esa-sceva/satcom-chunk-collection huggingface dataset:
# Deformation of Air Bubbles Near a Plunging Jet Using a Machine Learning Approach
## 4 Conclusions  
A truthful prediction of the deformation of air bubbles in a water volume in the presence of uncertain pressure field is an important aspect of the study of phenomena such as cavitation, air entrainment and foaming. If suitable experimental data are available, machine learning algorithms can provide powerful tools to obtain accurate predictions.  
In this study, three different models were built to predict the equivalent diameter and aspect ratio of air bubbles moving near a plunging jet. Five variants of each model were developed, varying the implemented machine learning algorithm: Additive Regression of Decision Stump, Bagging, K-Star, Random Forest and Support Vector Regression. The experimental data were obtained from measurements carried out using a shadowgraph technique.  
As for the prediction of the equivalent diameter, Model 1 provided the best predictions in most cases, but it needs a higher number of input variables, including the initial depth of the bubble. Model 2 provided results comparable to those of Model 1, but requires only the Reynolds, Weber and Eotvos numbers as inputs. The lower accuracy of Model 3 can be attributed to the absence of the number of Eotvos among the input variables. Within Model 1 and Model 2, the variant based on the Bagging algorithm led to the best results in most cases, while the variant based on SVR led to the least accurate results. On the other hand, the SVR-based variant provided the best results under Model 3.  
Regarding the forecast of the aspect ratio, the results of the three models cannot be considered fully satisfactory in general, even if, for some bubbles, the models have provided very good predictions. The unsatisfactory results can be attributed to the training dataset, which does not include an adequate number of bubbles with low aspect ratio values, and probably to the limits of the models, whose input variables do not adequately take into account the stresses acting on the surface of the bubbles.  
From what is shown in this study, it is therefore clear that a combined approach based on a shadowgraph technique and machine learning algorithms can certainly be considered to address complex problems in the study of multi-phase flows.  
Conceptualization, F.G.; methodology, F.G. and F.D.N.; software, F.G.; validation, F.D.N, F.A.P, F.D.F, G.d.M., R.G. and M.M.; formal analysis: F.G. and F.D.N.; investigation: F.G., F.D.N., F.A.P., F.D.F., G.d.M., R.G. and M.M.; data curation: F.D.N.; writing--original draft preparation, F.G. and F.D.N.; writing--review and editing: F.A.P., F.D.F., G.d.M., M.M. and R.G.; supervision: F.G. All authors have read and agreed to the published version of the manuscript.  
The work described here was conducted as part of the Italian Ministry of Education, University and Research 2017 PRIN Project ACE, grant 2017 SRH3 JY.  
The authors declare no conflict of interest.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 5 Conclusions  
* _DMY_ estimation of harvested forages in northern Sweden from Sentinel-2 data using univariate and multivariate regression models was tested in this study. The results demonstrate precise in-season _DMY_ estimation by the random forest algorithm. Multivariate models performed better than the univariate models in terms of accuracy. Using both individual band reflectances and VIs as predictor variables improved the accuracy of multivariate regression models compared to only utilizing individual bands.
* It was challenging to develop a sufficiently robust model to estimate forage _DMY_ by using Sentinel-2 data. The overfitting problem demonstrated by low model validation accuracy was the main indicator of this. The reasons may be the coarse spatial resolution and the small model training datasets. Data fusion by combining Sentinel-2 and Sentinel-1 data would be a potential way to overcome this. Furthermore, more datasets are needed for robust model building, and we therefore require continued resources and possibly international collaboration for further data collection. Nevertheless, even though model validation was slightly less accurate, the high accuracy of model calibration and evaluation showed that the selected model was promising.
* The estimated time-series of _DMY_ fitted well with the recorded harvesting dates. The methods established in this study could be used to develop a decision support system to assist farmers in making decisions on fertilization and harvest timing.  
Conceptualization, J.P., D.P. and J.M.; Methodology, J.P., D.P., J.-B.F., M.S. and J.M.; Software, J.P.; Formal analysis, J.P.; Investigation, J.P.; Data curation, N.Z. and J.M.; Writing--original draft, J.P.; Writing--review & editing, N.Z., D.P., J.-B.F., M.S. and J.M.; Visualization, J.P.; Supervision, D.P. and J.M.; Project administration, J.M.; Funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research was co-funded by Regional Agricultural Research for Northern Sweden and Swedish Farmers' Foundation for Agricultural Research, grant number #10/2018; Kempe Foundation, grant number JCK-2126.  
Data Availability StatementData will be available on request.  
Authors acknowledge the Regional Agricultural Research for northern Sweden (RJN) and the Swedish Farmers' Foundation for Agricultural Research (SLF), which funded the project: Monitoring forage fields in northern Sweden with satellite imagery (Vallsat, project ID: #10/2018) for financial support. Special appreciation is given to Kempe foundation for financial support. The authors also thank staff from the research stations at As, Lannas, Ojebyn and Robaksdalen for the help of field work. The study was supported by SITES (Swedish Infrastructure for Ecosystem Sciences), a national coordinated infrastructure, supported by the Swedish Research Council.  
The authors declare no conflict of interest.
## 4 Discussion
Nevertheless, the results suggest that even within this overlap zone, false detections can be reduced by utilizing RH and \(\Delta\)FTs thresholds. Figure 12 presents the ratio of hit and false alarm counts based on SZA (2\({}^{\circ}\) intervals), replacing \(\Delta\)FTs from Figure 11. Bins in which only false alarms occurred without any hits are shaded in gray, and their frequencies are also indicated. False detections based on SZA are most widespread during spring. Before applying stricter thresholds, they are especially prominent when the algorithm transitions from twilight to daytime conditions, specifically in the SZA range of 60-80\({}^{\circ}\). At night, frequent false alarms occur in the SZA range of  
Figure 11: Ratio distribution of false alarm counts to hit counts according to relative humidity and \(\Delta\)FTs over (**a**) inland area and (**b**) coastal area, along with the frequency distribution of relative humidity (top) and \(\Delta\)FTs (right) for each bin. The pink-shaded bins highlight zones of false alarm occurrences without corresponding hits in the GK2A fog detection. 110-150\({}^{\circ}\), which overlaps with regions of high GK2A fog detection shown in Figure 5. During twilight, however, FAR and bias are lower than in other periods. In contrast, during midday (SZA < 60\({}^{\circ}\)), fog detection frequency decreases sharply over inland regions but remains relatively high in coastal areas, likely due to the frequent occurrence of sea fog [34, 52]. Figures 11 and 12 suggest that false detections (i.e., over-detection) in the GK2A_FDA, caused by \(\Delta\)FTs and errors in daytime background reflectance, can be mitigated using ground-based observational data such as RH, regardless of region or time. To improve the GK2A_FDA using ground-based data, it is necessary to apply tailored refinement strategies for each of the six sub-algorithms (inland/coast \(\times\) day/night/twilight), utilizing variables such as RH and wind speed. These efforts should aim to reduce false detections while maintaining the POD. With recent advancements in the observational network, high-resolution objective analysis datasets at 500 m resolution have become publicly available in South Korea, offering new opportunities for the complementary use of GK2A fog products and surface observations ( [[https://apihub.kma.go.kr/](https://apihub.kma.go.kr/)]([https://apihub.kma.go.kr/](https://apihub.kma.go.kr/)) accessed on 25 May 2025.
# Magnetic Localization Method for Vehicles Based on Transformer
###### Abstract  
Magnetic positioning is a promising technique for vehicles in Global Navigation Satellite System (GNSS)-denied scenarios. Traditional magnetic positioning methods resolve the position coordinates by calculating the similarity between the measured sequence and the sequence generated from the magnetic database with criteria such as the Mean Absolute Difference (MAD), PRODuct correlation (PROD), etc., which usually suffer from a high mismatch rate. To solve this problem, we propose a novel magnetic localization method for vehicles based on Transformer. In this paper, we cast the magnetic localization problem as a regression task, in which a neural network is trained by equidistant sequences to predict the current position. In addition, by adopting Transformer to perform magnetic localization of vehicles for the first time, magnetic features are extracted, and positional relationships are explored to guarantee positioning accuracy. The experimental results show that the proposed method can greatly improve the magnetic positioning accuracy, with an average improvement of approximately 2 m.  
vehicle navigation; magnetic positioning; Transformer; neural network +
Footnote †: journal: Applied  
0000-0001-5000-5000][PERSON]  
0000-0001-5000-5000][PERSON]
# An Earthquake Early Warning Method Based on Bayesian Inference
## 3 Traditional Calculation Method for Earthquake Magnitude and PGV
### Calculation of PGV using Displacement Amplitude \(P_{d}\)  
Previous studies have reported a correlation between PGV and earthquake intensity [14; 15]. For the analysis of earthquake hazards, PGV is typically used to estimate the potential earthquake hazard in the local area [16]. Moreover, there is a strong log-linear relationship between \(P_{d}\) and PGV:  
\[PGV=\max[v(t_{i})], \tag{4}\]  
for  
\[a(t_{i})=\sqrt{{a_{\rm{EW}}{(t_{i})}^{2}+a_{\rm{NS}}{(t_{i})}^{2}+a_{\rm{UD}}{ (t_{i})}^{2}}}, \tag{5}\]  
\[v(t_{i})=\int_{0}^{\tau_{0}}a(t_{i})dt, \tag{6}\]  
where \(t_{\rm i}\) is the ith second after the arrival of the P wave; \(a(t_{\rm i})\) is the combined acceleration in all three directions at \(t_{i}\); \(a(t_{\rm i})\) is the integral of the resultant acceleration at \(t_{\rm i}\); and \(a_{\rm{EW}}(t_{\rm i})\), \(a_{\rm{NS}}(t_{\rm i})\), and \(a_{\rm{UD}}(t_{\rm i})\) are the acceleration records in the east-west, north-south, and vertical directions at \(t_{\rm i}\), respectively.  
\(P_{d}\) can be calculated as follows:  
\[P_{d}=\max[d(t_{i})], \tag{7}\]  
where \(d(t_{\rm i})\) is the displacement of the vertical earthquake record representing the displacement sequence from the initial arrival point of the P- wave to \(t_{\rm i}\). For the processed earthquake data, PGV and \(P_{d}\) at the first-three-second P- wave are used, and the log-linear relationship between \(P_{d}\) and PGV is established:  
\[lgPGV=algP_{d}+b\pm\delta, \tag{8}\]where \(a\) and \(b\) are the fitting parameters, and \(\delta\) is the standard deviation of Equation (8). Using the least-squares method, the parameters are determined as \(a=1.049\), \(b=-0.2691\), and \(\delta=0.16\).
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 5 Byproducts and Challenges  
As has been discussed in the literature [27; 37; 38] and demonstrated by trials [36], an effective in-band sensor and communication network may be an important component of a fully functional \(\overline{\text{DVO}}\) implementation. However, as also presented in the literature [37; 39], this approach has a number of critical challenges remaining. In both the \"byproduct\" and the \"challenge\" issues associated with the in-band sensor concept, important considerations and benefits have substantial merit.
# Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings
## 4 Discussion
Traditionally, installations of PV in locations other than roofs are perceived as inefficient or uneconomic and are reserved for emblematic or flagship buildings. The present study shows that, even when the energy performance is lower than traditional orientations, the economic performance is not so far from them. Considering that PV modules can replace building materials, the overall economic balance can be positive. In addition, the use of PV as shading elements in canopies can be used by architects to improve the user's comfort and the energy performance of the building by reducing cooling while increasing the energy production. These facts are relevant to the BIPV concept and can help to expand this sector. As solar PV is on the verge of its massive deployment in edifications, it is important to pay attention to the economic viability as well as its ecological and environmental footprint. A basic life cycle assessment study was performed for the residential case and the carbon balance, greenhouse gas, and energy payback times are good for all the configurations under study and in line with other studies [45; 53]. It is important to note that the analysis was carried out with consideration of the manufacturing of the PV components in a country with high emissions in electricity production (China) and the deployment in a country (Spain), with emissions as low as 1/4 compared to that of the manufacturer. Complementarily, these results help us to demystify the general rules of thumb of photovoltaic installations, such as: \"In the northern hemisphere, the solar panels have to be oriented towards the South\" and \"For maximum annual energy availability, a surface slope equal to the latitude is best\" [61]. More precisely, in an extensive numerical study [62], the optimal fixed tilt and azimuth angles are calculated for the continental USA and it is found that \"The optimum tilt was never greater than latitude tilt, but up to 10\({}^{\circ}\) less than latitude tilt\". In this reference, is also pointed that, if variable electricity prices are considered, these angles can vary. Moreover, as stated in [63], \"there is a false perception among building professionals that solar modules should be installed on ideally oriented and tilted surfaces\". The performance of systems with non-optimal orientations allows further flexibility in the installation of PVSCS in residences and industrial buildings. This is achieved without aesthetically distorting the structure and obtaining an acceptable economic benefit with an economic yield that varies only slightly compared to installations with optimal orientations. Future directions of this work will include a detailed techno-economic analysis of the industrial PVSCS with full-year data of both PV production and factory electricity consumption; systematic research of different orientations for representative, residential, commercial, and industrial users; and the development of a methodology for optimal design of PV installations in accordance with hourly-variable electricity prices and consumption patterns.
## 5 Results and Discussion
### Discussion  
[PERSON] et al. [33] suggested a computationally effective pre-distortion strategy premised on an \"adaptive partitioning neural network (APNN)\" to reduce nonlinear deficiencies in elevated UVLC technology. APNN is an acronym for adaptive partitioning neural networks. The UVLC mechanism becomes more complicated due to the distortions. Hence, this neural network is offered for the elimination of distortions. UVLC systems have employed deep neural networks to correct nonlinear deformation. In real implementations, it is crucial to prioritize the trade-off involving network intricacy and equilibrium effectiveness. In this study, [PERSON] et al. [34] propose the use of a unique hybridized frequency response assisted \"temporal convolutional neural network (TFCNN)\" with attention strategy as the final equalization in a controlled UVLC network. The nonlinear distortions present in the UVLC technology may be removed using post-equalization. [PERSON] et al. [35] introduced a \"Dual Self-Attention Network (DSANet)\" as a final equalization for the CAP-controlled UVLC technology in their research. The effectiveness of the transmission is limited by the nonlinearity influence introduced by the direct modulation/demodulation in UVLC. To solve this difficulty, [PERSON] et al. [36] created the \"convolution enhanced long short-term memory (CE-LSTM)\" neural system, which was used to improve UVLC devices' efficiency under severe non-linearity by overcoming linear and nonlinear distortion. The previous nonlinear post-equalizer using deep learning still has issues, such as how much data nodes affect the result, how much the equilibrium impact declines as the data flow increases, and how a model's complexity affects how long it takes to train.
# Influence of Macrophyte and Gut Microbiota on Mercury Contamination in Fish: A Microcosms Study
## 3 Results
### Sulfate Reducers Isolation and Hg Methylation Capacity  
Since SRP are considered to be the principal microorganisms responsible for Hg methylation, SRP strains were isolated from _Pseudancistrus_ sp. gut contents of the contaminated \"plant\" condition. The study of gene encoding 16S rRNA allowed us to affiliate 32 isolated pure strains to the _Desulfovibrionales_ order (Figure 5), and more precisely to the _Desulfovibrionaceae_ (30 strains) and _Desulfobulaceae_ (two strains) families. Concerning _Desulfovibrionaceae_, six strains (B11 to H3) were close to _Desulfovibrio aerotolerans_ (strain _DvO5_), seven strains (F7 to F2) were close to _Desulfovibrio carbinolicus_ (strain _DSM 3852_), 17 strains (G6 to G3) were close to _Desulfovibrio_ sp. _LG-2009_. The maximum bootstrap percentage was 82% (for _D. carbinolicus_), indicating that the isolated strains were not yet described in the literature. Two strains that belong to _Desulfobulaceae_ (E1, E4) were close to _Desulfobulaceae bacterium_ (strain _PR8 A05_), but considering the bootstrap percentages, they were probably also new species.  
The capacity of these 32 isolated strains to methylate Hg was tested, using the biosensor _E. coli MC 1061_. About 18.8% of isolated strains were able to methylate Hg (7 of 32 strains) at variable rates. Maximum light intensity was measured for strain G2 (103.7 \(\pm\) 25.7), followed by strain G6 (53.9 \(\pm\) 6.7), strain C3 (33.5 \(\pm\) 6.1), and strain G4 (11.5 \(\pm\) 5.0). Strains B5, A6, and H6 showed positive results and were considered as methylating strains although with a high standard deviation (6.3 \(\pm\) 2.7; 5.1 \(\pm\) 4.4; 4.4 \(\pm\) 3.8, respectively).  
SRP strains were also isolated from _Ludwigia_ sp. roots sampled in Sanguinet Lake [8] and three of them (2H7, 2F3, 3A2) are added in Figure 5. Strains 2H7 and 2F3 were close to _Desulfovibrio_ sp. (_LG-2009_), which is relatively close to 17 strains isolated from fish gut contents. Strains 3A2 were close to _Desulfovibrio carbinolicus_, like seven other strains from gut microbiota.  
Figure 5: Phylogenetic relationships based on the alignment of 16S rRNA gene sequences of strains isolated from _Pseudancistrus_ sp. gut and _Ludwigia_ sp. rhizosphere, and belong to _Desulfovibrionales_. Strains originated from the _Ludwigia_ sp. rhizosphere are underlined. Methylating strains are framed. Capacity of isolated strains to methylate Hg was estimated by the light intensity produced by the biosensor _E. coli MC 1061_. Bootstrap percentages above 50% (1000 bootstrap replicates) are indicated at the branching points. The scale bar represents nucleotide substitutions. Gene bank accession numbers are shown in parentheses. Tree was rooted with _E. coli_ (M25588).
## 5 Experimentation, Results, and Analysis
### Analysis
#### 5.4.5 Analysis of Diversity Metrics  
As shown in Table 15, the proposed DCAT model achieves the highest _Vocabulary Coverage_ (_VC_) and the lowest _Repetition Rate_ (_RR_) across all datasets. For instance, on Flickr8k, DCAT attains a VC of 0.713 and RR of 0.122, significantly outperforming NIC (VC = 0.512, RR = 0.202) and even strong baselines like AoANet (VC = 0.652, RR = 0.136). This demonstrates DCAT's ability to generate lexically rich and varied captions, enhancing the expressiveness and novelty of its outputs--an especially critical feature for low-resource languages like Tamil, where training data are limited and cultural context demands nuanced language use.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **VC** & **RR** \\ \hline \multirow{3}{*}{DCAT (Proposed)} & Flickr8k & 0.713 & 0.122 \\  & Flickr30k & 0.701 & 0.128 \\ \cline{1-1}  & MSCOCO & 0.692 & 0.132 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Diversity metrics comparison across models and datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Set** & **Average Fluency Score** & **Average Adequacy Score** \\ \hline Set 1 & 4.5 & 4.2 \\ Set 2 & 4.6 & 4.3 \\ Set 3 & 4.7 & 4.5 \\ Set 4 & 4.8 & 4.6 \\ Set 5 & 4.7 & 4.4 \\ \hline Overall & 4.7 & 4.4 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Human evaluation results for 5 sets of 100 samples.
## 1 Introduction
In contrast, the drought in Xinjiang was mainly caused by its high altitude, which resulted in insufficient precipitation, temperature, evapotranspiration, and vapor pressure [32]. Guilin, a WorldHeritage karst site located in Southwest China, has areas spanning 622 km\({}^{2}\) that have been affected by rocky desertification, and 151 km\({}^{2}\) have suffered from soil erosion. From July to November 2022, Guilin also experienced persistent drought. Its average rainfall was only 78.7 mm, which is 72.3% lower than the average for the same period in previous years, marking the lowest rainfall recorded for this period and meeting the criteria for severe hydrological drought [33]. Additionally, the southwestern region of China, where Guilin is located, experienced an exceptional temperature of 45 \({}^{\circ}\)C during that summer, the highest peak and longest duration in the country [31]. Regarding the differences in drought conditions between this southwestern region and others, [PERSON]'s [34] study attributes it to excessively high temperatures, while [PERSON] [31] believes that this is due to poor vegetation drought resistance. However, considering that Southwest China hosts the largest karst landscape in the world, this phenomenon should not disregard the specific conditions of karst areas under the impact of drought. Under the combined effect of a typical monsoon climate, unique topography, and hydrogeological structure, vegetation growth in fragile karst ecosystems is highly susceptible to drought suppression [35]. Previous studies on drought factors in the southwestern region of China have primarily focused on overall vegetation ecosystems, without specifically addressing karst ecosystems. Guilin is known for its relatively high annual rainfall with subtropical monsoon climate. However, because most of the rainfall is concentrated during the summer period, less rainfall during the dry season (autumn and winter) can result in significant periods of dryness. As there are uncertainties regarding the response mechanisms of vegetation ecosystems to extreme drought stress in this unique World Heritage karst site, therefore, this study aimed to (1) analyze the differences in the spatiotemporal characteristics of drought stress between karst and non-karst areas with respect to the TVDI, especially in 2022, (2) compare the impact on of drought on different vegetation types between karst and non-karst areas, and (3) reveal the impacts of drought stress on vegetation ecosystems in karst and non-karst areas with three vegetation indices (NDVI, LAI, and GPP). Our findings could enhance our understanding of the response and resistance of vegetation to drought stress in fragile karst areas.