esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 3 Statistical Techniques in Spectroscopy
### Data Processing  
Spectroscopic data are often high-dimensional, with thousands of variables per sample, requiring robust techniques for analysis and interpretation. Data processing is crucial in spectroscopic analysis, ensuring that raw spectral data are cleaned, normalized, and transformed to enhance signal quality and eliminate unwanted variations [123; 126]. Standard preprocessing techniques include:  
Visualizing spectral data using techniques such as boxplots, heatmaps, histograms, and scatterplots, and computing descriptive statistical measures like mean, median, standard deviation, and correlation constitutes a fundamental step in preprocessing and interpreting spectral datasets.  
Baseline correction, smoothing, and normalization techniques such as asymmetric least squares (ALS), Savitzky-Golay filtering, and vector normalization improve signal clarity, reduce noise, and ensure consistency across variables with different ranges.  
Reduction techniques, such as PCA, Factor Analysis (FA), Categorical Principal Component Analysis (CATPCA), and the wavelet transform, help to extract relevant information while eliminating redundant data [124; 128; 129].  
Additionally, statistical approaches such as the Mahalanobis distance and Grubbs' test are employed for outlier detection [130; 131; 132].  
In wine analysis, PCA is frequently used to process Fourier transform infrared (FTIR) spectra to identify the primary sources of variation. In contrast, FA is used to identify latent variables that influence spectral data and wavelet transform. The wavelet transform helps to decompose spectral data into different frequency components.  
[PERSON] et al. [129] utilized PCA to analyze FTIR and Raman spectra of red grape skin, demonstrating its efficacy in managing spectral data redundancy. In another study, PCA was applied to identify the primary sources of variation in FTIR spectra of 329 wines of various styles, enabling the identification and interpretation of samples and the identification of outlier samples [133].  
Therefore, integrating PCA, FA, and WT into the analysis of wine spectral data helps minimize redundancy, enhances the extraction of meaningful information, and improves the accuracy of predictive models used to assess and classify wine quality.  
Through effective data processing, researchers can gain a deeper understanding of spectral data, identify key trends, and prepare the data for further analysis, such as classification, clustering, or predictive modeling.
## 5 Experimental Results and Discussion
### Validation and Analysis of Effectiveness  
The No. 2 battery is used to test the model trained by the SVM to verify that the algorithm is not an accidental result but is suitable for this type of LIB. The health factor and the corresponding discharge capacity (the available capacity) of the No. 2 battery are used as the test data set to test the SVM model of the No. 1 battery and the error results are shown in Table 7. The OCV test information about the No. 2 battery is used to test the model shown in Table 8. The meaning of each parameter in Table 8 is same as the corresponding parameter in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cycle Number**} & **Real Available Capacity** & \multicolumn{2}{c}{**Estimated Available**} & \multirow{2}{*}{**Real SOH**} & \multirow{2}{*}{**Estimated SOH**} & \multirow{2}{*}{**Relative Error**} \\  & **(mAH)** & & & & \\ \hline \(1\) & 1495.3 & 1486.91 & 99.69\% & 99.13\% & 0.56\% \\ \(100\) & 1486.2 & 1473.59 & 99.08\% & 98.24\% & 0.85\% \\ \(195\) & 1458.4 & 1450.04 & 97.23\% & 96.67\% & 0.57\% \\ \(293\) & 1412.3 & 1430.27 & 94.15\% & 95.35\% & 1.27\% \\ \(392\) & 1365.9 & 1394.54 & 91.06\% & 92.97\% & 2.10\% \\ \(553\) & 1267.3 & 1308.15 & 84.49\% & 87.21\% & 3.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Estimated error of method based on OCV.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cycle Number**} & **Real Available Capacity** & \multicolumn{2}{c}{**Estimated Available**} & \multirow{2}{*}{**Real SOH**} & \multirow{2}{*}{**Estimated SOH**} & \multirow{2}{*}{**Relative Error**} \\  & **(mAH)** & & & & \\ \hline \(60\) & 1492.8 & 1497.524 & 99.52\% & 99.83\% & 0.31\% \\ \(120\) & 1473.8 & 1468.611 & 98.25\% & 97.91\% & 0.35\% \\ \(180\) & 1453.7 & 1482.828 & 96.91\% & 98.86\% & 2.00\% \\ \(240\) & 1429.9 & 1442.154 & 95.33\% & 96.14\% & 0.85\% \\ \(300\) & 1407.4 & 1446.934 & 93.83\% & 96.46\% & 2.81\% \\ \(360\) & 1400.7 & 1421.848 & 93.38\% & 94.79\% & 1.49\% \\ \(420\) & 1353.6 & 1369.674 & 90.24\% & 91.31\% & 1.19\% \\ \(480\) & 1318.8 & 1325.427 & 87.92\% & 88.36\% & 0.50\% \\ \(540\) & 1238.4 & 1247.826 & 82.56\% & 83.19\% & 0.76\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Estimated error of No.2 battery in test \(1\).  
Tables 7 and 8 presented that when the trained SVM model of the No. 1 battery is used to estimate the available capacity and SOH of the No. 2 battery, the relative error of the No. 2 battery is bigger than that of the No. 1 battery; most of the estimated relative errors are within 2% and the maximum relative error is less than 3%. The results also indicate that the model built using the No. 1 battery can be used for all batteries of the same type. In other words, it is feasible to use the terminal voltage drop information to estimate the available capacity and SOH. It is important to note that, due to the inherent inconsistencies of the batteries, there are some differences even for the same type of battery. These differences can explain why the overall estimation error of the No. 2 battery is larger than that of the No. 1 battery.
# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 2 Methods
### Research Questions  
As stated before, the goal of this systematic mapping study is to provide an overview of the current research on the topic of computer vision in autonomous UAVs. This way, we will obtain a broad view of navigation and flight control operations in which computer vision is relevant. Consequently, it will be possible to address future in-depth studies for the development of autonomous aerial robots, and, particularly, assistant UAVs. This overall approach has led to the following research questions:  
* _RQ1: How many papers have been published on computer vision in autonomous UAVs?_ This first question aims to discover the number of works about the use of computer vision in autonomous UAVs that have been published and the forums in which these papers are most frequently published.
* _RQ2: How is computer vision used in autonomous UAVs?_ The second question concerns the idea of grouping articles according to the type of operation for which computer vision is used. Four main categories related to navigation, stability and maneuver control, guidance or tracking, and obstacle detection and avoidance are considered. This enables us to know on what areas the research is principally focused, and allows us to carry out a more in-depth analysis of the remaining questions.
* _RQ3: What types of UAVs have been used in the proposed solutions?_ One of the most determining aspects is to discover for what type of UAV platform each proposal has been designed and validated. This is precisely the goal of the third question. In addition, by considering the first classification on the use of vision, we can get an idea of which type of UAV is most frequently used in each operation.
* _RQ4: What are the characteristics of the vision systems?_ The number, localization, and orientation of the cameras will be analyzed at this point in order to draw conclusions about the main features of the vision systems commonly used in each navigation and flight control area.
* _RQ5: What is the validation process for each proposed solution?_ The last research question is related to the kind of tests carried out to validate the proposals; flight, experimental, and simulation trials, or a combination of them. The idea is to perform an analysis of the kind of tests most frequently performed for each main category considered.
## 2 Proposed Finite Element Model
### Validation of Finite Element Model
#### 2.1.1 Model Geometry  
The test 1a includes the measurement of horizontal and vertical deflections and circumferential deformations on an instrumented steel pipe. The pipe, 72 inches diameter and 0.313 inches thickness, was installed inside a concrete box and buried using a layered clay soil compaction procedure, with an average thickness of 9 inches per layer. To simulate the upper external load and pressure at the pipe crown of approximately 62 kPa, a wooden box was installed.  
The results of Test 1a are shown in Section 2.3 along with the FEM simulation.  
The geometry of the pipe trench is determined by the pipe, the gravel bed, the compacted lateral layers and the fill over the pipe. In the experimental study [16] the concrete box was represented as a rigid element where lateral displacement was restricted at the walls and vertical displacement at the base. See Figure 5.  
Figure 4: Installation section for validation test 1a (mm). Adapted from [PERSON] [16].  
The length of the finite element model has been defined according to the sensitivity analysis described in Section 2.2.2.
# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
## 3 System Models and Problem Formulation
### Task Model  
#### 3.2.1 Application Model  
The satellite period is discretized into n time slots. There are a total of M mobile devices in the system, and the device number set is represented as \(M=\left\{1,2,3\cdots m\right\}\), and indexed with \(i\). The application generated on each mobile device can be divided into multiple tasks with dependencies, and the calculation task set is expressed as \(N=\left\{1,2,3\cdots n\right\}\), indexed by \(j\). Due to the dependencies between the divided tasks, the structure of the application can be represented by a directed acyclic graph DAG, denoted as \(G_{i}=\left(V_{i},E_{i}\right)\), in which \(V_{i}\) represents the set of subtask nodes in the \(i\)th application \(V_{i}=\left(v_{i,1},v_{i,2}\ldots v_{i,n}\right)\). In \(G_{i}=\left(V_{i},E_{i}\right)\), for a task \(v_{i,j}\), \(v_{i,j}=\left(C_{i,j},D_{i,j}\right)\), \(C_{i,j}\) represents the CPU cycle required to process the task, the unit is cycle, \(D_{i,j}\) represents the size of the current task in bits, and the deadline delay of the application is expressed as \(T_{d}^{i}\). \(E_{i}\) represents the set of directed edges between compute tasks \(E_{i}=\left\{e_{j,k}^{i}=\left(v_{i,j},v_{i,k}\right)|v_{i,j},v_{i,k}\in V_{i}\right\}\). The presence of directed edges between two vertices indicates that the end vertex cannot be processed earlier than the starting vertex of the directed edge; that is, there is a dependency between tasks. When a task is unloaded, the MEC server allocates resources to it until the task processing result is completed. At the beginning of each time slot, each mobile device requests an application for processing, and the task processing flow is as follows:  
1. At the beginning of each time slot, multiple users on the ground initiate a task request.
2. Through the scheduling algorithm, the task processing approach and the set of subtasks to be processed are determined.
3. If the task needs to be offloaded to the satellite, the resource allocation algorithm obtains the resources to be allocated.
4. The satellite edge node calculates the results for the ground user.  
Figure 1: Architecture diagram of satellite edge network.  
According to the task processing flow, the total delay generated by the whole system can be divided into processing delay and transmission delay. The overall processing latency of the application is then compared to the maximum tolerated delay in the task model. If it is less than that, the processing succeeds; otherwise, the processing fails.
# Dynamic Analysis of Underwater Torpedo during Straight-Line Navigation
## 5 Results and Discussion  
### Navigation Depth and Pitch Angles under Different Munk Moment Coefficients  
As can be seen from Figure 3, under the same Munk moment coefficient, the navigation depth shows an increasing trend in the time domain. At the same time, with the increase of the Munk moment coefficient, the torpedo will increase its navigation depth, and the deviation from the initial navigation depth will increase gradually. The two curves of the 0 and 0.02 Monk moment coincide. In Figure 4, the pitch angle also shows an increasing trend in the time domain. When the Munk moment coefficients are 0.06, 0.08, and 0.1, the navigation depth of the torpedo will suddenly drop from 50 s. The larger the Munk moment, the faster the torpedo will drop. The increase in pitch angle precedes the decrease in navigation depth. When the Munk moment coefficient is 0.10, the torpedo has already touched the seabed at about 81.7 s. This phenomenon can be observed in Figures 3 and 4, where the curve presents obvious nonlinearity. The reasonable explanation for this phenomenon is that the pitch moment is strongly affected by the Munk moment and increases with the increase of the Munk moment coefficient. The increase in the pitch moment will lead to an increase in the pitch angle, and the thrust \(T\) acts in the negative direction of the \(x\)-axis, thus increasing the navigation depth.  
Figure 2: Validation results: (**a**) torpedo’s speed in the \(x\)-direction; (**b**) horizontal displacement in the \(x\)-direction; (**c**) torpedo’s navigation depth; (**d**) torpedo’s lateral displacement.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### Application Prospects Based on Case Study Insights
#### 5.4.3 Smart City Infrastructure  
Apart from sound monitoring, the system's ability to integrate other environmental parameters, such as air quality, temperature, and light intensity, increases its usefulness for smart city initiatives. The case study shows that the system can serve as a multipurpose environmental monitoring tool, with the potential to gather additional parameters by incorporating more sensors. In congested urban areas, for instance, the system could monitor both noise and air pollution levels, offering critical data to support urban health initiatives. Additionally, the system's ability to classify sound sources like vehicles and pedestrian signals suggests potential for use in smart traffic management. With real-time traffic noise and congestion data, the system could help optimize traffic flow and improve air quality by suggesting alternative routes or adjusting traffic signal timing.
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 4 Experiments, Results, and Analysis
### Individual Prediction Interpretation--Localized Explanation  
We performed two analyses to look for a suitable measure of feature importance. SHAP and LIME provide a localized explanation of a specific prediction. Both local and global explanations show the complexity of models when making predictions. SHAP local explanation in Figure 6 visually shows the features considered important by the classifier in making an individual prediction. The prediction reflects the probability that the input traffic is a DOS attack. Results showed that the base value of 79.24% (model's average prediction over the training set) would be predicted if the features to the current output f(x) were unknown. However, the classifier was able to predict the DOS attack with 100% accuracy. The top features that predict the attack are shown in red color and are count, dst_host_count, service_ecr_i, service_http, etc., whereas the blue features dst_host_srv_count drives the prediction value lower. The larger arrow size of the count shows the magnitude of this feature's effect on making a classification decision.  
Figure 7 shows the local explanation of a DoS vs. NOT DoS attack using LIME. The classifier predicts that the traffic is a DoS attack, and the LIME bar chart highlights the importance given to the most relevant features that led to the prediction. As shown in Figure 7, protocol_type_udp, logged_in, count, srv_count, and dst_host_srv_count are depicted as contributing to the \"DOS\" prediction, while error_rate is against it. Cybersecurity experts can review these granular details to make an informed decision about trusting the model's predictions.  
Figure 5: Hybrid classifier models’ accuracy.  
Figure 6: SHAP local explanation of a DOS attack.
## 3 Results and Discussion
The improvement achieved is visible, again, both in the position of the scatter points as well as the histogram distribution. Figure 2: The results of the LR performed in the year 2020. Figure 4: The results of the LR performed in the year 2021. Figure 3: The results of the ANN calibration, trained on data from 2019 and applied to data from 2020. When it comes to the PM\({}_{10}\) measurements, no significant change in the concentrations throughout the years can be observed in the reference measurements. The low-cost measurements are successfully corrected for 2020 using the ANN, but for 2021 this is not the case. In contrast to the NO\({}_{2}\) measurements, the results for 2021 are better when the ANN is trained solely on data from 2020. This can be expected considering the nature of the low-cost PM\({}_{10}\) sensors and their degradation. The low-cost PM\({}_{10}\) sensors are optical, and they measure the light that is reflected off the particles that enter the sensor chamber. As time passes, the particles accumulate, blocking the entrance to the sensor chamber, resulting in lower PM\({}_{10}\) measurements as the particles cannot properly circulate through the sensor chamber and therefore refract less light. Taking this into account, the low-cost sensors' behavior in 2019 is vastly different from their behavior in 2021. Because of this, the ANN gains no benefit from 2019 data as they do not represent the same dependency between the variables that can be observed in 2021. Finally, the improvement for 2021 (when the ANN is trained on 2020 data) is present only for the R2 factor, while the RMSE is higher than the one obtained after LR. This is most likely because of the changes in the baseline of the low-cost sensor, caused by sensor degradation, although the overall behavior of the sensor in 2021 is still similar enough to the one in 2020 to provide an increase in R2.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Our Work** & **[24]** & **[25]** & **[26]** \\ \hline Focus & \begin{tabular}{c} Generating \\ personalized Thai \\ music playlists \\ \end{tabular} & \begin{tabular}{c} Predicting \\ valence-arousal values \\ of Thai music \\ \end{tabular} & \begin{tabular}{c} Evaluating Western \\ music emotion models \\ on Thai music \\ \end{tabular} & \begin{tabular}{c} Developing an emotion \\ classification system for \\ digital music with a \\ cascaded technique \\ \end{tabular} \\ \hline Methods & Random Forest & \begin{tabular}{c} Regression models \\ (MLR, SVR) \\ \end{tabular} & \begin{tabular}{c} Regression models \\ (MLR, SVR) \\ \end{tabular} & \begin{tabular}{c} Cascaded neural \\ networks (LM, Rprop) \\ \end{tabular} \\ \hline Data & \begin{tabular}{c} 1500 Thai song lyrics \\ with mood labels \\ \end{tabular} & \begin{tabular}{c} 155 popular Thai songs \\ with Spotify API data \\ \end{tabular} & \begin{tabular}{c} MediaEval2013 \\ Western music dataset, \\ 125 Thai songs with \\ Spotify API data \\ \end{tabular} & \begin{tabular}{c} MediaEval Database \\ for Emotional Analysis \\ (DEAM) benchmark \\ \end{tabular} \\ \hline Evaluation & F1 score of 83.5\% & \begin{tabular}{c} MLR outperforms SVR \\ accuracy of 61.29\% \\ \end{tabular} & \
\begin{tabular}{c} MLR outperforms SVR \\ accuracy of 61.29\% \\ \end{tabular} &
\begin{tabular}{c} Cascaded Rprop \\ achieves the best \\ accuracy of 89.5\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparisons of recent works with our proposed method.
# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 2 Materials and Methods
### Methods  
#### 2.3.1 Random Forest  
Random forest is an ensemble learning method that improves overall performance by combining the predictions of multiple models. It uses bootstrap to randomly sample from the training data to generate different training sets. In order to reduce the risk of overfitting, it only randomly selects some features.  
For regression problems, randomly select samples from the training set and construct the \(m\)-th tree, denoted as \(T_{m}\). At each node, randomly select m features and find the best split feature. Use the selected features to split the nodes until the stopping condition (such as the depth of the tree, the number of samples in the leaf node, etc.) is met. Assume there are N trees, and the prediction result of the \(m\)-th tree is \(\hat{y}_{m}\). The final prediction is as follows:  
\[\hat{y}=\frac{1}{N}\sum_{m=1}^{N}\hat{y}_{m} \tag{1}\]  
Parameter selection in random forests is an important step in optimizing model performance. The parameter setting method selected in this study was grid search (GridSearchCV function). By providing an array and cross-validation for each parameter, the system's parameter combinations were traversed to find the best hyperparameter settings.  
Random forests can calculate feature importance to assess the contribution of each feature to the model prediction.  
The impurity-based feature importance calculation formula is as follows:  
\[\text{I}(j)=\sum_{m=1}^{M}\sum_{t\in T_{m}}\Delta Gini(j,t) \tag{2}\]  
where \(\Delta Gini(j,t)\) is the reduction in impurity of feature \(j\) at node \(t\) of tree \(T_{m}\).