esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Datasets and Data Treatment  
Three datasets were used in this study, based on data from a single match of the 2022 FIFA World Cup. The first was a dataset created by applying the previously presented custom algorithm to synchronize the positional and event data, referred to as the optimize synchronization dataset (OSD). After being exported, the raw positional data from the match was processed in Python 3.8 and divided into two datasets: one for players and one for the ball. Using the ball dataset, the x- and y-coordinates on the field were used to compute the distance the ball travelled between each frame. With this distance, and the known time intervals between frames, the ball's speed was then calculated for each frame. Since the positional and event data were recorded using different time units, a conversion was applied to unify the time scale. Specifically, each event's timestamp (recorded in milliseconds) was adjusted to match the frame-based structure of the positional data (25 Hz, or 40 ms per frame).  
The second dataset was a synchronization of the positional and event data to the common time unit, referred to as raw synchronization dataset (RSD). This dataset consisted of a basic integration of the events and the positional data from the match, in which both data types were converted to the same temporal resolution, as presented previously.  
Finally, a third dataset, referred to as the manual notational dataset (MND), was used as \"gold standard\". This dataset was constructed by manually annotating interactions between players and the ball. The annotation was conducted by identifying all instances in which a player received or passed the ball, to precisely determine the moment at which each pass occurred. These actions were registered using an analysis software--LongoMatch Open-Source version 1.3.2--considering the video footage from the match with tactical camera [29]. After annotation, the recorded events were exported as a time series and adjusted to match the temporal resolution of the other datasets, ensuring comparability across all data sources. Manual annotations were conducted by a single expert analyst. While this ensured consistency, the absence of inter-rater validation represents a limitation. Future studies should incorporate multiple annotators and assess reliability to reduce subjectivity.
## 5 Conclusions  
A hybrid demodulation approach has been proposed for detecting defects in a multistage gearbox using variational mode decomposition and modulation intensity distribution. Variational mode decomposition is used as a pre-processing technique prior to modulation intensity distribution. The reduced frequency aliasing effect of variational mode decomposition is evaluated, which demonstrated that variational mode decomposition is very useful for separating noise and harmonic components. Moreover, the influences of spectral coherence density and spectral correlation density on the modulation intensity distribution have been also investigated. The effectiveness and noise robustness of the proposed method have been well-verified using a simulated signal compared with the empirical mode decomposition algorithm associated with modulation intensity distribution. The merits of the presented technique are as follows: (i) variational mode decomposition can effectively separate noise and harmonic components in the original signal due to its good reduced frequency aliasing effects; (ii) modulation intensity distribution can numerically identify the hidden cyclostationary signatures in the bi-frequency domain and thus can well-differentiate different gear defects; and (iii) the pigmentation visually illustrated in modulation intensity distribution plots can be adopted to quantitatively detect gear defects. The effectiveness of the proposed hybrid demodulation method has been well-verified with simulated and four experimental tests corresponding to different gear health conditions. The results show that the performance of the presented method is also better than that of empirical mode decomposition combined with modulation intensity distribution.  
Figure 23: The decomposed BLIMFs using VMD (**a**) and IMFs using EMD (**b**) from the signal of the gear with a broken tooth.  
Besides this, a new technique that has been developed to automatically select the optimal components achieved by variational mode decomposition will be reported in future work. Model-based techniques are another important method for fault diagnosis. A comparison between the model-based technique and the proposed signal-based technique will be also considered in the future.  
[PERSON], [PERSON], [PERSON] and [PERSON] proposed the idea and designed the experiments. [PERSON] carried out the experiments and analyzed the data. [PERSON] and [PERSON] drafted the paper. [PERSON] and [PERSON] revised the paper.  
The major part of the study was supported by the project of the National Natural Science Foundation of China (51475098 and 61463010) and the Guangxi Natural Science Foundation (2016 GXNSFFA380008). The High Level Innovation Team of Colleges and Universities of Guangxi and the Excellent Scholar Program are gratefully acknowledged. This work is also partially supported by the Recruitment Hundred-Talent Program of Global Experts and the Innovation Project of Guangxi Graduate Education (YCSW2017136).  
The authors declare no conflict of interest.
# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 2 Materials and Methods
### The Proposed Method
#### 2.2.2 The Relevant Definitions of Graph  
In this section, we will introduce the relevant definitions and symbols related to the graph. Suppose we have a graph \(G=(V,E,A)\), where \(V\) denotes the set of nodes with \(|V|=N\); \(E\) is the set of edges; \(A\in\mathbb{R}^{N\times N}\) is the adjacency matrix of the graph. Based on the adjacency matrix, we can obtain the degree matrix \(D\) with \(D_{ii}=\sum_{j=1}^{N}A_{ij}\). Additionally, we  
Figure 2: The detailed structure of AFGN. Few shot learning needs a large number of episodic tasks for model training. Here, we take the training process of a single episodic task \(\tau\) as an example. The episodic task \(\tau\) is designed as 2-way 1-shot, where the support set \(S\) includes 2 categories, each with a single sample, while the query set \(Q\) consists just one sample. The task \(\tau\) is feed into ResNet to obtain image feature embeddings, the initial node features and edge features are obtained through BDM module, and the graph is constructed. After L-layers of graph network, the node features and edge features of the graph are updated. We predict the category of the node in the query set based on the output of the graph network. Subsequently, we calculate the cross-entropy loss, and parameters in the network are updated through backpropagation.  
can derive the Laplacian matrix \(L=D-A\) and the symmetrically normalized Laplacian matrix \(L=\mathbb{I}-D^{-1/2}AD^{-1/2}\), where \(\mathbb{I}\) represents the identity matrix.
## 3 Results
### Monte Carlo vs. SVOX with the VSV Kernel for the Lung Tissue  
The SVOX method, using the lung VSV kernel, was employed to calculate the \(AD\) to the lungs after cropping the activity map to include only the lung region. Cropping was necessary because applying the SVOX method to the entire activity map would otherwise result in anomalous dosimetric contributions from the liver (as discussed in Section 3.2). The results obtained with the new kernel (referred to as Lung_296), those globally rescaled to the mean density of the Reference phantom's lungs (referred to as Lung_221), and those locally rescaled for the tissue heterogeneities (referred to as Lung_L, according to Equation (3) and using the ICRU lung density as uniform tissue density), are shown on the y-axis of the correlation plot in Figure 5, similar to the results presented in Figure 4. For comparison, the SVOX_ST data are also included.  
Even when using the new VSV kernel for lung, all the evaluations severely underestimate the results obtained from \(MC\) simulations. The \(RD\) computed according to Equation (6) and presented in Table 4 shows values around \(-62\%\) for Lung_296, \(-50\%\) for Lung_221, and \(-36\%\) for Lung_L. The \(AD\) for each computational approach is reported in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Lung Shunt** & **MIRD (\%)** & **LED (\%)** & **SVOX\_ST (\%)** & **SVOX\_L (\%)** \\ \hline
10\% & \(-42.4\) & \(-27.4\) & \(-87.8\) & \(-28.5\) \\
20\% & \(-40.7\) & \(-26.7\) & \(-87.8\) & \(-28.2\) \\
30\% & \(-40.3\) & \(-26.5\) & \(-87.8\) & \(-28.1\) \\
40\% & \(-40.2\) & \(-26.4\) & \(-87.9\) & \(-28.1\) \\ \hline \end{tabular}
\end{table}
Table 2: Lungs’ \(\overline{AD}\) relative difference for the classical approaches with respect to the \(MC\) evaluations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Lung Shunt** & **MC** & **MIRD** & **LED** & **SVOX\_ST** & **SVOX\_L** \\ \hline
10\% & \(11.9\) & \(6.8\) & \(8.6\) & \(1.4\) & \(8.5\) \\
20\% & \(23.0\) & \(13.6\) & \(16.8\) & \(2.8\) & \(16.5\) \\
30\% & \(34.3\) & \(20.5\) & \(25.2\) & \(4.2\) & \(24.6\) \\
40\% & \(45.6\) & \(27.3\) & \(33.6\) & \(5.5\) & \(32.8\) \\ \hline \end{tabular}
\end{table}
Table 3: Lungs’ \(\overline{AD}\) for the \(MC\) simulation and the classical approaches, as visually reported in Figure 4. All the results are reported in Gy per GBq of administered activity.
# Personalized Scholar Recommendation Based on Multi-Dimensional Features
## 5 Evaluation
### Comparative Approaches  
To prove the superiority of the Doc2 Vec model to process text, we compare Mul-RSR with other state-of-the-art approaches. They are described as follows:  
* \(TF\)-\(IDF\): Term Frequency-Inverse Document Frequency [4]. It evaluates the importance of words based on the frequency of feature items and document frequency.
* \(BOW\): Bag of Words [4]. It puts all the words of a corpus into a set. The words are independent of each other. It mainly considers the number of occurrences of the words.
* \(LDA\): Latent Dirichlet Allocation [22]. The model is a generative model based on probabilistic topics, which trains a set of potential topics from the existing text set.
* \(Word2 Vec\): Word to Vector [24]. The model trains and predicts text based on a neural network and maps each word to the output of a low-dimensional vector.
* \(Doc2 Vec\): Document to Vector [25]. This model is an extension of Word2 Vec. Low-dimensional vectors of variable-length text fragments are obtained after training. To verify the recommendation performance of Mul-RSR, we compare Mul-RSR with other recommendation models.
* \(CB\): Content-Based [4; 22]. This model makes predictions and recommendations based on users' past content preferences.
* \(DT\): Decision Tree [40]. Each internal node of the model represents a test of an attribute, and each branch represents the result of the test.
* \(RBM\): Restricted Boltzmann Machine [41]. It is a neural network composed of visible layers and hidden layers, with full connections between layers and no connections within layers.
* \(CNN\): Convolutional Neural Network [42]. This model is a special feedforward neural network with convolutional layers and pool operations.
* \(LSTM\): Long Short Term Memory Network [43]. The model adds three control gates and a cell structure to make the network have memory capabilities.
* \(MLP\): Multi-Layer Perceptron [28]. It is an extension of the perceptron and contains multiple neuron layers.  
* _LSTM-AT_: LSTM-Attention. A recommendation model constructed by combining LSTM and attention mechanism.
* _Mul-RSR_: The recommendation model we propose is constructed by combining MLP and attention mechanism.
## 5 Results and Discussion
### Validating the Proposed Methodology on a 69-Node MG
#### 5.3.1 PGA Performance in the Grid-Connected 69-Node MG

Table 8: Objective function values for the 69-node MG in grid-connected mode over a one-week operation period. The standard deviation was approximately 1.9008%, and the coefficient of variation was 1.5849%, once again reflecting a stable and consistent reduction performance under varying conditions. The fact that these reductions surpass 100% reflects the MG's ability to generate more renewable energy than it consumes, allowing it not only to achieve self-sufficiency but also to inject surplus clean energy into the main grid. This surplus displaces higher-emission generation sources in the broader electrical system, effectively supporting the reduction in the total grid emissions. As a result, the proposed strategy contributes to both improving operational efficiency and advancing environmental sustainability, aligning MG operations with broader carbon reduction targets and decarbonization goals. Moreover, the average computation time required by the proposed methodology to solve the \(CO_{2}\) emissions minimization problem was approximately 434.4161 s per day, equivalent to 7.2403 min. This is consistent with the execution times observed for \(FO_{1}\) and \(FO_{2}\), all averaging close to 7.2 min per day, which confirms the practicality and scalability of the proposed optimization framework across multiple objectives in real-time or day-ahead operation planning. Additionally, a comparative analysis of the processing times required by the PGA for the 33-node and 69-node MGs was performed. This evaluation highlights how increasing system complexity affects the computational effort needed by the metaheuristic technique to solve the optimization problem. In the grid-connected 33-node, PGA required an average computation time of 241.2056 s across the three objective functions, whereas, in the 69-node MG, this time increased to 434.6393 s. This represents an 80.1974% increase for grid-connected operation, highlighting that greater system complexity results in significantly longer processing times. Nevertheless, these durations, on the order of minutes, are relatively short compared with the 24-hour analysis performed by the implemented technique. This suggests that the proposed strategy can be executed multiple times in response to system contingencies, which reinforces its overall reliability and flexibility.
# A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery
## 2 Related Works
### Selected Algorithms for the Comparison  
To analyze the performance of our approach, we contrasted our R-PRIM-CL with three of the most well-known and reputable algorithms in the field of data classification in Section 4 to assess the effectiveness of our method. Although the comparison can be expanded to include other algorithms, it is interesting to compare classifiers based on bagging with random forest (RF), boosting with XG-Boost (XGB), bump-hunting with random PRIM-based classifier, and the linear model with logistic regression (LR). Indeed, comparing a new classification algorithm with established algorithms like random forest, XG-Boost, and logistic regression is essential because these well-known models represent different strengths, weaknesses, and approaches to classification, making them valuable benchmarks.  
The random forest [36] ensemble technique efficiently handles large high-dimensional datasets by combining multiple decision trees to increase the accuracy, robustness, and resistance to overfitting [37]. One way to evaluate how well a new algorithm captures complex patterns and interactions in the data is to compare it with random forest.  
XG-Boost [38], with its exceptional accuracy, speed, and efficiency, excels in machine-learning comparisons and real-world applications. It is especially good at handling large datasets, missing values, and imbalanced data [39]. Assessing the new algorithm's speed, scalability, and accuracy against one of the most competitive models out there is made possible by comparing it to XG-Boost.  
In contrast, the widely used technique known as logistic regression [11] is easy to understand, straightforward, and a reliable baseline because it can effectively simulate linear relationships between inputs and outputs. It offers a simple point of comparison to assess whether the new algorithm's added complexity represents a significant improvement over a conventional and uncomplicated method.  
The range of machine-learning techniques covered by these algorithms, which include boosting, tree-based ensembles, and linear modeling, is quite extensive, ranging from simple to complex. Its performance across various data distributions, feature interactions, and problem complexity is demonstrated through benchmarking against them, which aids in establishing the new algorithm's efficacy, adaptability, and competitive edge. Additionally, these comparisons show where R-PRIM-Cl sits on the interpretability versus predictive power spectrum: while random forest and XG-Boost offer high accuracy with less interpretability, logistic regression offers high interpretability. Thus, comparing R-PRIM-Cl to logistic regression, XG-Boost, and random forest offers a thorough understanding of its advantages, disadvantages, and possible uses while placing it in the context of classification models as a whole.
# Locating Image Sources from Multiple Spatial Room Impulse Responses
## 3 Case Studies
### Amsterdam Concertegebouw  
In addition to the measurements in the variable acoustic room, the algorithm was applied on two concert hall datasets. Originally measured for concert hall acoustics research [5; 19], these datasets have not been measured with this application in mind. Nevertheless, they contain 25 source positions on stage and several receiver positions scattered around the audience area, which is an interesting setup to apply the image source search. The source layout is depicted in Figure 6 and the colors are chosen so that instrument sections share the same color with different shades.  
The Amsterdam Concertegebouw is well known for its praised acoustics. The hall has been studied a lot, and the latest research reported large scale measurements that allowed visualizing the waveforms from a single source [20]. In this study, we use measurements from 25 source and 10 receiver positions to find out the most prominent image sources. The source positions are approximated from the drawings and the measurement documentation. The visualizations of the results, see Figures 13 and 14, reveal that the sources (i.e., the direct sounds) are found quite accurately and the algorithm locates the receivers to their positions surprisingly well. Note that receivers R9 (on side balcony) and R11 (behind the loudspeakers) are not used as they are not in the main radiation direction of the loudspeakers and therefore their position estimates are noisy. Furthermore, sources 19 and 20 are not used for the same reason; they point backwards to approximate the directivity of French horns.  
Figure 13 clearly shows that the algorithm finds the first order image sources of the side walls as well as the image sources of the ceiling and the back wall of the hall. In addition, the coloring illustrates the mirroring of the orchestra layout. The figure is done requiring 3 ER objects per image source and therefore hardly any higher order image sources are found. When applying only 2 ER objects per image source (Figure 14) some second order image sources are found at the expense of more detection noise.  
In Figure 14, the image sources are again aligned as proposed in Section 2.6, effectively showing the first order image sources better. The Figure illustrates the most interesting finding in the transverse plot in the low right corner. There are image sources that are elevated and closer to the sources than the first order image sources from the side walls. The reflecting surface has to be a softin on the lower part of the balcony front, as the balcony fronts have a vertical structure. That structure creates a cat-eye reflection which is seen as blue elevated image sources on the left and red elevated image sources on the right. In other words, first lateral early reflection from half of the stage area (even blue or red sources) is created with the balcony front softit, not the side wall in Concertegebouw.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Results  
#### 3.5.1 Preferences  
For each of the four features, Figure 2 depicts listeners' preferred adjustments relative to the unmodified speech baseline (left column), mean keywords correct scores (middle), and the time spent in the adjustment phase (right). For all features, listeners made the smallest adjustment relative to the original in quiet, and adjustments became progressively larger as the noise level increased. While listeners boosted energy in the mid and high bands, the opposite was the case for the low band, where listeners preferred to attenuate its energetic contribution. Since the attenuation of the low band results in the boosting of the other spectral regions, the strategies employed by listeners point to a consistent goal of increasing the SNR in the mid-high frequency region. Listeners also preferred flatterspectral tilts with increasing noise, again suggesting a preference for reallocation of energy to the mid-high frequencies.  
Since adjustment choices were not always normally distributed (see Figure 3), rank-based Kruskal-Wallis H tests were conducted to examine the effect of SNR on preferences for each feature independently. These tests showed a statistically significant effect of SNR for all features (min H = 56.3, all \(p<0.001\)). Post hoc pairwise comparisons using [PERSON]'s test, with Holm correction [73] for multiple comparisons, indicated that preferences for all features differed across pairs of SNR conditions (all \(p<0.05\)), with the following exceptions: \(-3\) and \(-6\) dB for the low band (\(p=0.07\)), and between the pair 0 and \(-3\) dB for both the mid band (\(p=0.08\)) and spectral tilt (\(p=0.12\)). Statistical comparisons were performed using the kruskal and posthoc_dunn functions of the stats.scipy and scikit_posthoc libraries in Python [74].  
Figure 2: (**Left**): Listeners’ preferred energy adjustments relative to the unmodified speech baseline (dotted line); (**middle**): mean keywords correct; (**right**): time spent in the adjustment phase. Error bars here and elsewhere denote \(\pm\) 1 standard errors.
## 3 Results and Discussion
### Analysis of the Means and S/N Ratios of the Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Levels**} & \multicolumn{4}{c}{**Polishing Efficiency (PE)**} & \multicolumn{4}{c}{**Torque (T)**} \\ \cline{2-10}  & \(T_{g}\) & \(\omega\) & \(F_{z}\) & \(V_{f}\) & \(T_{g}\) & \(\omega\) & \(F_{z}\) & \(V_{f}\) \\ \hline
1 & \(\mathbf{-2.451}\)* & \(-3.838\) & \(-4.759\) & \(\mathbf{-2.642}\)* & \(\mathbf{9.049}\)* & \(\mathbf{9.398}\)* & \(\mathbf{12.278}\)* & 8.642 \\
2 & \(-3.128\) & \(-2.464\) & \(-2.297\) & \(-2.888\) & \(8.692\) & \(8.667\) & 8.219 & 8.808 \\
3 & \(-\) & \(\mathbf{-2.066}\)* & \(\mathbf{-1.312}\)* & \(-2.838\) & \(-\) & \(8.546\) & 6.114 & \(\mathbf{9.160}\)* \\ Delta & 0.677 & 1.772 & 3.447 & 0.246 & 0.357 & 0.851 & 6.164 & 0.518 \\ Rank & 3 & 2 & 1 & 4 & 4 & 2 & 1 & 3 \\ \hline \hline \end{tabular} Notes.* Optimum levels of the factors.  
\end{table} 
Table 4: Response table for mean S/N ratios. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Exp. No. **} & \(T_{g}\) & \(\omega\) & \(F_{z}\) & \(V_{f}\) & **Mean PE** & **Calculated \(S\!/N\) for PE** & **Mean \(T\)** & **Calculated \(S\!/N\) for \(T\)** \\ \hline
1 & 0.019 & 3500 & 8 & 5 & 0.572 & \(-4.848\) & 0.24 & 12.396 \\
2 & 0.019 & 3500 & 12 & 10 & 0.719 & \(-2.863\) & 0.37 & 8.636 \\
3 & 0.019 & 3500 & 16 & 15 & 0.829 & \(-1.629\) & 0.38 & 8.404 \\
4 & 0.019 & 4000 & 8 & 5 & 0.634 &