esa-sceva/satcom-chunk-collection huggingface dataset:
# Future Renewable Energy Communities Based Flexible Power Systems
## 1 Introduction
These managerial solutions are provided from different aspects, as follows:  
* Solutions related to the management of the types of future energy resources including those coming from the sun, earth, and ambiguous ones. * Solutions related to advanced, flexible, and cutting-edge power electronic interfaces that connect renewable resources to the main grid. * Solutions related to the management of flexible energy resources, to deal with the high share of renewables in the future. * Solutions related to the management of future prosumers and consumers, individually, or in a form of energy communities. To the best of the authors' knowledge, the previous similar literature discussing the solutions to future renewable-based power systems did not consider all the above-mentioned aspects. Table 1 reviews some similar papers that discussed the solutions to the renewable energy communities based flexible power systems. For example, ref. [8] reviews the state-of-the-art literature that captures CO\({}_{2}\) with a focus on the integration of different energy resources. Reference [9] reviews the recent advanced technologies that help demand-side management in the smart grid environment while [10] discusses the proposed approaches to implement demand response programs in the future grid. An open-source model for the active contribution of prosumers to energy provision and in the future power system was provided by [11]. Ref. [12] analyzes the integration of some types of future renewable sources such as wind and PV that are supported by an energy storage system through power-electronic converters. A comprehensive study was conducted on the power electronic devices and their future directions and applications by [13]. Renewable energy communities and their applications in the future power system were also studied and analyzed in [14]. In another work [15], the authors studied the impacts of high PV penetration on future power systems and reviewed the potential solutions to mitigate these impacts. However, the solutions were not assessed fully to consider all of the aspects, compared to our paper. The authors or [16; 17; 18; 19] tried to discuss solutions to increase the flexibility of power systems and introduce new sources of flexibility. However, they mainly focus on demand-side resources and do not consider different types of energy for future systems. Finally, the solution provided by [20] was from a power electronic point of view, trying to control multilevel converters which act as an interface between a renewable resource and the grid. However, this paper presents a holistic approach by combining four different solutions to pave the way towards future renewable-based and flexible power systems. The proposed approach combines the solutions related to the appropriate types of future energy, advanced power-electronic-based devices, and tools that connect these resources to the grid in a flexible way, the future sources of flexibility as well as the prosumers' and consumers' adaptive management to adopt the maximum available flexibility.
## 4 Discussion
### Structural Setting and Earthquakes  
Possible relationship between SP subsidence and recent tectonic activity relies on the analysis of structural lineaments reported into the Italy Hazard from Capable faults database [46]. According to the main faults bounding the SP, we subdivided it in three sectors (Figure 5a) and with foreseen different deformation patterns for the three sectors from InSAR ascending and descending time series (Figure 6). The analysis of the averaged Envisat displacement time series show very similar trends for the three investigated areas, although with different absolute values. On the other hand, COSMO-SkyMed time series show a different deformation pattern for each sector.  
In detail, the ascending time series reveal a general trend characterized by an increase of the negative displacements but with greater absolute values in the southern sector. The descending time series highlight an increase of negative displacements in the Corigliano sector with values between 10 mm and \(-\)5 mm in the Villapiana-Sibari sector, and an expected increasing positive trend in the Cracti Delta sector. This trend can be related to the presence of a host bounded by Cracti and Timparelle faults [48].  
A comparison between ground deformation pattern and seismicity was performed taking into account the earthquakes occurrences and magnitude [74] during the time covered by SAR data (Figure 5a). No significant earthquakes struck the study area excepting for one shallow offshore M 4.5 event on 27 June 2006 [74] with none ground deformation effects (Figure 6).  
Therefore, we can exclude the presence of seismically-induced displacement and we hypothesize that the structural settings could indirectly influence the ground subsidence phenomena.  
Figure 5: (**a**) The Amendolara Ridge located in the Ionian Sea in front of the Sibari Plain. The bathymetric map (modified from [73]) shows the presence of three morphological highs (Amendolara, Rossano and Cariati highs). The main structural elements (thrusts and anticlines) are extracted from [71]. In the map is showed the hypocenters location of the earthquakes occurred between 2003 and 2010 (ISIDe [74]) with the relative magnitude and hypocentral depth. Onshore capable faults from [46] and focal mechanisms from [45]. Yellow dashed lines are the boundaries of three selected sectors (1. Corigliano; 2. Cracti Delta; 3. Sibari-Villapiana) for Envisat and COSMO-SkyMed time series analysis (Figure 6). (**b**) Sketch (based on the interpretation of the seismic profile F75-89 of [45]) representing the trigger of the flexural subsidence due to the overlap of the Amendolara Ridge back thrusts on Calabrid and Southern Apennines units.
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 7 Conclusions  
This study demonstrates the substantial potential of deep learning techniques in enhancing pneumonia detection from chest X-ray (CXR) images. Through a comprehensive comparative analysis of several state-of-the-art architectures--including VGG16, ResNet50, DenseNet121, InceptionV3, MobileNetV2, and Vision Transformer (ViT)--as well as a novel ensemble framework (PELM), we established that deep learning models can significantly outperform traditional diagnostic methods in both accuracy and operational efficiency. The proposed PELM ensemble achieved 96% accuracy, 99% precision, 91% recall, and a 95% F1-score, setting a new benchmark for AI-driven pneumonia diagnosis.  
Our findings emphasize that model architecture and feature representation strategies are critical to achieving robust performance. While individual models such as ResNet50 and ViT demonstrated strong generalization capabilities, the ensemble approach effectively combined their complementary strengths to improve reliability and diagnostic precision. Visual analyses--including training curves and ROC visualizations--further confirmed stable convergence and balanced sensitivity-specificity tradeoffs.  
Despite these promising outcomes, certain limitations persist. The curated dataset, though large and diverse relative to previous studies, may not fully represent the heterogeneity of real-world clinical populations. Future studies should therefore prioritize validation on multi-institutional, demographically varied datasets. Moreover, enhancing model interpretability remains an essential challenge for clinical adoption. Transparent prediction rationale is critical for building clinician trust and ensuring responsible deployment.  
Future research will integrate AI models into clinical workflows via real-time PACS/EHR compatibility, conduct prospective real-world studies, and advance explainable AI (XAI) for model interpretability. The methodologies also provide a foundation for expanding automated diagnostics to other pulmonary conditions like tuberculosis or chronic obstructive pulmonary disease (COPD).  
In summary, this work underscores the transformative role of deep learning in radiology and highlights its potential to augment clinical decision-making, streamline diagnostic workflows, and ultimately improve patient outcomes through faster and more accurate pneumonia detection.
## 3 Results
### Quantitative Findings  
#### 3.1.1 Self-Reported Emotional Response  
The self-reported emotional response (SRER) to all sounds was assessed at baseline and post-study (10-week measurement) to determine any changes in sensitivity amongst participants. After 10 weeks, four participants reported a decrease in sensitivity to at least one target stimulus (sounds that were perceived as negative [SRER \(\geq\) 4] by the participant and subsequently interacted with during the game). In contrast, one participant reported  
Figure 1: Self-reported emotional response user interface.  
an increase in sensitivity to one target stimulus, while another participant reported both a decrease in sensitivity to one and an increase in sensitivity to another target stimulus (see Figure 2). We found that the final participant (P08) did not interact with any auditory stimuli they rated negatively (\(\geq\)4) by SRE at baseline and as such found no changes in sensitivity to target sounds.  
Analysing baseline and 10-week post-study SRE scores for all target stimuli shows a similar distribution: the participants reported a decrease in sensitivity to seven individual sounds, an increase in sensitivity to two sounds, and no change in sensitivity to four sounds (Figure 3).  
The mean change in SRE for all target sounds was a decrease of 0.31, with a mean baseline level of 5.23 and a post-trial measurement of 4.92. Figure 4 displays the changes in SRE across all target stimuli for each participant.  
Figure 3: Number of target sounds that have been interacted with and their changes in SRE between baseline and 10-week assessment.  
Figure 2: Number of children reporting changes in sensitivity towards target stimuli between baseline and 10-week assessment. Sensitivity was measured using self-reported emotional response (SRER). Reduction in sensitivity = reduced SRER score for one or more target sounds. Increase in sensitivity = increased SRER score for one or more target sounds. Reduction + Increase = lowered SRER score for one and increased SRER score for another target sound. No changes = participant did not interact with target stimuli.  
Finally, analysis of SRER scores for non-target stimuli (stimuli assessed at baseline and post-trial at 10-weeks that provoked a negative response but were not interacted with) for all participants showed an average increase of 0.14 with an average baseline measurement of 4.67 and a post-trial measurement of 4.81.
## 3 Results
### Training Results
#### 3.3.1 Multilayer Perceptron  
Comparing the MLP models, we have the following results in Table 2 and the learning curves in Figure 6.  
Looking at Table 2, it is possible to notice that an MLP cannot replace the GRIDPP as the only post-processing step, since the RMSE of the pure GRIDPP was halved compared to the MLP using only MEPS data, and the NN was not able to converge much further than the early stopping patience, as seen in Figure 6. On the other hand, using MEPS and GRIDPP together allowed a longer period of convergence and a smaller error than GRIDPP alone.  
Since the combination of MEPS and GRIDPP performed better, deeper analyses comparing this architecture with the benchmarks were conducted, counting the number of errors greater than 3 K in each case, split by the variable \"hour ahead\" to understand how the error increases as the hours pass and uncertainty increases. This analysis is represented in Figure 7.  
Given the critical error threshold of 3 K, the behavior of GRIDPP and the neural network models was plotted in situations where the GRIDPP error was higher than 3 K for the test sample. Figure 8 represents the error higher than 3 K for GRIDPP and Figure 9 represents the error of the MLP model when the GRIDPP error was higher than 3 K. The MLP model achieved a smaller error in 74.8% of the samples where the GRIDPP error was greater than 3 K.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **RMSE** & **Stopped Epoch** \\ \hline Only with MEPS & 3.78 & 2005 \\ Only with GRIDPP & 1.797 & 5504 \\ With MEPS and GRIDPP & 1.786 & 9929 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test set RMSE on MLP models.  
Figure 6: MLP learning curve.  
Figure 8: GRIDPP error higher than 3 K.  
Figure 7: Comparing errors of MLP to the benchmark.  
The results above were actually similar to the MLP results. Once more, the combination of MEPS and GRIDPP as inputs of the neural network showed better performance than either of them alone. Therefore, the same combination was chosen to be compared to the benchmark, as shown in Figure 11.  
In the same way as was plotted for the MLP results, Figure 12 represents the CNN error distribution when the GRIDPP has errors greater than 3 K. The CNN achieved a smaller error in 73.8% of the samples where the GRIDPP error was greater than 3 K.  
In Figure 13, it is possible to see how the RMSE grows over the hours due to the increase in uncertainty. Here, the MLP and CNN curves are represented by the same line  
Figure 11: Comparing errors of CNN to the benchmark.  
Figure 12: CNN error in cases when GRIDPP error is higher than 3 K.  
(the blue one) because both results were very close, so it was not possible to distinguish the two lines.
# Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties
## 2 Methodology  
In robot manipulation, there are two different methods to move objects from initial position to final position, such as prehensile and non-prehensile methods. In prehensile manipulation, the method is to grasp an object and move to final position. On the other hand, in non-prehensile manipulation, the object can be moved to goal position by pushing, throwing, or striking. Furthermore, non-prehensile manipulation methods are mainly divided into two categories, such as quasi-static and dynamic methods [20]. In quasi-static manipulation, an object is always in contact with a manipulator and motions are assumed slow enough to neglect the inertial effect, such as pushing [21] and sliding [22]. The quasi-static manipulation is not suitable for limited workspace and in some applications where manipulations of an object demand higher speed. On contrary, in the dynamic manipulation, the object continues its motion after losing contact with manipulator. In the dynamic manipulation, the motion of the object is determine by the motion of manipulator and its dynamic characteristics after losing contact. This paper deals with singulation of objects in cluttered environment based on dynamic manipulations, where impulsive motions are used to scatter the object to achieve the desired distance among objects after scattering. Practically, it is difficult to control the moving object with impulsive actions to an exact location however, it still useful for object singulation application as the targets are not that stringent.  
In this paper, 115 videos were collected by performing the collision of single object and multiple objects with a manipulator. By processing these unlabeled videos, certain parameters are observed such as travel distance of each object after collision, travel time, velocities before/after collision, and acceleration of each object. Furthermore, using these observed motion parameters, physical properties of all objects being involved in the collision can be estimated. Based on these physical properties, a virtual world simulator is designed to perform a controlled scattering. In control scattering, the maximum and minimum distance is controlled between objects after collision to ensure that all objects are singulated. The initial position of objects is observed from the real world and is given as an input to the virtual world along with measured physical properties. The distance between objects is a function of manipulator colliding velocity. In the virtual world, the velocity of manipulator is controlled based on the feedback error (between measured distance and desired distance) until the desired distance among objects is achieved. Once the goal is achieved in virtual world, the same desired velocity input is given to the real world and then controlled scattering is performed.
## 3 Residual Dense Convolutional Graph-U-Net-Based GAN for Target Speech Enhancement
### Loss Function  
In most works on speech enhancement using DL, the mean square error (MSE)-based loss function is used as the criterion for training the neural architecture. In addition to MSE, the scale invariant signal-to-noise ratio (SI-SNR) can also be incorporated within the loss function as a training criterion. A loss function combining the SI-SNR and MSE for better training of the neural architecture has been applied in [27], leading to good speech enhancement performance under low SNR conditions. Following [27], we also apply the combination of SI-SNR and MSE as the loss function in this work, whereby the formulation of the loss function can be explained by (36)-(40), as follows:  
\[x_{target}=\frac{<x_{enh},\,x_{clean}>.x_{clean}}{||x_{clean}||_{2}^{2}} \tag{36}\]  
\[z_{noise}=x_{enh}-x_{clean} \tag{37}\]  
\[\mathcal{L}_{SI-SNR}(x_{clean},\,x_{enh})=10.log_{10}(\frac{||x_{target}||_{2 }^{2}}{||z_{noise}||_{2}^{2}}) \tag{38}\]  
In (36)-(38), the terms \(x_{enh}\) and \(x_{clean}\) represent the enhanced and clean target speech signals in the time domain, respectively. The operation \(<._{.}>\) denotes the dot product operation between any two vectors. In (38), \(||.||_{2}^{2}\) denotes the L2 (or Euclidean) norm. The SI-SNR-based loss function is denoted by \(\mathcal{L}_{SI-SNR}(.)\). If the true mask and estimated mask are denoted by \(M_{clean}\) and \(M_{est}\) (where \(M_{est}\in\{M_{s}^{DL},\,M_{int+noise}^{DL}\}\)), respectively, then the MSE loss function can be expressed as follows:  
\[\mathcal{L}_{MSE}(M_{clean},M_{est})=\sum_{\epsilon,f}[(Re(M_{clean})-Re(M_{ est}))^{2}+(Im(M_{clean})-Im(M_{est}))^{2}] \tag{39}\]  
In (39), \(Re(.)\) and \(Im(.)\) denote the real and imaginary parts, respectively. Based on (38) and (39), the combined loss function can be expressed as follows:  
\[\mathcal{L}(x_{clean},x_{enh})=\mathcal{L}_{SI-SNR}(x_{clean},x_{enh})+\log( \mathcal{L}_{MSE}(M_{clean},M_{est})) \tag{40}\]
###### Abstract
Many studies also point to possible abnormalities connected to reproductive functions [15]. [PERSON] et al. [27] reported a statistically significant increase in estradiol in Japanese medaka (_Oryzaias latipes_) females after exposure to fluoxetine at concentrations of 100 and 500 ng/L for a period of 30 days. Furthermore, [PERSON] and [PERSON] [36] found that fluoxetine also significantly affects fish behavior, causes swimming abnormalities, and reduces the caution of fish before a potential predator attack. Similarly, [PERSON] et al. [37] described an increase in aggressiveness of crayfish _Orconetes virilisafter_ after serntraline exposure. Additionally, [PERSON] et al. [38] reported opposite outcomes, in the form of seizure susceptibility and habituation, in African clawed frogs (_Xenopus laevis_) after exposure to two antidepressants from the same group of these psychiatric drugs. A fluoxetine-treated group of tadpoles showed increased seizure susceptibility and reduced startel habituation, and on the other hand, a citalopram-treated group reported decreased seizure susceptibility and increased habituation. Various representatives of aquatic organisms (e.g., fish, amphibians, invertebrates) are used in ecotoxicological tests for the evaluation of potential negative effects of tested chemicals [39; 40; 41]. The 3R approach (reduce, refine, replace) is an alternative strategy, which prevents the unnecessary suffering of animals and reduces the number of tested animals [52; 53; 54]. In accordance with European legislation, the embryonic stages of aquatic organisms are not considered as experimental animals. Their undeniable advantage is that they are very sensitive to the action of various pollutants, providing relevant and reproducible results. In addition, the embryonic tests are fast and cost-effective [54]. In view of this, the aim of our study was to compare the sensitivity of the embryos of two different representatives of aquatic organisms (zebrafish and African clawed frogs) exposed to two antidepressants both as a single substance and as a mixture. Fluoxetine hydrochloride and citalopram hydrochloride acting as selective serotonin reuptake inhibitors were chosen because of their abundant use in human medicine and due to the frequent occurrence of their residues in the surface water. The evaluation was performedusing embryonic toxicity tests, in which selected indicators of embryotoxicity (mortality, hatching, malformations, and heart rate) were monitored at regular intervals for 96 h.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 5 Experimental Analysis
### Results of Data Set \(\widetilde{\Psi}_{2}\)  
Figure 13 shows the position error of ACNA, EKF and RIO under GNSS jump attacks (data set \(\widetilde{\Psi}_{2}\)). After GNSS attacks in data set \(\widetilde{\Psi}_{2}\), the \(1\sigma\) positioning accuracy of GNSS increases to \(13.47\) m. Figure 14 shows the corresponding movement trajectories of the three algorithms of ACNA, EKF and RIO under GNSS attacks. Their east, north and relative positioning accuracy (\(\sigma_{\text{east}},\sigma_{\text{north}}\) and \(\sigma\)) and maximum error (\(E_{\text{MaxEast}}\), \(E_{\text{MaxNorth}}\) and \(E_{\text{Max}}\)) are shown in Table 3 below.  
It can be seen that the EKF algorithm fails to detect GNSS jump attacks and the positioning errors grows gradually as the GNSS attacks occur. The 1\(\sigma\) positioning accuracy of the EKF algorithm increases to 13.24 m, with an improvement of 2% compared with the GNSS. Additionally, the RIO algorithm and ACNA algorithm can detect jump attacks in time. The 1\(\sigma\) positioning accuracy of the RIO algorithm increases to 1.39 m, with an improvement of 90% compared with the GNSS. Additionally, the 1\(\sigma\) positioning accuracy of the ACNA algorithm increases at 1.14 m, with an improvement of 92% compared with the GNSS.