esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Model Training  
For machine learning, we used the scikit-learn library (ver. 1.2.2) of Python. To create a machine learning dataset, undetectable electrophysiological data were preprocessed as follows. Cases where DML could not be detected were replaced with DML = 20 ms. Cases where SCV could not be detected were replaced with SCV = 0 m/s. In total, 70% of all data were used as training data and 30% were used as test data. The features used for the machine learning model are summarized in Table 1.  
The first machine learning model was created to binary classify electrophysiologic severity at one year postoperatively. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and LightGBM (LGBM) were used as machine learning algorithms. The characteristics of each machine learning model are as follows. Logistic Regression (LR): a linear model for binary classification, estimating probabilities using a logistic function. Support Vector Machine (SVM): a supervised learning model finding the optimal hyperplane to classify data points. Random Forest (RF): an ensemble learning method using multiple decision trees to improve classification and regression performance. LightGBM (LGBM): a gradient boosting framework that uses tree-based learning algorithms for fast, accurate results. In the second experiment, LR, Elastic Net, SVM, RF, and LGBM were used as regression models to predict the change in CTSI-SS and CTSI-FS at one year postoperatively.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Meaning** \\ \hline Age & Age at the time of surgery \\ \hline IsFemale & Gender: female or male \\ \hline preDML & Pre-operative DML \\ \hline preSCV & Pre-operative SCV \\ \hline prestage & Pre-operative electrophysiological severity stage \\ \hline preSS & Pre-operative CTSI-SS score \\ \hline preFS & Pre-operative CTSI-FS score \\ \hline \hline \end{tabular}
\end{table}
Table 1: The features used for the machine learning model.
## 6 Conclusions
For instance, an adversarial attack can disrupt the model training by contaminating training data with malicious data, thereby tricking the detection model into misclassifying malicious web requests as benign. * Support online learning: almost all reviewed studies adopted offline learning in building their detection models. In offline and online learning, the model is trained using batch algorithms (i.e., the cost function is computed over a group of instances). However, while in offline learning, the model is tested and validated using batch algorithms, in online learning, the model is tested using real-time data; thereby, the cost function is re-evaluated over a single data instance at a time. In industry, models trained online are preferred over models trained offline because the latter are generally evaluated against outdated and simplistic datasets. Therefore, it is important that researchers give more consideration to online learning in order to reduce the gap between research and industry in the DL-based web attacks detection area. * Support adaptive incremental learning: according to a recent study conducted in 2020 [97], 42% of attacks are zero-day attacks (i.e., new or unknown attacks), while 58% are based on known vulnerabilities. As with traditional detection systems, Machine Learning-based approaches have a difficult time detecting zero-day attacks because they rely on past and known attacks. Thus, it is fundamental to constantly retrain ML models to account for these attacks. However, retraining models from scratch is time-consuming and computationally intensive. Thus, incremental learning is essential, as it will allow updating trained models as new data is generated. * Generate a corpus for web attacks detection: although DL-based web attacks detection problems are similar to Natural Language Processing (NLP) problems, there exists no corpus yet that can help to develop NLP-like models for web attacks detection problems. * Define a standard and transparent research methodology: it is important to define a transparent research protocol that researchers should follow when proposing a DL-based method for the detection of web attacks. Such a methodology will improve the quality of research works and facilitate their comparison. * Develop a common framework for comparing DL-based web attacks detection models: there is a large diversity in performance measures, datasets, and platforms used in reviewed studies, which makes a comparative analysis between research works difficult if not impossible. Therefore, it is fundamental to provide a standardization of datasets, performance metrics, environments, as well as a transparent research methodology that allows comparing the different approaches and evaluating the models' suitability for real-world web applications.
###### Abstract  
Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO\({}_{2}\) (XCO\({}_{2}\)) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O\({}_{2}\) and CO\({}_{2}\) absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO\({}_{2}\) data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO\({}_{2}\) retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO\({}_{2}\) monitoring satellites will produce at least an order of magnitude more data. For this reason, the Fast atmOspheric traCe gAs retrievaL FOCAL has been developed reducing the computational costs by orders of magnitude by approximating multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer. Here we confront FOCAL for the first time with measured OCO-2 data and protocol the steps undertaken to transform the input data (most importantly, the OCO-2 radiances) into a validated XCO\({}_{2}\) data product. This includes preprocessing, adaptation of the noise model, zero level offset correction, post-filtering, bias correction, comparison with the CAMS (Copernicus Atmosphere Monitoring Service) greenhouse gas flux inversion model, comparison with NASA's operational OCO-2 XCO\({}_{2}\) product, and validation with ground based Total Carbon Column Observing Network (TCCON) data. The systematic temporal and regional differences between FOCAL and the CAMS model have a standard deviation of 1.0 ppm. The standard deviation of the single sounding mismatches amounts to 1.1 ppm which agrees reasonably well with FOCAL's average reported uncertainty of 1.2 ppm. The large scale XCO\({}_{2}\) patterns of FOCAL and NASA's operational OCO-2 product are similar and the most prominent difference is that FOCAL has about three times less soundings due to the inherently poor throughput (11%) of the MODIS (moderate-resolution imaging spectroradiometer) based cloud screening used by FOCAL's preprocessor. The standard deviation of the difference between both products is 1.1 ppm. The validation of one year (2015) of FOCAL XCO\({}_{2}\) data with co-located ground based TCCON observations results in a standard deviations of the site biases of 0.67 ppm (0.78 ppm without bias correction) and an average scatter relative to TCCON of 1.34 ppm (1.60 ppm without bias correction).  
**Keywords:** CO\({}_{2}\); Satellite; remote sensing; OCO-2; validation
## 4 Evaluation  
In contrast to problems where we only try to satisfy a simple objective and where optimality is given by a single minimal solution, in the multi-objective problem, we strive to find the set of non-dominated solutions that satisfy the constraints associated with path selection according to QoS criteria and simultaneously optimize two or more features. For each domain, 30 independent runs of the genetic algorithm were performed with a breakpoint of 500 generations. Figure 7 shows the initial and final populations for each implementation of the MOCell in the three independent domains. The values are normalized to the maximum value found for the scores of each parameter and domain. A significant decrease was observed in the space of better solutions representing higher QoS parameters. As for the path selection of the non-dominated solutions, it is possible to consider a priority order of the functions according to the flow requirements. In this case, we choose the delay first, packet loss second, and bandwidth third, according to the performance of the different algorithms evaluated in Table 4 and their flows, as shown in Figures 8 and 9. The experiment measured data transmission between the end-to-end hosts of data centers H1 and H14. We compared traditional data forwarding based on the shortest path algorithm and the highest available bandwidth. We also considered the path with the lowest delay, the lowest packet loss, a weighting criterion, and our MOCell routing proposal. We used the IPERF [44] application to test network performance with the connection-oriented protocol (TCP) and connectionless protocol (UDP). Our method queries the SDN controller to obtain the network's topology and compute the paths from one end to the other. Table 4 shows the network paths for hosts from source to destination according to their optimization value and all QoS parameters used in this work. Figure 7: Set of non-dominated solutions obtained by the multi-objective cellular genetic algorithm MOCell. (**a**) Domain A, (**b**) Domain B, and (**c**) Domain C.
## 4 Current Status and Identified Needs
### Level 1 TOA Radiometry and Geometry  
The need for blending optical data from multiple sources directly translates into the need for ensuring that the two datasets are consistent in terms of geometric and radiometric calibration. Any geometric misregistration will induce a geometric systematic error whenmixed-source temporal series have to be used for regional applications. Likewise, inter-sensor radiometric inconsistencies will translate into biases in the derived harmonised multi-mission data record. While the root cause of the radiometric or geometric errors can be considered systematic, their combined impact into the projected and calibrated radiances or reflectances (Level 1 product) have a more complex nature, notably owing to the interpolation and regridding process.  
From the ESA's perspective, ensuring radiometric and geometric consistency at Level 1 across its fleet of optical land sensors is of the utmost importance, and it is considered the first fundamental step for tackling the issue of interoperability. This approach is shared among the other Space Agencies, and it is the main principle followed in the frame of the CEOS-LSI-VC and GSICS working groups.  
The issue of inter-sensor radiometric consistency has been addressed for several decades, and there is now a good level of maturity in terms of the required pre- and post-launch Cal/Val activities. On the other hand, we have not yet reached the stringent accuracy requirements for some applications, notably for climate applications, and challenges remain in properly characterising geometric mismatch errors for cross-sensor consistency analysis and when propagating the uncertainty at Level 1 into higher-level spatially-aggregated climate datasets [55].  
Within this section, we present the current status and highlight the remaining needs in terms of infrastructure and methodologies for the validation of Level 1 TOA products. The needs identified in the text and the derived recommendations are highlighted in Table 2 at the end of the section.
# A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment
## 5 Conclusions  
In this paper, we proposed an efficient cooperative UAV deployment model in the UAV-assisted sensor network. A multi-UAV coverage deployment and transmission mechanism was established, and the coverage utility and corresponding energy consumptions under the network structure were studied based on cooperation. The system model was transformed into a coalition formation game (CFG). The CFG with Pareto order was proved to have at least a stable coalition partition. Then, coalition selection and a position deployment algorithm based on Pareto order (CSPDA-PO) were designed to perform coalition selection. Finally, simulations were performed to validate the effectiveness of our proposed approach, indicating that the proposed model can describe UAVs' cooperative behavior well and has better performance compared with other approaches in UAV networks.  
**Author Contributions:** Formal Analysis, Y.Z. and D.L.; Validation, J.C., H.J., Y.Z. and D.L.; Visualization, J.C., Q.G. and H.J.; Writing--Original Draft, L.R.; Writing--Review and Editing, L.R., Y.Z. and D.L.  
Figure 6: The performance convergence graph of the proposed model (\(\rho=1\times 10^{-5}\)). (**a**) Diagram of UAV deployment and coalition formations; (**b**) Convergence performance of overall coverage reward; (**c**) Convergence performance of overall energy consumption; (**d**) Convergence performance of overall coverage utility.  
Funding: This work was supported by the National Natural Science Foundation of China under Grant No. 61771488, No. 61801492, in part by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034, and the Guang Xi Universities Key Laboratory Fund of Embedded Technology and Intelligent System (Guilin University of Technology).  
The authors declare no conflict of interest.
###### Abstract  
Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach involves adaptive feature extraction using principal component analysis (PCA) in the Euclidean space to capture relevant signal characteristics and improve classification performance. Covariance matrices are derived from the extracted features and projected onto the Riemannian manifold. Emotion classification is performed using the minimum distance to Riemannian mean (MDRM) classifier. The effectiveness of the method was evaluated through experiments on four datasets, DEAP, DREAM, MAHNOB, and SEED, demonstrating its generalizability and consistent accuracy improvement across different scenarios. The classification accuracy and robustness were compared with several state-of-the-art classification methods, which supports the validity and efficacy of using Riemannian geometry for enhancing the accuracy of EEG-based emotion classification.  
Keywords:brain-computer interface; Riemannian geometry; emotion recognition; PCA; electroencephalogram (EEG); feature extraction; dimensionality reduction; Frechet mean +
Footnote †: journal: Applied
## 4 Results and Discussion
### Unsteady Simulation Results
#### 4.2.3 Flow Field Modal Decomposition
Figure 19: Normalized eigenvalues of the first 300 POD modes. Figure 20: Spectrum results of POD eigenvectors: (**a**) TVD; (**b**) SVD. The first second-order POD mode identified large-scale flow structures associated with the diffuser stall cells and BPF. Due to the pseudoperiodic property of POD modes, similar flow structures with similar frequency and energy are observed in the first second-order POD mode [33]. For TVD, the vortex structure formed by stall cells and impeller-diffuser interaction almost blocks two passages of the diffuser, which prevents the fluid from flowing into the passages normally and increases the flow loss in the diffuser. However, a similar diffuser passage blockage phenomenon is not found in SVD, which enables the fluid from the impeller exit to flow into the diffuser normally, thus broadening the stability range of the compressor. The third- and fourth-order POD modes also contributed to understanding the other flow patterns different from the first second-order POD mode. Compared with the first second-order POD mode, the intensity of the vortex structures in the diffuser decreases significantly in the third- and fourth-order POD modes. For TVD, the stall cells still affect the internal flow of the diffuser, which leads to the blockage of some flow passages of the diffuser. However, no similar blockage phenomenon is found in SVD, which further indicates that SVD can improve the stability and performance of the compressor stage. Based on the POD analysis, the stall frequency (86.645 Hz) and the blade passing frequency (BPF) have a significant influence on the flow field near the shroud of the diffuser. The pressure field for DMD modes of these two frequencies at 0.95 span is shown in Figure 22. Similar to the POD method, the DMD method is time-dependent, which describes the evolution characteristics of the vortex structures at a specific frequency during a period [29]. It can be seen from Figure 22a that the pressure fluctuation caused by the stall cells mainly appears in the diffuser passages, especially in the TVD, where the large-scale vortex structures block part of the diffuser passages. However, the vortex structure caused by the stall cell is weak but still blocks the flow passage of the diffuser, causing the diffuser to stall. The vortex structures caused by the BPF (impeller-diffuser interaction), are mainly distributed at the leading edge of the diffuser vane and present positive and negative alternation (Figure 22b). From an overall point of view, compared with TVD, the intensity of the vortex structures in the diffuser passages in SVD is significantly weakened at two frequencies. This is also the main reason why SVD can improve the stability and performance of the compressor stage compared to TVD. Figure 21: The spatial patterns for POD modes of pressure fields at 0.95 span: (**a**) zeroth-order POD mode (averaging flow field); (**b**) 1 st-order POD mode; (**c**) 2 nd-order POD mode; (**d**) 3 rd-order POD mode; (**e**) 4 th-order POD mode.
## 4 Experimental Study
### Spectrum Analysis  
The spectrum analysis of transmission error under the optimal measurement speed was carried out based on the spatial spectrum analysis method [20]. In order to more accurately obtain the error source, the low-pass filtering of the original transmission error curve was carried out. The spectrum of the filtered transmission error is shown in Figure 9, where \(f\) and \(f_{0}\), respectively, represent the frequency of transmission error and the frequency of output shaft; \(f/f_{0}\) represents the number of variation times of transmission error within one rotation of the output shaft [21]. Table 4 shows the harmonic components. The spectral component contains a constant term. The constant term is mainly caused by the inherent clearance of the RV reducer. In the transmission error measurement of the RV reducer, the pin gear shell was fixed and the planetary frame was the output end. Therefore, in the range of one rotation of the output shaft, the planetary frame rotated in a circle and the harmonic component 1 was mainly caused by the machining error of the planetary frame. The number of  
Figure 8: Variations of the peak-to-peak value of transmission errors with speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speed** & **Peak-to-Peak** & **Speed** & **Peak-to-Peak** & **Speed** & **Peak-to-Peak** \\
**(rad/s)** & **Value (Armin)** & **(rad/s)** & **Value (Armin)** & **(rad/s)** & **Value (Armin)** \\ \hline
0.4187 & 0.6119 & 2.4483 & 0.5058 & 10.4667 & 0.5318 \\
0.8373 & 0.6068 & 3.1400 & 0.5141 & 12.5600 & 0.5391 \\
1.0467 & 0.5631 & 4.1867 & 0.5163 & 17.4444 & 0.5409 \\
1.5700 & 0.5541 & 5.2333 & 0.5165 & 20.9333 & 0.5468 \\
2.0933 & 0.5283 & 7.3267 & 0.5246 & 26.1667 & 0.6076 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peak-to-peak values of transmission errors under different speeds.  
Figure 7: Transmission error curve (2.4483 rad/s).  
cycloidal gear teeth of the measured RV reducer was 39 and the harmonic components 39 and 78 were mainly caused by the machining error of the cycloidal gear. The number of pin gear of the measured RV reducer was 40, and the harmonic components 40, 80, 160, and 200 were mainly caused by the machining error of a pin gear. The input shaft rotated 121 cycles in the range of one rotation of the output shaft, and the harmonic component 121 was mainly caused by the machining error of input shaft. The harmonic components indicated that the machining and installation errors of planetary frame, cycloid gear, pin gear, and input shaft of the RV reducer had great influences on transmission errors and it should be considered in the design and manufacturing process.
## 5 Simulation and Discussion
### ABS Control System Simulation
#### 5.2.3 Variable Pavement Braking  
To verify the adaptability of the control strategies, a complex variable surface condition was selected, transitioning from dry asphalt to a snowy road. Braking began on dry asphalt, and after 1 s of braking, the road surface switched to snow. Figure 9 shows the braking performance of the three ABS control strategies, ADRC, SMC, and PID, under variable road conditions. From Figure 9a, it can be observed that the ADRC strategy provides a shorter braking distance, thereby enhancing safety. Figure 9b,c indicates that on dry asphalt, both ADRC and SMC achieve similar effects in terms of reducing speed. However, when the road surface changes, PID control exhibits overshooting, while ADRC adjusts more quickly than SMC.  
Figure 8: Braking performance on snowy pavement. (**a**) Braking distance. (**b**) Vehicle speed. (**c**) Wheel speed. (**d**) Slip.  
Figure 9d demonstrates that in terms of slip ratio control, during sudden changes in road conditions, the response times of SMC and PID both lag behind those of ADRC. Moreover, ADRC reaches a steady state and outputs the optimal slip ratio faster than the other two methods. This indicates that PID and SMC have poorer adaptability to variable road conditions, whereas ADRC shows stronger adaptability and better robustness to changes in road surfaces. Considering factors such as braking time, braking distance, and control response time, the ADRC strategy performs better compared to PID and SMC. It significantly improves vehicle stability and safety during braking.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 4 Node Classification
### Classification  
The classification method proposed in this work uses a binary classifier that categorizes a mobile node disconnected from a WSN as either a failed node or a left node. This classifier uses four values. These are two trends extracted from \(Q\) and \(R\) and two thresholds \(T_{nc}\) and \(T_{r}\) used to determine whether a disconnected mobile node sends its last value from either inside or outside of a monitoring region. Therefore, the classification rule to distinguish between a failed node and a left node consists of four conditions related to the trends and the thresholds. The classification rule is given as:  
\[H=\left\{\begin{array}{rl}\text{Left Node,}&\text{if $S(Q)<0$ and $Q_{w}<T_{nc}$ and $S(R)>0$ and $R_{w}>T_{r}$}\\ \text{Failed Node,}&otherwise\end{array}\right. \tag{11}\]  
where \(S\) indicates the slope and \(Q_{w}\) and \(R_{w}\) indicate the last neighbor count and the last ratio of the boundary nodes, respectively, that a disconnected mobile node transmitted.