esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative System Usability in Spaceflight Analog Environments through Remote Observations
## 2 Background
### Remote Observations through Conversation Analysis
[13] coupled cardiovascular measures and speech to measure the dynamics of cognitive workload during various tasks (see also [14]). Voice recordings may serve as a non-intrusive measure of critical dynamics in human-machine interactions. In essence, empirical research underscores the value of in tegrated audio recordings in capturing nuanced dynamics of human-computer interactions and collaborative work. Our team has chosen to expand the application of conversation analysis to spaceflight analogs. In these analog environments, crew members spend extended periods together in isolation, often tasked with solving problems that demand coordination. Moreover, analog crews undergo close monitoring like astronauts in space, as analogs serve both as a research platform and a simulation of spaceflight. Analog missions are equipped with various video and audio recordings that can be leveraged to collect remote observations. This facilitates the collection of data while crews engage in problem-solving tasks using novel software. Past work has utilized voice data from a spaceflight analog setting to infer team cohesion based on utterances and vocal microbehaviors [11], which underscores the feasibility of using analog audio to explore the dynamics of collaborative HCI tasks. Consequently, we advocate for conversation analysis as a valuable addition to conventional usability methods, especially when studying remote populations that pose challenges in accessibility.
## 1 Introduction
Recently, many studies have shown that the double perovskite structure with a composition of \(A_{2}BM\)O\({}_{6}\) (\(A\) = Ba, Sr; \(B\) = Ca, Zn; \(M\) = Mo, W) is activated by trivalent europium ions (Eu\({}^{3+}\)) [12; 13; 14; 15]. Phosphorus activated by Eu\({}^{3+}\) are considered ideal red sources because of their sharp emission lines in the red region [12; 13; 14; 15; 16]. Eu\({}^{3+}\)-doped double-perovskite materials have a broad excitation band ranging from UV to visible light, and they also show highly efficient red luminescence. For that, Eu\({}^{3+}\)-doped double molybdenum (Mo)-based double perovskite oxides have attracted significant attention for their possible application as luminescent materials, such as Sr\({}_{2}\)MgMo\({}_{x}\)W\({}_{1-x}\)O\({}_{6}\): Eu\({}^{3+}\)[17], Sr\({}_{2}\)Ca(Mo/W)O\({}_{6}\): Eu\({}^{3+}\)[18], Sr\({}_{2}\)CaMoO\({}_{6}\): Eu\({}^{3+}\)[19], (Ba,Sr)\({}_{2}\)CaMoO\({}_{6}\): Eu\({}^{3+}\), Yb\({}^{3+}\)[20], Ca\({}_{2}\)LaMoO\({}_{6}\): Eu\({}^{3+}\) (\(M\) = Sb, Nb, Ta) [21], and A\({}_{2}\)CaMoO\({}_{6}\) (\(A\) = Sr, Ba) [6], respectively. To the best of our knowledge, the luminescent characteristics of concentrated Eu\({}^{3+}\) ions in Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors, which are molybdenum-based double-perovskite oxides, have not been reported. In this study, the red-emitting phosphors of Eu\({}^{3+}\)-doped Mo-based double-perovskite Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) oxides were synthesized by the conventional high-temperature solid state reaction method. We found that Eu\({}^{3+}\) concentration and the synthesizing temperature of the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors had a large effect on their luminescent characteristics. The concentration quenching effect of Eu\({}^{3+}\) ions in Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors was found and would be well discussed [22]. In this study, the first important novelty is that we have not found any similar studies about Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors. The second important novelty is that we found a linear relationship between the synthesizing temperature and lifetime of Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors. We also investigated the relationship between the synthesized temperature and lifetime of Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors.
## 3 Experimental Results and Discussion
### Comparative Analysis with Literature
This can be implemented on edge computing devices equipped with GPUs/TPUs, ensuring low-latency processing. The trained model can be deployed either on these edge devices or at centralized processing units to perform real-time disturbance classification. Once disturbances are detected and classified, the results are communicated to the central control center or grid operators via a secure communication network. These results can then trigger automated control actions such as switching operations, load balancing, or system protection reconfiguration to mitigate the impact of disturbances. Additionally, the system can be integrated with existing Supervisory Control and Data Acquisition (SCADA) systems to enhance comprehensive monitoring and automated decision-making. By adopting this layered approach, the proposed deep CNN ensemble system can be seamlessly incorporated into the existing power grid infrastructure, enabling enhanced monitoring, rapid disturbance detection, and effective response strategies, thereby improving grid reliability and stability in a smart city context. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Training Time**} & **Test Time (Batch of Fifty Samples)** & \multirow{2}{*}{**Test Time (Single Sample)**} \\ \hline ResNet-50 & 298 min 48 s & 2.98 s & 59.8 ms \\ VGG-16 & 293 min. 50 s & 2.77 s & 55.6 ms \\ AlexNet & 270 min 59 s & 2.51 s & 50.4 ms \\ SqueezeNet & 330 min 25 s & 4.02 s & 80.6 ms \\ ResNet-50 with SA mechanism & 283 min 41 s & 2.69 s & 53.9 ms \\ Voting Ensemble & - & 2.75 s & 55.1 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of computational times of deep CNNs models.
## 5 Improved DSR Using Phonological Features in Vocabulary-
### Results
#### 5.4.2 Very-Low-Resource Setting
To better understand the impact of using PFs in recognizing dysarthric speech, we categorize all dysarthric speakers based on their intelligibility levels (High/Medium/Low) using the information provided in the databases or estimated when such information is unavailable. The PER results averaged across all speakers in each intelligibility level are presented in Table 8, focusing exclusively on experiments of **Ordered** selection method for simplicity (**E16,23,24**). The statistical results here are calculated across speakers from each intelligibility level. The table reveals a consistent benefit of the **PFs** method, in line with previous findings. However, the advantages are most significant among speakers with high and medium intelligibility levels, nearly diminishing in the low intelligibility groups. And this disparity in performance becomes more pronounced with limited training data. These observations suggest that either accurately recognizing PFs from speakers with low intelligibility levels is challenging, or the mapping of PFs to phones becomes inaccurate, both resulting in inaccurate phone recognition. Conversely, for speakers with good intelligibility levels, precise PF recognition and mapping to phones via the Signature matrix appears to be achievable, thereby yielding superior performance compared to other methods. This observation confirms our previous hypothesis about the limitations of the **PFs** method.
## 3 Results and Discussion
### Comparison of the Predicted Glycemic Index (pGI) of BRS  
The glycemic index (GI) is an index that reflects the degree and variation of the impact of intake food on human blood sugar. The lower the GI value, the lower the blood sugar intake. Since measuring the GI value must be performed in vivo, it shows great fluctuation and is time-consuming and costly; as a result, current studies mostly replace the GI value ([PERSON] et al., 2016; [PERSON] et al., 2017). Using an in vitro digestion method, the pGI test simulates the digestive environment and measures the release of glucose from food to estimate the GI ([PERSON] et al., 2016). According to the GI value, the pGI value of food can be divided into three levels: low GI (GI < 55), medium GI (55-70), and high GI (GI > 70) ([PERSON] et al., 2016).  
The pGI values measured from the 16 BRS samples at D1 varied between 9.71-16.47 and were far less than 55, so they belonged to the low GI value range (Table 2). In addition, a significant correlation (\(p\) < 0.05) between the pGI values and banana cultivars could be observed in the present work. Kazirakwe, Jinfen 1, and Saba recorded higher pGI values, while Meishi 2 exhibited the lowest. The pGI was reported to correlate with the presence of resistant starch. As shown by [PERSON] et al. (2017), adding unripe banana flour to biscuits resulted in a decrease in the GI of biscuits, since the resistant starch contributed to slowing down the rate of enzymatic hydrolysis of starch. In addition, during baking, the structure of the natural starch particles tended to be disturbed and were easily hydrolyzed by digestive enzymes. However, adding BRS reduced the proportion of fast digestible starch and inhibited the hydrolysis process ([PERSON] et al., 2016). It showed that postprandial hyperglycemia and high insulin spikes caused by the rapid digestion of starch were suppressed by the resistant starch ([PERSON] et al., 2016). The lower the GI values of the resistant starch, the better the stability of the postprandial blood glucose. Therefore, unripe BRS could be a great potential dietary source for people with hyperglycemia, consistent with the previous research ([PERSON] et al., 2016; [PERSON] et al., 2017). Meishi 2 emerged as the most suitable BRS cultivar with the lowest pGI. Furthermore, the pGI of AAB genome BRS was moderately low, with an average of 11.68. In comparison, the mean pGI values of ABB and AAA genome BRS were 13.91 and 13.15, respectively.
## 2 Digital Twin Technology for V2G
### Predictive Analytics and Optimization Capabilities  
Digital twin platforms for electric mobility may leverage advanced predictive analytics to enable proactive system management and optimization. These capabilities extend beyond simple data visualization to provide business insights for energy management, maintenance planning, and operational optimization.  
Generally speaking, energy consumption prediction for EV utilizes historical data patterns, route information, and environmental conditions to forecast future energy requirements. This prediction can be used for intelligent pre-conditioning of battery systems,optimal route planning, and strategic charging schedule optimization. Machine learning algorithms, particularly recurrent neural networks and time series analysis methods, prove particularly effective for capturing the complex relationships between energy consumption data collected under real driving patterns and environmental conditions. As an alternative, the model-based approach presented here can be used for battery discharge calculations over real use profiles, offering high-fidelity predictions that may also complement data-driven methods.  
On the other hand, grid interaction optimization represents a critical application area where digital twins can analyse grid conditions, electricity pricing and vehicle availability to overall optimize V2G operations. This involves complex multi-objective optimization considering factors such as grid stability requirements, user mobility needs, energy consumptions and costs and battery health preservation. Digital twins for future power systems indicate substantial potential for transforming electrical grid operations, with particular emphasis on sustainability and security enhancements [36]. These can therefore play a fundamental role in V2G deployment.  
The here presented multi-physics model can be utilized in two complementary ways for AI optimization of V2G systems, both in the design phase and in the digital twin real-world implementation as in the following:  
* For neural network training, the model generates high-fidelity simulation data through thousands of diverse operational scenarios, enabling networks to learn complex relationships between driving conditions and energy consumption. Once trained, these networks can provide real-time predictions with millisecond response times, although they may require periodic retraining if vehicle characteristics change or evolve (such as due to battery deterioration).
* Alternatively, the model can serve as a simulation environment for reinforcement learning algorithms, where virtual agents experiment with different charging and discharging strategies while receiving performance feedback. This approach offers adaptive learning capabilities that can automatically adjust to changes in vehicle characteristics but require computationally intensive training periods.  
The model accuracy is crucial for both modalities, ensuring that both training data for neural networks and feedback for reinforcement learning are sufficiently reliable to develop robust optimization strategies for V2G optimisation.
# Weather Radar in Complex Orography
## 4 Solutions and Results
### Specific Needs and Customised Applications  
In Section 2.2, we briefly presented a number of customised radar nowcasting systems that have been developed and configured to respond to the specific needs of users. However, there is yet another example of a recently developed application that is worth mentioning, related to hail and the specific needs of the building industry and insurance sector.  
Both the northern and southern sides of the European Alps are areas with a comparatively high frequency of hail [52; 53]. Two radar products are routinely generated in Switzerland to describe hail, POH and MESHS. In addition, there is the polarimetric hydrometeor classification, which has two hail classes (Figure 7). POH indicates the probability of hail, MESHS instead indicates the maximum expected diameter of hail stones from severe hail. The two products have been available for 20 years and were used to build the data base of a recently established set of climatological maps of hail in Switzerland (www.hagelklima.ch, accessed on 14 December 2021). POH and MESHS both refer to a reference area of one square kilomet. However, in the building industry and the insurance sector, the area of interest often corresponds to the size of a single building.  
It is important to know the size of the largest hail stone that hits a building. The reference area is thus much smaller than a square kilometre, that is, around 100 m\({}^{2}\) for a detached house or 250 m\({}^{2}\) for an office complex. A transformation had to be found to estimate the largest hail size on a smaller reference area of the order of 100 or 250 square metres, starting from the MESHS climatology. Strictly speaking, this is not possible, unless we know the exact size distribution of hail and how the hail stones are distributed within the square kilometre of interest. For hail protection guidelines, it is possible to make assumptions on the _average shape_ of the size distributions and the _average spatial distribution_ within the square kilometre. These assumptions are then used to derive an arithmetic rule to transform an MESHS value, representing 1 km\({}^{2}\), into the size of the largest expected hail stone over a smaller area of the order of a building. Figure 20 presents such a transformation, LEHA, which was derived from hail size distributions observed in Switzerland during Grossversuch IV [172] and tested for plausibility with measurements from the recently deployed network of automatic hail sensors. LEHA involves a strong simplification, considering how complex the distribution of hailstone sizes is in reality. It is intended to give a rough estimate of hail diameters in areas of the size of a building for hail protection purposes.
## 4 Discussion
### Advantages and Constraints of Our Estimation Methodology
Most of the samples in their report came from the upper 30 cm of the seafloor. The compressional sound speed \(c_{0}\) was measured in the laboratory using the pulse technique, which operates at about 200 kHz, and has margins of error of about \(\pm\)3-5 m s\({}^{-1}\). They reported that the measurements were performed in three main environments: (1) a continental margin (shelf and slope), (2) an abyssal plain, and (3) an abyssal hill. At the continental margin, the average \(c_{0}\) for silty clay, clay silt and silt was 1517, 1546 and 1615 m s\({}^{-1}\), respectively. At the abyssal plain, the average \(c_{0}\) for clay, silty clay, and clayey silt was 1503, 1515 and 1528 m s\({}^{-1}\), respectively, whereas for the abyssal hill they were 1493, 1508 and 1522 m s\({}^{-1}\), respectively. These results are in good agreement with our results, considering the silty-clay composition of the seafloor in the study area.
## 4 Experimental Results
### Confusion Matrix Results  
In order to verify both areas and strengths of our proposal, the confusion matrix (CM) has been leveraged to further show whether our design is able to successfully classify personal interactions based on the selected features. Figure 3 illustrates CM outcomes for the classification performance of our proposed technique based on the RF model for the smartphone authentication system.  
Firstly, the matrix illustrates diagonal clarity, where the majority of predictions are correctly classified. For example, most of the users have been successfully classified based on prediction accuracy, and this shows the great performance of our proposal. The given simulation results indicate that our proposed technique is able to correctly recognize between user touch dynamic patterns based on valuable selected features, including spatial coordinates (X, Y), interaction duration or velocity, and touch pressure intensity.  
However, with some minor emerged errors, the matrix also reveals misclassification cases, represented by off-diagonal values, and this could be due to overlapping behavioral patterns among participated users, leading to the possibility of overlapping among classes. For example, one sample from each of the users \(6,7,\) and \(8\) have been misclassified as users \(8,19,\) and \(15\), respectively, due to some overlapping in dynamic pattern recognition, e.g.,  
Figure 3: The Confusion Matrix (CM) outcomes of our proposal.  
similar swipe trajectories and/or similar touch pressure levels among some participated users. Note that some minor deviations in performance may lead to missing data in some experiments when taking into consideration the differences in some samples among the participating users.  
Even though some numbers have not been classified correctly, the correct classifications shown on the main diagonal of the CM elucidates the significant effectiveness of the TOPSIS methodology-based feature extraction. Also, the high correct predictions in the matrix diagonal imply that the selected features, e.g., spatial X coordinates, trajectory curvature, and touch pressure intensity, are the most promising ones. By incorporating only valuable features and optimizing the hyperparameters of the model, the proposed technique can offer an excellent balance among different classes, and this, in turn, could lead to mitigate the wrong classification and obtain better performance.
# Dynamic Aeroelastic Response of Stall-Controlled Wind Turbine Rotors in Turbulent Wind Conditions
## 3 Reduced-Order Characterization of the Aero-Structural Interaction
### Wind-Speed Stability Threshold and the Role of Aerodynamic Damping  
For wind speeds between 5-16.5 m/s, we found that the dynamic oscillatory response of the rotor remains stable. The kinetic energy of the wind pulse initiates oscillations, and those oscillations become damped by the system, creating a decay in the oscillatory signal (which is observable in the time evolution of the axial component of the blade deflection depicted in Figures 2 and 3). Here, \(U_{lx}\) represents the instantaneous axial deflection of the blade about its equilibrium value corresponding to the deformation state induced by a certain constant \(W_{\infty}\).  
Above 17 m/s, the rotor, as an oscillatory system, no longer damps the oscillations, but amplifies them, feeding on the energy from the wind flow. After the pulse has passed, the  
Figure 2: Examples of the time evolution of the axial component of the blade deflection at wind speeds (a) below and (b) above the wind speed stability threshold.  
oscillations continue expanding exponentially until a second damping mechanism starts to act, slowing the growth and reaching an equilibrium where the oscillation amplitude becomes stable and self-sustained.  
As we will see in detail in the following, this change in behavior from stable to unstable oscillatory regimes is associated with a change in sign of the aerodynamic damping. This depends exclusively on the mean value of the wind speed.  
Between the stable and unstable regimes, there is a narrow range of wind speeds that exhibit the classical signs of a transitional behavior where attenuation and amplification may occur. Here, we see two examples (even though several variations are possible).  
Figure 3: Examples of the time evolution of the axial component of the blade deflection at wind speeds on the narrow range of the transitional regime. (**a**) lower end of the transitional range, and (**b**) higher end of the transitional range.  
For wind speeds that are early on in the transitional regime (Figure 3a), the system starts damping the oscillations, but they are not completely dissipated, and some residual \"bands\" of oscillation remain. Further, at higher wind speeds in this regime (Figure 3b), expansion occurs, followed by a brief stabilization, and then the oscillations decay. The same manifestation of sustained residual oscillations is present in the long term.  
This regime is characterized by a very rich combination of frequencies whose amplitudes evolve in time in a very complex manner.
# Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours
## 2 Materials and Methods
### Data Used  
Red Mullet specimens were collected on the trawl-fishing grounds along the island shelf and slope of Mallorca and Menorca during the 2002 survey of the BALAR project (2000-2004), which was integrated in the Spanish 'Programa Nacional de Datos Basicos Pesqueros' for the Balearic Islands. Trawls were carried out during daylight hours on board the oceanographic vessel R/V \"Francisco de Paula Navarro\" [43]. Gurnard specimens were collected on the trawl-fishing grounds along the Catalan Coast in commercial fishing boats of the ROSES and BOLINUS projects, both funded by the Catalan Government, during 2015 and 2017 [44].  
Side-view images of the whole fish were taken in the field with all specimens in the same standardised position and orientation. The fish appear in the center of the image, with the head oriented to the left and the belly facing the bottom of the image. The specimens had different ages and lengths, and the distances from which the pictures were taken were not the same. An expert taxonomist added two points to each image, one to mark the tip of the snout and the other to mark the anterior insertion of the first dorsal fin. Those points indicate the beginning and the end of the contour segments used by taxonomists to identify the mullet's species. Figure 4 shows, on the bottom, a specimen of each species and, at the top, the profile portion used by the expert to identify the species for all individuals in the database. For this study, we have a data set of 23 images for each species of Red Mullets. The left part is devoted to _M. barbatus_ and the right part to _M.surmuletus_. Figure 5 shows the head contours of 108 specimens belonging to nine species of Gurnards.  
Figure 4: (**a**) _Mullus barbatus_ and (**b**) _Mullus surmuletus_ specimens in the database. The images on the top show the profile of interest according to taxonomist criteria. The specimens are identified with the letter B/S plus a number.