temperature signals, on the other hand, were far less affected by downsampling.
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 3 Common Machine Learning Algorithms in IDS
### Metrics  
Many metrics are used to evaluate machine learning methods. The optimal models are selected using these metrics. To comprehensively measure the detection effect, multiple metrics are often used simultaneously in IDS research.  
* **Accuracy** is defined as the ratio of correctly classified samples to total samples. Accuracy is a suitable metric when the dataset is balanced. In real network environments; however, normal samples are far more abundant than are abnormal samples; thus, accuracy may not be a suitable metric. \[Accuracy=\frac{TP+TN}{TP+FP+FN+TN}\] (3)
* **Precision (P)** is defined as the ratio of true positive samples to predicted positive samples; it represents the confidence of attack detection. \[P=\frac{TP}{TP+FP}\] (4)
* **Recall (R)** is defined as the ratio of true positive samples to total positive samples and is also called the detection rate. The detection rate reflects the model's ability to recognize attacks, which is an important metric in IDS. \[R=\frac{TP}{TP+FN}\] (5)
* **F-measure (F)** is defined as the harmonic average of the precision and the recall. \[F=\frac{2*P*R}{P+R}\] (6)
* **The false negative rate (FNR)** is defined as the ratio of false negative samples to total positive samples. In attack detection, the FNR is also called the missed alarm rate. \[FNR=\frac{FN}{TP+FN}\] (7)
* **The false positive rate (FPR)** is defined as the ratio of false positive samples to predicted positive samples. In attack detection, the FPR is also called the false alarm rate, and it is calculated as follows: \[FPR=\frac{FP}{TP+FP}\] (8)  
where the TP is the true positives, FP is the false positives, TN is the true negatives, FN is the false negatives. The purpose of an IDS is to recognize attacks; therefore, attack samples are usually regarded as positives, and normal samples are usually regarded as negatives. In attack detection, the frequently used metrics include accuracy, recall (or detection rate), FNR (or missed alarm rate), and FPR (or false alarm rate).
## 3 Computational Examples and Analysis
### Case Study I: Bi-Objective Optimization
#### 3.1.1 Data Description  
In the optimization process of SMA-13 containing basalt fibre designed by RSM, raw materials included SBS-modified asphalt, coarse and fine aggregates, mineral filler and basalt fibre, whose basic physical properties were summarized in the previous study [16]. The experiment with three independent factors at three experimental levels was devised according to the face-centered central composite RSM. The preparation parameters are regarded as fibre content (\(X_{1}\)) and its length (\(X_{2}\)) as well as asphalt-aggregate ratio (\(X_{3}\)); and Marshall stability (\(MS\)) (\(Y_{1}\)) and flow (\(FV\)) (\(Y_{2}\)) are performance parameters, air voids (\(VA\)) (\(Y_{3}\)), voids in mineral aggregates (\(VMA\)) (\(Y_{4}\)) and voids filled with asphalt (\(VFA\)) (\(Y_{5}\)) represent volumetric properties. The experimental design and outputs with 19 groups of basalt fibre-reinforced asphalt mixtures are listed in detail in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{4}{c}{**Preparation Parameters**} & \multicolumn{4}{c}{**Responses or Dependent Variables**} \\ \cline{2-9}  & \(\mathbf{X_{1}\) (\%)} & \(\mathbf{X_{2}\) (mm)} & \(\mathbf{X_{3}\) (\%)} & \(\mathbf{Y_{1}\) (kN)} & \(\mathbf{Y_{2}\) (mm)} & \
## 3 Experiment
### Effectiveness of RPS-ResNet  
In Section 2.2.2, we introduced RPS-ResNet, a ResNet-50 improvement designed based on the distribution characteristics of UGS. To investigate the effectiveness of RPS-ResNet, we conducted the following ablation study on the GS2018 dataset. GSPRnet and GSRSnet represent models using RPS-ResNet and ResNet-50 as the feature extraction backbone, respectively. Both models were trained and validated under identical conditions. Figure 8 illustrates the performance curves of the two models. GSPRnet exhibits significant advantages over GSRSnet both in the initial and highest performance. Moreover, GSPRnet's IoU accuracy improvement curve is smoother, indicating a more stable training process, with less fluctuation in IoU metrics during training. This suggests that the enhanced RPS-ResNet outperforms the original ResNet-50 in UGS segmentation tasks.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Conv1**} & \multirow{2}{*}{**Max Pooling**} & \multirow{2}{*}{**Conv2**} & \multirow{2}{*}{**Conv3**} & \multirow{2}{*}{**Conv4**} & \multirow{2}{*}{**Conv5**} & \begin{tabular}{c} **IoU \(\uparrow\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **OA \(\uparrow\)** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Params \(\downarrow\)** \\ **(M)** \\ \end{tabular} &
\begin{tabular}{c} **FLOPs \(\downarrow\)** \\ **(G)** \\ \end{tabular} \\ \hline Model-1 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 84.090 & 93.221 & 51.406 & 339.666 \\ Model-2 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 84.067 & 93.382 & 18.008 & **179.203** \\ Model-3 & \(\surd\) & & \(\surd\) & \(\surd\) & 84.274 & 93.463 & 18.008 & 216.042 \\ Model-4 & Reverse Pixel Shuffle & \(\surd\) & \(\surd\) & \(\surd\) & **86.702** & **94.393** & **17.999** & 215.751 \\ \hline \hline \end{tabular}  
* The number of FLOPs were tested using an open-source package for a single image with a resolution of 256 \(\times\) 256. The \(\uparrow\) and \(\downarrow\) indicate the larger the better and the smaller the better, respectively.  
\end{table}
Table 1: Ablation study for the urban green space segmentation model’s structure on the GS2018 validation dataset. The best and second-best results are highlighted in bold and underlined, respectively.  
Figure 8: Ablation study for the different feature extraction backbones on the GS2018 validation dataset.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 3 Methodology  
In this section, the authors describe the methodology followed in the study. In the first step, the accelerometer data are observed, and a simple visual assessment is made to see if there is any macroscopic anomaly. If no anomaly is found visually, the accelerometer data are analyzed using the peak-picking method; the simplest and least computationally demanding OMA method.  
The frequencies obtained by peak-picking are then compared with the theoretical results obtained from mechanical formulations of vibrating rods and ropes.  
After this first phase, two more complex OMA methods with higher computational burdens are applied: Cov-SSI and PolyMAX. The frequencies of the first three vibration modes are calculated.  
These frequencies show a trend over time that is correlated with the ambient temperature trend. The correlation with temperature is cleaned to the best possible standard and the reliability of the cleaned frequencies is assessed, attempting to find known damages introduced in the rods.  
Figure 1 shows the flow-chart of the study.  
Figure 1: Flow chart of the study.
# Parallel Bidirectionally Pretrained Taggers as Feature Generators
### Research Questions, Aims, Means, and Novelty  
In this paper, we propose a novel stacked language-independent classifier architecture. We use probabilistic outputs of multiple bidirectionally pretrained POS-taggers (spaCy, TreeTagger and Stanza) as inputs to an improved neural network classifier. The approach is then evaluated on texts in Serbian with the aim of answering several research questions.  
1. Is bidirectional training of POS-taggers an appropriate way to create new, independent models of unique value?
2. Can composition-based POS-taggers outperform standalone pretrained taggers?
3. Is the stacked classifier the optimal way to combine outputs of pretrained POS-taggers in order to improve the overall performance and stability?
4. Is bidirectional training of POS-taggers through dataset augmentation an easy and appropriate way to further improve the results of the stacking architecture as compared with standalone taggers?  
To that end, we used the following steps:  
1. Procure a balanced dataset of texts, preannotated for POS using at least one tagset;
2. Test several POS-taggers that are able to output the probabilities necessary for our stacked architecture, train them bidirectionally using dataset augmentation, and determine which ones would contribute most in a composite environment by measuring their mutual entropy and their ability to produce uniquely correct annotations;
3. Train composition-based classifiers that use probabilistic outputs of selected pre-trained standalone taggers as features;
4. Evaluate the relative performance of standalone taggers, unidirectionally and bidirectionally built composite taggers, as well as several other combination techniques.  
The main contributions of this paper are the following:  
Figure 1: Simple composition algorithm based on the outputs of the TreeTagger and spaCy POS-taggers, together with TreeTagger’s probability outputs.  
1. A novel bidirectional training architecture, where the input is not only the verticalized training set but its augmented, inverted variant as well;
2. A novel stacked tagger architecture combining off-the-shelf taggers;
3. The first bidirectional combined POS-tagger for the Serbian language, which outperforms the state-of-the-art taggers [19];
4. An up-to-date comparative analysis of the state-of-the-art taggers.  
This article is structured as follows. The second section introduces the dataset that was used for the training and evaluation of POS-taggers for this research and explores the process of initial tagger selection. It also tackles the methodology used for the bidirectional training of taggers and explores training, tagging, and evaluation pipelines. The third section presents the results obtained through the evaluation process, followed by a discussion of the results and concluding remarks, together with plans for future research.
## 3 Results
### WOB Level Classification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Specificity (\%)**} & \multicolumn{2}{c}{**Sensitivity (\%)**} & \multicolumn{2}{c}{**Sensitivity (\%)**} \\  & **E1** & **E2** & **E1** & **E2** & **E1** & **E2** & **E1** & **E2** \\ \hline
**LDA** & 56.58 & 55.42 & 57.69 & 17.39 & 63.89 & 75.56 & 35.71 & 53.33 \\
**SVM** & 47.37 & 56.63 & 0 & 0 & 100 & 100 & 0 & 0 \\
**KNN** & 39.47 & 54.22 & 34.62 & 52.17 & 41.67 & 64.44 & 42.86 & 26.67 \\
**STACKING** & 35.53 & 46.99 & 53.85 & 17.39 & 25.00 & 77.78 & 21.43 & 0 \\
**(LDA-SVM-KNN)** & 40.79 & 48.19 & 42.31 & 17.39 & 44.44 & 75.56 & 28.57 & 13.33 \\
**STACKING** & 47.37 & 37.35 & 42.31 & 65.22 & 66.67 & 24.44 & 7.14 & 33.33 \\
**(SVM-KNN-KNN)** & & & & & & & & \\
**STACKING** & 50.00 & 43.37 & 34.62 & 34.78 & 66.67 & 62.22 & 35.71 & 0 \\
**(LDA-SVM-KNN-KNN)** & & 80.00 & 82.35 & 92.31 & 82.61 & 91.67 & 100 & 100 & 80 \\
**NN** & [93.42] & [91.57] & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracies, specificities, and sensitivities obtained for each of the machine learning algorithms for both experiments. In the case of neural networks, the accuracy is determined using 20% of the dataset for validation and the between-brackets accuracy is determined using 100% of the dataset. Figure 6: Validation and training loss function relation for the one thousand NN of experiment 1 (E1: restrictive dataset) and experiment 2 (E2: obstructive dataset).