esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 2 Related Works
### Active Learning  
As mentioned previously, manually labeling datasets for rumor detection can be costly, and may demand domain-specific expertise. Active learning, which aims to achieve high accuracy with minimal labeled instances, has gained significant traction in various domains, including text classification [25; 29], biomedical text mining [30], and computer vision tasks [31]. Uncertainty-based sampling models have demonstrated particularly promising results, making them a predominant choice when applying active learning to social media.  
For instance, [PERSON] et al. [32] demonstrated how traditional text classifiers can reduce their need for labeled training data through active learning by leveraging a vast pool of unlabeled documents. [PERSON] et al. [33] conducted a large-scale empirical study on deep active learning, confirming its efficacy in improving deep learning performance for text classification, especially through Bayesian active learning by disagreement.  
While active learning is commonly employed in text classification, its application in rumor detection has been relatively limited. Most active learning studies in the context of rumor detection have focused on fake news detection. [PERSON] et al. [34] explored active learning for identifying the veracity of fake news using uncertainty-based probability of classification. This approach was later extended to domain-specific and context-richframeworks [35]. [PERSON] et al. [36] introduced a fake news detection framework incorporating active learning based on entropy sampling, achieving high accuracy with a limited training dataset (4% to 28% of available data). [PERSON] et al. [37] conducted a comparative study of various active learning strategies on different text embeddings for text classification and fake news detection. Although there are inherent differences between rumor detection and fake news detection, lessons from active learning in the context of fake news detection can inform and benefit rumor detection tasks.  
Recently, [PERSON] et al. [25] introduced an active transfer learning framework for rumor detection. Their work explored different pretrained language models, estimators, and active learning strategies. Extensive experiments on the PHEME dataset revealed that supervised machine learning rumor detection models using embedded representations with limited labeled data were able to achieve similar performance to models trained on the entire dataset. The authors' goal was to design a new rumor detection model based on pretrained language models and active learning. In contrast, the present study aims to conduct a comprehensive and comparative analysis of rumor detection approaches using active learning.
# Ultrasound B-Mode Imaging: Beamforming and Image Formation Techniques
###### Abstract  
In the last decade, very active research in the field of ultrasound medical imaging has brought to the development of new advanced image formation techniques and of high-performance systems able to effectively implement them [1]. For years, Brightness (B)-mode, one of the mostly used ultrasound imaging modalities [2], has been based on a time-consuming process, in which focused beams are iteratively sent into the body and the received waves are used to form an image scan-line, covering line-by-line the region of interest.  
\"Image formation\" refers to the whole process of image reconstruction, starting from the transmission strategy to the reception of signals, beamforming, and image processing. The role of the so-called \"beamformer\" is central in this process, as it manages the ultrasound beam generation, focusing, and steering [3]. Image quality is in fact deeply influenced by the beam shape, and thus the beamforming optimization plays an important role in maximizing the signal-to-noise ratio, contrast, and resolution of the final image, while limiting as much as possible off-axis interferences to reject clutter and noise. Additionally, an important goal is to improve the acquisition frame-rate, which, as mentioned above, is limited by the line-by-line acquisition process [4].  
Image enhancement methods play an important role during both the image pre- and post-processing phases [5]. In the former case, these techniques aim at improving the quality of B-mode frames by directly operating on the image generation process, as for example in spatial/frequency compounding, pulse compression, or harmonic imaging. The latter category instead refers to approaches aimed at reducing noise/artifacts, making speckle more uniform, detecting edges, and consequently facilitating the following processing steps, like segmentation or measurement of quantitative parameters.  
Given the above premises, this Special Issue was launched to collect novel contributions on both ultrasound beamforming and image formation techniques. Twenty-one interesting works were consequently submitted and, among them, 10 were selected for publication (i.e., 48% acceptance rate).
# The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review
in 2021 aimed at extracting pattern features and visualizations from vGRF signals in PD patients across different severity stages (0, 2, 2.5, and 3 on the HY rating scale) [58]. The authors achieved their objectives by transforming one-dimensional time-domain signals into two-dimensional patterns (images) using a feature transformation method based on a continuous wavelet transform. Subsequently, they constructed and trained a CNN classifier and evaluated the performance of the classification algorithm through cross-validation. Previously, [PERSON] et al. (2020) separately modeled the left and right gait parameters using a CNN, followed by an attention-enhanced long short-term memory (LSTM) network [59]. However, the first algorithm to perform a severity prediction based on the UPDRS, in addition to classifying each subject's walk either as [PERSON]'s disease or a control, was the one proposed by [PERSON] et al. in 2020 [60]. Each subject's walk was divided into smaller segments of 100 time steps with 50% overlap, and these segments were then used to train a 1D CNN (1D-Convnet) so that it was able to classify them. Among the studies that primarily aimed to predict PD, [PERSON] et al. achieved the best performance in 2020, where they reached an accuracy of 99.31%, a sensitivity of 99.35% and a specificity of 99.23% with their CNN-LSTM [59]. These numbers were not outperformed by the neural networks trained by other authors. Regarding the goal of predicting the severity of PD, [PERSON] et al. had the highest results in 2022, with an accuracy of 99.5%, a sensitivity of 98.7%, and a specificity of 99.1% [57]. In general, the methods exploited for analyzing data from wearable sensors employ CNNs and DNNs, such as U-Net and AlexNet, and other ML techniques, like random forest and k-nearest neighbor. These techniques have enabled authors to extract and analyze temporal and spatial features from accelerometer and gyroscope data, and thus, demonstrate high accuracy in diagnosing neurodegenerative diseases, such as PD, HD, and ALS, despite the limitations of small datasets. Additionally, the use of signal transformations, like a continuous wavelet transform, has improved the performance of predictive models.
# Effects of Arm Swing in Lower Limb Muscle Activation and Coordination During Treadmill Walking
###### Abstract  
Arm swing is an inherent aspect of human locomotion that enhances lower limb (LL) muscle activation, which may explain its benefits for stroke rehabilitation over fixed-arm approaches. This study analyzed how restricting arm movement affects LL muscle coordination by comparing treadmill walking with arms (WWA) and walking with no arms (WNA) through muscle synergy analysis. Surface electromyography was recorded from eight LL muscles in ten healthy participants. Significant differences were found in muscle activity envelopes at 50% of the gait cycle (GC) for the Gluteus Medius, 0% and 100% GC for the Vastus Lateralis and Semintendinosus, and 25% GC for the Semitendinosus Medialis, and Soleus. The Rectus Femoris and Vastus Lateralis showed higher variability and activation in WNA compared to WWA. Synergy analysis revealed four muscle synergies, with a median global variance of 95%. While spatial components were similar, temporal differences emerged at 0% GC for Synergy 1, 5% and 90% GC for Synergy 2, and 95% GC for Synergy 3 (\(p<0.05\)). Our results suggest that arm swing influences LL muscle activity and coordination during walking. Future studies will aim at understanding the effects of arm swing in stroke rehabilitation, which could help design more effective gait rehabilitation protocols including arm swing.  
arm swing; electromyography; muscle coordination; muscle synergies; walking
## 3 Results
### Maximal Endurance Measurements
#### 3.4.1 \(\rm{VO_{22\rm max}}\)  
Results of the \(\rm{VO_{22\rm max}}\) treadmill test showed no time effect for \(\rm{VO_{22\rm max}}\) rel (HL: 61.1 \(\pm\) 5.3-58.9 \(\pm\) 5.4 mL\(\cdot\)min\({}^{-1}\cdot\)kg\({}^{-1}\); LL: 59.7 \(\pm\) 9.4-59.9 \(\pm\) 8.3 mL\(\cdot\)min\({}^{-1}\cdot\)kg\({}^{-1}\); CON: 63.4 \(\pm\) 8.6-63.3 \(\pm\) 6.0 mL\(\cdot\)min\({}^{-1}\cdot\)kg\({}^{-1}\); \(p\) = 0.905), \(\rm{O_{2}}\)-Pulse\(\rm{VO_{22\rm max}}\) (HL: 18.9 \(\pm\) 2.6-18.3 \(\pm\) 2.5 \(\rm{VO_{2}}\cdot\)HR\({}^{-1}\); LC: 15.9 \(\pm\) 5.0-16.3 \(\pm\) 4.5 \(\rm{VO_{2}}\cdot\)HR\({}^{-1}\); CON: 17.8 \(\pm\) 3.2-18.3 \(\pm\) 3.2 \(\rm{VO_{2}}\cdot\)HR\({}^{-1}\); \(p\) = 0.735), and \(\rm{V_{max}}\) (HL: 11.6 \(\pm\) 0.8-12.0 \(\pm\) 1.3 km\(\cdot\)h\({}^{-1}\); LL: 11.7 \(\pm\) 0.9-11.9 \(\pm\) 0.9 km\(\cdot\)h\({}^{-1}\); CON: 12.5 \(\pm\) 1.5-12.5 \(\pm\) 1.1 km\(\cdot\)h\({}^{-1}\); \(p\) = 0.202). There were no significant interactions between time and group for any of the variables.  
Figure 4: Individual data points for \(\rm{VO_{22\rm max}}\) and \(\rm{VO_{2\rm peak}}\)-DP standardized to body mass before (pre-test) and after the intervention period (post-test) for HL, LL, and CON. The absolute difference indicates \(\Delta\) changes in oxygen uptake during the interventional period. The data points in bold and black with red crossbars represent mean values for each data set. \({}^{\star}\) Larger than at pre (\(p\) < 0.05).
## 3 Design Methodology
### Microstrip Line Design  
For parametric analysis purposes, the authors changed the transmission line length from 30 mm to 50 mm with respect to monitoring S\({}_{11}\) and gain spectra. Such analysis is conducted to ensure the effects of changing the transmission line length on the antenna performance. Figure 2a shows the evaluated S\({}_{11}\) spectra for three considered microstrip lengths. It is found that by increasing the transmission line length, the antenna frequency resonance shifts toward the lower frequency bands due to the increase in the electrical length [10]. Nevertheless, the antenna gain is found to increase rapidly with increasing the antenna length due to the fact of increasing the surface current [11]. Therefore, the authors considered the transmission line with a length of 50 mm as the best choice because it showed two frequency resonances at 1.9 GHz and 4.3 GHz with S\({}_{11}\) of \(-\)18.4 dB and \(-\)11 dB and gain of 1.5 dBi and 3.1 dBi as in Figure 2b.  
Figure 1: The proposed monopole antenna geometrical details.
# Chaotic Features of Decomposed Time Series from Tidal River Water Level
## 3 Characteristics of Water Level and Decomposed Time Series
### Water Level and Sea Surface Level Characteristics  
The information entropy theory was applied to identify the relationship among the separated four components as shown in Equations (1)-(4). In addition, the amount of information provided to the water level time series was quantified, and the results are summarized in the following Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Water Level** & **Tide** & **Wave** & **Noise** & **Rainfall-Runoff** \\ \hline Number of data (daily) & \multicolumn{3}{c}{2891 (1 January 2011–31 November 2018)} \\ Mean (m) & 1.28 & 1.17 & 0.12 & 0.02 & 0.01 \\ Standard deviation (m) & 0.18 & 0.10 & 0.12 & 0.05 & 0.12 \\ Coefficient of skewness (m) & 8.61 & 0.14 & 0.22 & 2.00 & 23.47 \\ Coefficient of variation & 0.15 & 0.08 & 1.01 & \(-\)1.87 & 15.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic statistics for the decomposed time series of water level from Ulsan station.  
Figure 4: Decomposed components from water level and sea level: (**a**) tide component, (**b**) wave component, (**c**) noise component, (**d**) rainfall-runoff component.  
\(\sum\) The sum of the four information entropies obtained using Equations (1)-(4) is 4.67. The value of the tide component is 2.22, indicating that it has the most information on the original water level data. This was followed by the wave component with 1.14, the rainfall-runoff component with 0.80, and the noise component having the least information with 0.51.
# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 3 Methodology  
We employ multiple different classical methods and neural networks for real-time classification, to log activities, find regions of interest and to estimate pulse rate and breath ing rate, and extract various additional pieces of information, such as in motion activity monitoring and different nursing activities. First, we separated the different detection tasks and developed a solution for them. At the top level, the scenario is recognized by applying statistical methods and a neural network. In this way, we can interpret the scene and provide high-level warnings and control for the lower-level vital sign extractions. In parallel, the respiration and heart signal extraction and rate calculation are performed by two separate networks. Finally, a long duration statistical evaluation is done, collecting information from the different algorithmic components.  
In the following subsections the main algorithmic components are described. These are the (i) the Top-Level Classification block, (ii) the Pulse Calculator Block and (iii) the Respiration Calculator Block.
## 6 Performance Analysis
### Numerical Results
#### 6.4.1 The Impact of Massive Access on the RACH Success Probability  
Figure 4(a),b confirm and demonstrate the theoretical analysis of the impact of increasing the number of devices in a network where the number of preambles is limited. Applying Equations (2)-(4), it is observed in Figure 4(a) that, regardless of the number of devices, the success probability of the devices accessing the network increases when the number of preambles increases. Figure 2 shows that, during the re-transmission attempts of devices, the probability of collision is almost 50% when only 30 preambles are available because the collided devices will remain in the system, waiting for the expiration of their back-off time to re-transmit. The re-transmitting devices will join the group of newly arrived devices, resulting in a more congested network and exponentially increasing the probability of collision.
## 4 Discussion
The results of the proposed study, although solely being able to identify AD in the areas mentioned above, do not reject the hypothesis that AD can affect connectivity in other regions of the brain and hence this provides opportunity for future studies to confirm. Clearly, this study is not without limitations. A first limitation arises from the duration of the epochs that were available in the dataset (12 s per subject). Longer epochs would have provided more EEG samples for phase synchronization analysis, as well as an opportunity to evaluate the dynamic changes of connectivity over time, as had been previously done in [PERSON] et al. [30]. Another limitation involved the number of participants, as previously mentioned. The dataset used in this research consisted of 20 AD participants and 20 age-matched HCs. Given the individual differences inherent to the progress of AD, a larger dataset would have been optimal to yield more reliable results. Moreover, this study made use of the phase-locking value as an index for phase synchronization, as the data was recorded in a bipolar manner, and therefore the analysis was considered to be robust to the common source effects [27; 30]. Future research could use other indices of functional connectivity, such as coherence and phase-lag index (PLI), to investigate their efficacy in detecting AD impacts on the brain activity. Finally, it shall be noted that this study applies a neural metric for Alzheimer's Disease detection from a data science perspective rather than a neuroscience one. This implies that the methodology employed in this study strived to find an accurate tool for the detection of AD from EEG brain activity, rather than attempting to explain the cognitive and neural mechanisms that underlie the observed differences between AD patients and healthy controls. In this case, the findings of this research are well in line with the existing literature regarding AD detection and brain connectivity and show that the combination of bipolar derivations with phase synchronization can yield comparable results to studies that used other connectivity methods. This qEEG analysis could therefore be considered as secondary tool, to be used alongside the visual EEG analysis that is employed by clinicians. Furthermore, as solely post-mortem studies can identify AD with perfect accuracy [6], the authors are aware that the proposed methods in this study may not yield the most accurate results in the prediction of AD from EEG. However, the data science perspective proposed in this study leaves room for more advanced techniques, such as machine learning (ML), which has been growing in the field of AD detection over the past years. Promising results have been observed in regular support vector machine (SVM) models [42], as well as in new, innovative ML methods [43], leaving fertile ground for future research in this domain.
## 5 Discussion
### Implication for Seismic Hazard  
The results of the present study indicate that seismic \(b\)-values could be an indicator of fault slip history and shear stress acted on the fault. The present study is thus helpful in seismic hazard assessment and earthquake prediction, especially for injection-induced seismicity in fields having unfavorably oriented faults, immature faults, or tensile fractures created by hydrofracturing. We must pay more attention to such faults because shear fracturing contributes a high level of injection-induced seismicity. Figure 7 shows the magnitude-frequency distributions and seismic \(b\)-values for earthquakes induced by injection during shale gas fracking at two shale gas sites, Weiyuan and Shangluo, in Sichuan China [2]. The Weiyuan site is located in the central uplift of the Sichuan basin, and has a lower density of pre-existing faults. Induced seismicity is probably associated with immature fractures created by hydraulic fracturing operations, and thus has a higher \(b\)-value (1.52). The Shangluo site, which is located in the south fold zone of the Sichuan basin, has a high density of pre-existing faults (most of which are unfavorable in the present-day stress field). Since the mean seismic \(b\)-value is 0.91, pre-existing and well-developed faults play a significant role in injection-induced seismicity at the Shangluo site. These interpretations can also explain the maximum earthquake magnitudes observed thus far, which are approximately 3.5 and 5, at Weiyuan and Shangluo, respectively. At the shale sites in the Sichuan basin, many well pads exhibited induced seismicity, with a sizable magnitude of up to approximately 3.5. Events with a magnitude greater than 4 are limited to well pads close to known or unknown faults. In order to produce an earthquake of magnitude 5, a fault having a size greater than several kilometers is required [2; 15], which is larger than fractures that can be created by shale fracturing.
# Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAlHub
## 3 Results
### Earth and Climate Sciences Use Cases
#### 3.2.2 Generating New Satellite River Imagery  
Data generation with deep neural networks is one of the approaches that has not been widely explored in water resources and climate research. However, one study explores generative adversarial networks (GANs) [53] in satellite river imagery generation. This model we show as a use case here is a pre-trained version of the Progressively Growing GAN [54] model with the dataset presented in [PERSON] et al. [55] that outputs a randomly generated bird's eye river image. Since this is a generation task from a random noise vector, the config file was created accordingly. The config file for this model can be seen in Appendix B.6. A generated 1024 \(\times\) 1024 river image from a random vector on EarthAIHub can be seen in Figure 7.
###### Abstract  
Several studies have shown the capabilities of Synthetic Aperture Radar to map sea currents in ocean regions mainly characterized by large flows. We consider the well known method based on the analysis of the Doppler Centroid. The Doppler, as, in general, the scattering from the sea, is sensitive to several phenomena, occurring between the upper ocean and atmospheric boundary layers. To investigate such phenomena, we considered the combined use of both ENVISAT calibrated amplitude and Doppler data in conjunction with hindcast wind information provided by atmospheric models as well as Wind and Doppler Geophysical Model Functions (W/D-GMF) developed, in the literature, for C-Band systems. This integrated analysis for the interpretation of the Doppler surface currents measurements was carried out on a case study located in the Mediterranean Sea which is characterized by a general low circulation regime: specifically, the coastal region around the city of Naples. In this case study, we show that generally, wind plays a direct significant role in the observed Doppler surface current. The availability of an oceanographic numerical model for one of the analyzed cases also allowed us to attempt to interpret the effect of the typical thermohaline circulation pattern on the Doppler anomaly.  
synthetic aperture radar; Doppler anomaly; sea surface currents 1