esa-sceva/satcom-chunk-collection huggingface dataset:
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 2 Related Work
### Adaptive Ensemble Models and Incremental Learning  
Adaptive ensemble models and incremental (continual) learning techniques have been increasingly explored to address concept drift in data streams. These approaches aim to maintain stable performance over time by enabling selective model updates without full retraining.  
In adaptive ensembles, base detectors are designed to evolve over time or to be replaced selectively in response to distributional changes. Learn++.NSE [6] and DynamicWeighted Majority (DWM) [29] are early examples of concept drift-aware ensemble methods. They adapt to drift by weighting or replacing classifiers based on recent performance. However, they rely on labeled data and are limited to supervised learning scenarios.  
Recent research has extended ensemble adaptivity to streaming environments. DeepStreamEnsemble [7] combines deep feature extraction with Hoeffding Tree ensembles to detect and respond to drift. While it supports online adaptation, it requires labeled data after each drift event and is not designed for anomaly detection. Recurrent Adaptive Classifier Ensemble (RACE) [8] addresses recurring drift by maintaining a pool of historical classifiers and reactivating them as needed. This approach also operates under a supervised setting and retrains models rather than updating them incrementally. Ensemble of Drift Feature Subspaces (EDFS) [30] targets anomaly detection in streaming settings using an ensemble-based strategy with entropy-guided pruning. Although the method emphasizes unsupervised detection, it still assumes access to occasional labels after drift for ensemble updating.  
In summary, while these methods demonstrate the value of ensemble models for adapting to concept drift, they are limited in two key aspects. Most require supervision during adaptation, and few support incremental learning. The proposed ADDAEIL framework addresses these limitations by integrating fully unsupervised drift detection and incremental ensemble updates for streaming anomaly detection.
### Technical Motivation Based on MAE
#### 1.1.3 Why Not a Standard Autoencoder or Other Alternatives?  
Standard Autoencoders  
Typical autoencoders assume access to all (or nearly all) input features, then compress them into a latent dimension.
* They do not physically remove data but only map the full input to a lower-dimensional latent space. Hence, they do not yield a genuine time-domain compression that can be exploited for increasing channel capacity.
* By contrast, an MAE literally masks out entire patches, demanding that the decoder reconstruct them from only partial information.  
CNN or FNN-based Approaches  
CNNs can handle spatially or temporally correlated data but typically focus on local receptive fields; they struggle to capture long-range dependencies if large chunks of data are missing.
* FNNs fully connect all inputs but are not naturally suited to partial-observation \"inpainting\" tasks.
* MAEs (particularly those using self-attention) adapt more easily to scenarios with large missing portions by leveraging global context in an end-to-end learned fashion.  
End-to-End Deep Learning without Masking  
While end-to-end methods can improve channel equalization, they still transmit the complete waveform.  
Consequently, they cannot provide the effective capacity gains we achieve by physically not sending up to 75-85% of the samples.
## 4 An Application of the Mellin Transform  
Given a random variable, \(x\in R^{+}\), the Mellin transform, \(M(s)\), of a pdf, \(f(x)\), can be defined as:  
\[M\{f(x);s\}=M(s)=\int_{0}^{\infty}f(x)x^{s-1}dx \tag{12}\]  
Let \(h\) be a measurable function on \(\mathbb{R}\) into \(\mathbb{R}\) and \(Y=h(x)\) is a random variable. Then, some properties of the Mellin transform can be described, as shown in Table 1. For example, if \(Y=ax\), then the scaling property can be expressed as:  
\[M\{f(ax);s\}=\int_{0}^{\infty}f(ax)x^{s-1}dx=a^{-s}\int_{0}^{\infty}f(ax)(ax)^ {s-1}=a^{-s}M(s) \tag{13}\]Given a continuous non-negative random variable, \(X\), the \(n\)th moment of \(X\) is denoted by \(E(X^{n})\) and is defined as:  
\[E(X^{n})=\int_{0}^{\infty}x^{n}f(x)dx \tag{14}\]  
For \(n=1\), the mean of \(X\) can be expressed as:  
\[E(X)=\int_{0}^{\infty}xf(x)dx \tag{15}\]  
and the variance of \(X\) can be calculated by:  
\[\sigma_{x}^{2}=E(X^{2})-\left[E(X)\right]^{2} \tag{16}\]  
Since the relation between the \(n\)th moment and the Mellin transform of \(X\) can be linked by:  
\[E(X^{n})=\int_{0}^{\infty}x^{(n+1)-1}f(x)dx=M\{f(x);n+1\} \tag{17}\]  
then the mean and the variance of \(X\) can be calculated by:  
\[E(X)=M\{f(x);2\} \tag{18}\]  
\[\sigma_{x}^{2}=M\{f(x);3\}-\{M\{f(x);2\}\}^{2} \tag{19}\]  
According to Equations (18) and (19), we can quickly calculate the mean and the spread in any distribution. In practice, the uniform, the triangular, and the trapezoidal distribution are usually used, and their Mellin transforms are summarized as shown in Table 2. More Mellin transforms in various probabilistic density functions can be found in [19].  
Based on Table 2, the mean and spread values can be efficiently derived by calculating \(M(2)\) and \(M(3)\). Then, we can rank and defuzzify imprecise data using Equation (7). The following section uses four numerical examples to show the detailed procedures of the proposed method.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 3 Results
### Analysis of Spatial Variables  
We simulated future urban growth using spatial variables (slope map, roads with a 50 m buffer, water bodies and existing built-up areas in 2019). We assigned the map with a slope percentage of <25%, a value of 1, and 0 represented an unsuitably steep slope (Figure 4a). For the roads and water bodies maps, existing ones were rated 0 (unsuitable) and 1 for areas without roads and water bodies, respectively (Figure 4b,c). The existing built-up areas in 2019 were given a value of 0 (unsuitable), and the undeveloped areas were given a value of 1 (suitable), as shown in Figure 4d.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 3 Challenge: Data Quality
### Semi-Supervised Learning
#### 3.2.1 Self-Teaching  
The most intuitive SSL method is self-teaching. The model is initially trained using labeled data and then used to label the unlabeled samples through prediction instead of conventional manual labeling. Such labels are often called pseudo labels. In the following training epochs, the models are trained using labeled and pseudo-labeled data altogetheras in normal supervised learning. The pseudo labels have to be updated with the newest model after each epoch until the loss function converges [69].  
[PERSON] et al. train a CNN with self-teaching strategies for processing ultrasound signals to detect fouling in industrial equipment in [70]. Their results show that training with semi-supervised learning can reduce 15% of the error rate compared with using labeled data only. Self-teaching methods are easy to implement, but the errors can propagate across the epochs and self-reinforce if the model accuracy is initially low. Moreover, it is not scalable when the unlabeled data are greatly dominant.
## 3 Results and Discussion
### Overall Results on Emotion Perception
#### 3.1.2 Out-Group Perception Results (Asians)  
The Asian listeners perceived _neutral_ and _sadness_ at the highest recognition rates. They mainly perceived _anger_ as _neutral_, _happiness_ as _anger or surprise_, and _neutral_ as _sadness or happiness_. Although the Asians showed almost similar tendencies in perceiving the different emotions and confusion (refer to Table 1), significant differences between the ethnic groups were noted:  
* In general, _anger_ is less acceptable in Asian cultures [36]; this is reflected by the perception rates of _anger_ in the present experiment where Asians significantly perceived _anger_ less than the Spanish. The lowest perception rate of _anger_ was scored by the Japanese listeners, which is statistically significantly less than the Chinese (\(p\) = 0.002) and Malaysians (at \(p\) < 0.001). This finding is in line with findings of previous studies, where Japanese typically tend to avoid negative outcomes and thus engage in fewer negative emotions [37; 38]. Evidence suggests that generally Asians suppress their negative emotions when perceiving out-group emotions, but not positive emotions such as _happiness_[39]. In this study, it was noted that all Asian groups perceived _happiness_ higher than the Spanish listeners.
* Noticeably, Malaysians perceived Spanish _surprise_ and _anger_ more accurately than the other Asian ethnics. They perceived _surprise_ statistically significantly higher than Japanese (at \(p\) < 0.05) and Chinese (at \(p\) = 0.001), whereas _anger_ was perceived statistically significantly higher than the Japanese (\(p\) < 0.001) and Vietnamese (\(p\) < 0.005). Studies by [PERSON] et al. [40] showed that Malaysians tend to be  
Figure 2: Bar chart representing percentage of Actual Emotion Perceived by Spanish, Japanese, Chinese, Vietnamese and Malaysian Subjects (based on the listening test using the SEV Corpus).  
more expressive in showing high-arousal emotions such as _surprise_ and _anger_ compared to other Asian ethnicities.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 2 Methods
### HRU-Net
#### 2.3.2 Realization Principle  
The dual multi-tiered feature integration frameworks (UMR module and MRF module) delineated in this study employ convolution, pooling, activation, and normalization techniques to diminish the primary attributes of the image while utilizing upsampling to  
Figure 1: This is the UMR module structure diagram. Different colored cubes represent feature maps of different sizes. The numbers 1, 2, and 3 respectively signify feature map input connections coming from the previous layer, the same layer, and the next layer in the network. Squares represent feature maps.  
Figure 2: This is the MRF module structure diagram. Different colored cubes represent feature maps of different sizes. The numbers 1 and 2 respectively signify the feature map input connections coming from the same layer and the next layer in the network. Squares represent feature maps.  
augment resolution and intricate specifics. These frameworks assimilate data from diverse resolutions, merging the granular high-level semantic elements with delicate minutiae to generate high-resolution feature mappings that concurrently contemplate both categories of attributes.
## 5 Experiments and Results
### Validation  
The proposed technique is validated for process control using two complementary approaches. First, the Pareto optimality of the resulting solutions is analyzed to ensure their efficiency in addressing multiple objectives. Second, the solutions are assessed using process capability metrics to evaluate their practical effectiveness within the RL-based optimization framework.
## 1 Introduction
This method has been widely used in various fields as a benchmark for subsequent studies [9; 31; 45]. Firstly, source location and the additional variable are considered independent variables to conduct the estimation. Secondly, the estimation is developed by considering the relationship between the additional variable and the source location. However, the covariance matrix of measurement errors used in this location process is difficult to accurately estimate, which causes performance degradation at the source location [9]. Unlike the method of weight least squares, [PERSON] et al. [46] recently proposed comprehensive closed-form solutions using a set of sensor networks and the logistic probability density function, so as to improve the location accuracy. However, location errors of the closed-form solution are still large, because only six sensors are considered in each calculation process, and the coefficient matrix of the linear equations is close to ill-conditioned. In addition, since many subsets of six sensors exist, it requires enormous computing power; thus, it has a poor performance in real-time implementation [47]. Aiming at improving the error-tolerance and real-time implementation of the AE source location, this paper proposes the preconditioned closed-form solution based on weight estimation (PCFWE) to seek the optimal source coordinates by filtering outliers and reducing the ill condition of linear equations. PCFWE is also compared with the TSWLS [41] and linear least squares (LLS) methods. The effectiveness and accuracy of the proposed PCFWE are verified by the pencil-lead break experiment and simulating tests. In Section 2, the ordinary closed-form method that fairly treats all of the measurements is stated. In Section 3, the theory of the proposed method is formulated mathematically. In Section 4, the location results of the pencil-lead break experiment are reported. Finally, outlier tolerance and the velocity sensibility of the proposed method are discussed and concluded in Section 5.  
location error with a minor disturbance. Therefore, the preconditioning of linear equations is adopted to weaken the ill condition and further improve the location accuracy.
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 3 Materials and Methods
### Dimensionality Reduction  
Dimensionality reduction helps to simplify high-dimensional ECG-derived features into a compact representation, facilitating classification and visualization. This also helps to reduce noise, eliminate redundancy, and mitigate overfitting [9].
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 5 Performance
### Expected Uncertainty Reductions on Surface Emission  
The spatially distributed scenarios of random and systematic errors described above for MERLIN have been integrated into an atmospheric inversion simulator that converts uncertainties in XCH4 into uncertainties on surface emissions using an inversion system (based on [92]). This simulator has the originality to carry both random and systematic error ([PERSON], pers. Comm.). Although relatively marginal at sounding level (\(\pm\)3.7 ppb on global scale, value from the MERLIN system specification requirement), the systematic errors have a larger influence than the random errors on the regional CH4 emission estimates because they are not reduced by data averaging accounting for both types of errors. Expected uncertainty reductions on CH4 emissions are found on average to be 59% for the continental tropics (30\({}^{\circ}\)S-30\({}^{\circ}\)N), 84% for continental mid-latitudes (30\({}^{\circ}\)N-50\({}^{\circ}\)N), and 53% for continental high latitudes (above 50\({}^{\circ}\)N, Figure 7). The largest uncertainty reductions are achieved for temperate regions. Uncertainty reductions obtained for the aggregated boreal regions is 53% on average, indicating that MERLIN still brings constraints for high northern latitudes, contrary to most passive SWIR missions. For tropical regions, the score for uncertainty reduction is mostly influenced by desert regions and the choice to distribute systematic errors partly on albedo to represent potential driver of nonlinearity effect of the MERLIN detector. More details and regional analyses will be provided in follow-up papers.  
Unsurprisingly, once the instrument is flying, systematic errors are expected to be the main limiting factor to use XCH4 to reduce uncertainties on CH4 emissions. Nevertheless, the MERLIN error budget presented here is considered an important step towards anticipating the major causes and magnitudes of MERLIN systematic errors. Using validation measurements, it should be possible to correct some of these systematic errors, thus reducing their impact on the error budget. Most surely, some other unexpected errors will appear or expected ones will be different in their space-time distribution or magnitude. Although the ambitious systematic error targeted by MERLIN calls for caution before the mission is operating, these first results on the end-to-end error budget of MERLINare very encouraging, highlighting the ability of MERLIN to provide useful information for the global CH\({}_{4}\) cycle.
## 3 Results and Discussion
### Principal Component Analysis Results
Finally, principal component analysis was conducted using 67 sets of data to distinguish between damage and materials as shown in Figure 9:7 Based on the main component analysis of the total 67 sets of data, it was calculated that the vector with the greatest variance was the moment of real value (96.70%), and the second vector was the moment of imaginary value (3.29%). The distribution of cristobalite was the same. Furthermore, data from the intact alumina material were found to be distributed in the positive direction of PC1 whereas in the cristobalite material there was a greater variance in PC2. The PCA results based on the frequency response data using at least two main component vectors or three main component vectors showed that it was possible to make a distinction between intact and damaged cristobalite materials and between cristobalite and alumina materials.
# Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting
## 2 Materials and Methods
### Methodology
#### 2.2.1 Data Preprocessing  
In order to train a deep learning model that can predict several time-steps, a preprocessing stage is needed to transform the original time series data. First, we perform min-max normalisation to the entire sequence to scale the values between 0 and 1, which helps to improve the convergence of deep networks. Secondly, we transform the sequence into instances that can be used to feed the  
Figure 2: Line plots illustrating the electric vehicle power consumption data at different scales.  
network. There exist several strategies to deal with multistep forecasting problems [32]: the recursive strategy, which performs one-step predictions and feeds the result as the last input for the next prediction; the direct strategy, which builds one model for each time step; and the multi-output approach, which outputs the complete forecasting horizon vector using just one model. As suggested in recent forecasting studies that use neural networks [33; 34], in this work, we adopt the MIMO strategy (Multi-Input Multi-Output) which belongs to the last category. Instead of forecasting each time-step independently, the MIMO approach can model the dependencies between the predicted values since it outputs the complete forecasting window. Furthermore, this strategy avoids the accumulated errors over predictions that appear in the recursive strategy.  
Following this approach, a moving window scheme is used to create the input-output pairs that will be fed to the neural network. All deep learning models used in this study accept a fixed-length window as input and have an output dense layer with as many neurons as the forecasting horizon defined for each problem (24 for electricity demand and 48 for electric vehicle demand). Figure 3 illustrates the process of applying the moving window over the complete time series. As can be seen, the window slides and obtains an input-output instance at each position. While the output window size is defined by the problem, the input window size has to be decided. The optimal value can be different depending on the data, the designed model, and the forecasting horizon. In our study, we have experimented with three different sizes for the input window of each problem. The values have been carefully selected, considering the characteristics and seasonality of the datasets. For the electricity demand, we evaluate using 144, 168, and 268 time-steps as input window (which corresponds to 24, 28, and 48 h, respectively). For the power demand of electric vehicles, we consider 168, 336, and 672 time-steps as input window (which corresponds to 7, 14, and 28 days, respectively).  
Figure 3: Moving window procedure that obtains the input–output instances. In this example, the input and output windows have lengths of 7 and 3, respectively.
# Shock Reduction through Opposing Jets--Aerodynamic Performance and Flight Stability Perspectives
## 5 Conclusions  
The current numerical research aimed to understand the phenomenon of opposing jet for drag reduction on a blunt leading edge airfoil (however, discussion related to temperature, density and acoustic were not considered for the analysis of opposing jet). After the numerical validation of baseline experimental cases, a jet is introduced on conventional airfoil. The computational investigation shows that by introducing opposing jet the overall drag of an airfoil reduces up to 70%. However, the performance of supersonic jet is extremely affected by varying operating parameters as follows:  
* The pressure ratio (\(PR\)) is the key parameter that affects the counter flow jet. The LPM exists in a smaller range of pressure ratios (2.4); it produces a long shock standoff distance. As the pressure ratio is beyond the threshold value, the counter flow jet transmits its flow variation from LPM to SPM (\(PR=3\)).
* Increased in angles of attack influenced the opposing jet efficiency. Beyond \(\alpha_{\infty}=6\), the jet destroyed and the drag at the leading edge starts increasing again.  
Figure 18: Variation of free stream pressure distribution by changing the airfoil angles of attack dynamically (\(\alpha=0^{\circ}\) to \(8^{\circ}\)) using \(UDF\), at constant amplitude of airfoil (\(\alpha_{A}=4^{\circ}\)) and flow mean angle of attack (\(\alpha=4^{\circ}\)).  
* Free stream Mach is the major influencing parameter. The SPM mode is achieved at \(M=2\). With further increase in Mach (\(M=3\)) the jet becomes highly under expanded and the shape of the Mach disc changes into a convex form.
* The static and dynamic stability of airfoil in supersonic flow enhances by the implementation of jet. At higher free stream flow angles (\(\alpha_{\infty}=6\)) the jet does not work properly as it tilts in the free stream flow direction. So, the stability of an airfoil reduced.  
Conceptualization, S.R., F.N. and A.M.; S.R. and F.N. performed the investigation and numerical analysis; Supervision, A.M.; Writing--Original Draft, S.R., F.N.; Writing--Review Editing, A.M., R.R. and S.S. All authors have read and agree to the published version of the manuscript.  
There were no funding sources for this research.  
We acknowledge, [PERSON] for providing us the basic theme of opposing jet technique and RCMS for high performance computing facility.  
The authors declare no conflict of interest.