esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Conclusions  
The integration of artificial intelligence (AI) agents into autonomous maritime systems marks a transformative shift in global shipping. This article has demonstrated how real-time AI agents enhance safety, efficiency, and sustainability by supporting navigation, decision-making, and operational control. Central to their role are applications in collision avoidance, anomaly detection, emergency response, and fail-safe mechanisms, all of which significantly reduce risks and enhance reliability.  
AI agents not only enable real-time situational awareness through advanced sensor fusion (LIDAR, radar, AIS, cameras) but also optimize routing, fuel consumption, and maintenance, aligning with industry goals for cost reduction and environmental responsibility. Importantly, these agents facilitate compliance with regulations such as the IMO's GHG guidelines and the Carbon Intensity Indicator (CII) by dynamically adjusting vessel operations in response to environmental and regulatory data.  
Despite their benefits, the implementation of AI agents poses challenges. Technical limitations--such as processing power and latency--necessitate edge computing and modular AI architectures. Ethical and legal uncertainties require frameworks for accountability and transparency. Interoperability and human oversight remain essential to ensure seamless integration with maritime infrastructure.  
Future directions lie in deploying advanced AI models--such as deep reinforcement and federated learning--to enhance adaptability, resilience, and coordination across fleets. Integration with smart port systems, scalable AI architectures, and robust cybersecurity will be critical for widespread adoption. Addressing unresolved issues in ethics, standardization, and extreme scenario resilience will further shape the path ahead.  
In summary, AI agents are key enablers of next-generation maritime autonomy. Through continuous innovation, interdisciplinary collaboration, and regulatory alignment, they promise a safer, smarter, and more sustainable future for global maritime transportation.  
Conceptualization, T.M., I.D., E.K., P.K. and W.S.; methodology, T.M., I.D. and E.K.; investigation, T.M., I.D. and E.K.; resources, T.M., I.D., E.K., P.K. and W.S.; writing--original draft preparation, T.M., I.D., E.K., P.K. and W.S.; writing--review and editing, T.M., I.D., E.K., P.K. and W.S.; visualization, T.M.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No additional data were created.  
The authors declare no conflicts of interest.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 3 Proposed Hardware Architecture
### Arithmetic Logic Unit
#### 3.5.2 Booth Encoding  
Booth encoding is a technique used to reduce the number of bit shifts required in a multiplication operation [35]. In a standard multiplication, each bit in the multiplier affects multiple bits in the product, requiring multiple shifts. Booth encoding groups together the bits in the multiplier that affect the same bits in the product, allowing for fewer shifts and therefore faster multiplication. When used in a Montgomery radix-4 multiplier, Booth encoding can further improve the efficiency of the multiplication operation.  
In Booth encoding, each digit is assigned a weight based on its significance in the final result. The weights are typically \(-2\), \(-1\), \(0\), \(1\), and \(2\), which represent the number of times the multiplicand needs to be added or subtracted to the partial product. The Booth encoded representation is then used to perform the multiplication operation using a series of additions and subtractions, rather than multiple bit shifts [36]. This reduces the number of operations required, making multiplication faster and more efficient. Booth encoding with weights of \(-2\), \(-1\), \(0\), \(1\), and \(2\) is a technique used to reduce the number of bit shifts required in a multiplication operation, improving its efficiency and speed [37]. The use of Booth encoding in a Montgomery radix-4 multiplier provides a highly efficient multiplication operation. Booth function for radix-4 is represented by the following equation:  
\[-2X_{p}(n+1)+X_{p}(n)+X_{p}(n-1) \tag{5}\]  
By using Equation (5), the following results are computed, which are described in Table 2. Table 2 provides information related to partial products. The first column of Table 2 is \(X_{p}\), while the second to ninth columns define the maximum possible values of \(X_{p}\). Based on the \(X_{p}\) values, this will produce the result \(0\), \(Y\), \(-Y\), \(2Y\) and \(-2Y\).
## 5 Example Cases
### Example Case 1--Large-Scale Plant with Constant Steam Demand and High Temperature  
Case 1 represents a very large industrial facility with a constant steam demand of 1200 t/h which corresponds to about 900 MW. Steam needs to be supplied at 200 \({}^{\circ}\)C and can be produced at 300 \({}^{\circ}\)C saturated steam. The facility is located near the Equator and thus the year is split into dry season and wet season, which is reflected in the electricity prices as a large share of the power production is based on hydropower. For each season, one representative week was selected and was repeated for half-a-year. Energy prices for the two representative weeks are presented in Figure 8.  
The cost structure for all considered storage types is presented in Figure 9 considering the thermal requirements of Case 1. For the LHTS with KNO\({}_{3}\)-NaNO\({}_{3}\) as a PCM at 1000 \(\upmu\)/m\({}^{3}\), the storage material costs dominate the overall costs for each application area. Concrete storage shows a similar cost structure however, storage material costs make up for a lower share of total costs. For both LHTS and concrete storages the share of tube costs increases with heat loads for both storage types since larger heat transfer areas are required. Costs for Ruths storages are dominated by vessel costs which make up for more than 85% of the overall costs for each dimensioning range. In contrast to the other storage types where valve costs are negligible, valve costs for Ruths add up to about 10%. Similar to LHTS and concrete storage the storage material costs dominate the overall costs for molten salt storage with a share of over 85%, followed by vessel costs in all dimensioning ranges. All other cost drivers combined are in the range of <5%.  
The optimal system for Case 1 is shown in Figure 10 and is summarized in Table 4. It consists of an electric boiler with a maximum load of 1.70 GW for steam generation and a concrete storage with a capacity of 40.75 GWh and a maximum heat load of 0.93 GW. Investment costs for the electric boiler and the concrete storage system are 426.14 M\(\upmu\) and 433.49 M\(\upmu\), respectively. Annual energy costs for the optimal electr  
to 311 k\(\epsilon\)/y, compared to energy costs of 476 k\(\epsilon\)/y without storage. The costs without storage consider steam production using electric boilers. This results in a saving potential of energy costs of 34.7%.  
Figure 16 shows a small cutout of the heat load profiles for all components in the P2H system for Case 2. In times of low electricity prices, the electric boiler is used to charge the LHTS, whereas the HTHP is used at more constant heat loads throughout the entire period. The concrete storage seems to be used to reduce peak heat loads of the LHTS.
# A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
## 4 Bubble Growth and Detachment Model
### Bubble Growth in Non-Uniform Boiling  
The growth of bubbles in non-uniform boiling is more complex and unpredictable than the situation in uniform boiling. The radius \(R\) is mostly proportional to \(t^{0.5}\) in most of the above-discussed correlations, while the time exponent can be less than 0.5 in non-uniform boiling, e.g., [PERSON] et al. [74] measured the growth of bubbles during a saturated-boiling experiment with n-pentane and ether at atmospheric pressure and noticed that the radius of the bubbles conforms to the relation \(R\propto t^{n}\), where n takes values in the range of 0.19-0.475. The main difference in the modeling of bubble growth during the asymptotic stage of non-uniform boiling lies in the assumptions made on the heat-transfer mechanism of the bubbles. The researchers assumed two main mechanisms, one in which the increase in bubble mass originates mainly from the evaporation of liquid in the micro-fluid layer below the bubble, and the other attributed to the evaporation of the thermal boundary layer around the bubble surface.  
It is now generally accepted that bubble growth is attributed to the action of the micro-fluid or thermal boundary layer, as shown in schematic Figure 12. In Figure 12a, bubble growth is driven by evaporation from the boundary layer carried by the bubbles from the heated surface. In Figure 12b, the bubbles protrude outside the wall's thermal boundary layer (WTBL) and grow from microlayer evaporation. The mechanism in Figure 12c is similar to that in Figure 12a, except that the thermal boundary covers only a part of the bubble. In Figure 12d, the growth of the bubble comes from the combined effect of the micro-fluid layer and the thermal boundary layer around the bubble.  
[PERSON] et al. [66] concluded that models based only on micro-fluid layer evaporation are insufficient to explain bubble growth, and can only reach good agreement with the results under certain specific experimental conditions after a detailed evaluation of several mathematical models of the micro-fluid layer. Bubble growth is mainly promoted by evaporation from the thermal boundary layer around the bubble, and the effect of the micro-liquid layer is relatively small for some fluids such as water.
## 4 Performance Evaluation
### Packet Delivery Ratio  
Figures 9 and 10 show the results in terms of delivery ratios at the sink node. Similarly, HMC-MAC achieves better delivery ratios than other protocols during high data rate and burst traffic.  
Figure 9: Delivery ratio at the sink for periodic traffic.
## 2 System and Channel Model
### Joint Turbulence and Pointing Errors Effects  
Considering the combined impact of both turbulence-induced and misalignment induced fading, the joint PDF for the normalized irradiance \(I_{\{i,j\}}\), is obtained by the following integral [7; 17]:  
\[f_{\{i,j\}}\left(I_{\{i,j\}}\right)=\int f_{\{i,j\}\left|I_{a_{i}\{i,j\}}} \left(I_{\{i,j\}}\right)\left|I_{a_{i}\{i,j\}}\right.\right)f_{I_{a_{i}\{i,j\} }}\left(I_{a_{i}\{i,j\}}\right)dI_{a_{i}\{i,j\}} \tag{9}\]  
where \(f_{\{i,j\}\left|I_{a_{i}\{i,j\}}\right.}\left(I_{\{i,j\}}\right)\left|I_{a_{i} \{i,j\}}\right.\) is the conditional probability given, \(I_{a_{i}\{i,j\}}\), either for G-G or NE distribution, Equations (3) or (5), respectively.  
Thus, by using (3) (Equation (07.34.03.060.501) in [30]) and by performing the analysis in [7] we obtain that for weak to strong G-G modeled turbulence, Equation (9) is written as [7; 31]:  
\[\begin{array}{ll}f_{\text{G}G,I_{\{i,j\}}}\left(I_{\{i,j\}}\right)&=\frac{ \alpha\beta\varphi_{\{i,j\}}^{2}}{A_{0,\{i,j\}}\Gamma^{\{\alpha\}}\Gamma\{ \beta\}}\\ &\times\text{G}_{1,3}^{3,0}\left(\frac{\alpha\beta I_{\{i,j\}}}{A_{0,\{i,j\}} \mathcal{S}_{\{i,j\}}}\right)\left|\begin{array}{c}\psi_{\{i,j\}}^{2}\\ \psi_{\{i,j\}}^{2}-1,\ \alpha-1,\ \beta-1\end{array}\right.\end{array} \tag{10}\]  
where \(G[.]\) denotes the Meijer function, (Equation (5) in [32]) that is a standard built in function which can be evaluated with most of the well known mathematical software packages and can be transformed to the familiar hypergeometric functions [32].  
Similarly, for saturated turbulence conditions, by using (5), Equation (11) in [32] and by following the same analysis performed in [7], we obtain that (9) can be written according to [33], as  
\[\begin{array}{ll}f_{NE,I_{\{i,j\}}}\left(I_{\{i,j\}}\right)&=\frac{\psi_{\{i, j\}}^{2}}{A_{0,\{i,j\}}\delta\{i,j\}}\\ &\times G_{1,2}^{2,0}\left(\frac{I_{\{i,j\}}}{A_{0,\{i,j\}}\delta\{i,j\}} \left|\begin{array}{c}\psi_{\{i,j\}}^{2}\\ \psi_{\{i,j\}}^{2}-1,\ 0\end{array}\right.\end{array}\right)\end{array} \tag{11}\]  
Moreover, the instantaneous electrical SNR at the receiver through \(j\)th hop of the \(i\)th path is expressed as \(\gamma_{\{i,j\}}=2P^{2}\eta^{2}I_{\{i,j\}}^{2}/\sigma_{n}^{2}\) with the corresponding average electrical SNR being \(\mu_{\{i,j\}}=2P^{2}\eta^{2}\left(E\Big{[}I_{\{i,j\}}\Big{]}\right)^{2}/\sigma_ {n}^{2}\), where \(P\) denotes the average signal power at the receiver's side and \(E[I_{\{i,j\}}]\) is the corresponding expected normalized irradiance value, [15; 17; 22] which for both G-G and NE turbulence distributions is given according to [33; 34; 35], as:  
\[E[I_{\{i,j\}}]=A_{\psi,\{i,j\}}=A_{0,\{i,j\}}g_{\{i,j\}}\Big{(}1+\psi_{\{i,j\}} ^{-2}\Big{)}^{-1} \tag{12}\]
# Multilevel Regularization Method for Building Outlines
## 2 Materials and Methods
### Overall Regularization of Building Outlines Using Hausdorff Distance and MBR  
The Hausdorff distance algorithm measures the similarity between two sets of sample points in space by calculating the distance between them [22]. Because vectorized building outlines can be represented as ordered sets of points in space, the Hausdorff distance algorithm can be used to measure the dissimilarity between the outline of a building and its MBR. By setting a threshold, parts of the original building outline with Hausdorff distances smaller than the threshold are superseded using corresponding line segments on the MBR, achieving the partial regularization of the building outline.
## 2 Materials and Methods
### Extraction and Determination of Carotenoids
#### 2.4.3 Identification and Quantification  
The most relevant carotenoids were identified by comparison of UV-visible spectra with those of commercially available standards, \(\beta\)-carotene (Sigma 95%, synthetic,) (C-9750), lutein (Sigma 70%, from alfalfa) neoxanthin (0234.1) from CaroeNature GmbH (Erlenauweg 17, 3110 Munsingen, Switzerland), matching also different information such as position of absorption maxima (\(\upl_{\max}\)) and the degree of vibration fine structure (% III/II) (Table 1) [34].  
Quantification of individual compounds was done by calibration curves using the respective standards for lutein and \(\beta\)-carotene with R\({}^{2}\) = 0.9997 and R\({}^{2}\) = 0.9991, respectively, whereas neoxanthin was expressed as lutein equivalent because of the unavailability of a fresh neoxanthin standard. The results were expressed in terms of concentration (mg kg\({}^{-1}\) of berries) and content (\(\upmu\)g berry\({}^{-1}\)) to avoid an overestimation of the changes that may be the result of an altered berry surface area to volume ratio.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound** & \(k^{\prime}\) & \(\upl_{\max}\) **(nm)** & **\% (III/II) a** \\ \hline (9\({}^{\circ}\)\(Z\))-Neoxanthin & 4.38 & 414; 436; 464 & \\ (all-\(E\))-Lutein & 7.23 & (422); 445; 472 & 42 \\ \(\beta\)-Apo-8\({}^{\prime}\)-carotenal & 9.28 & 460 & \\ \(\beta\)-Carotene & 11.82 & (428); 452; 478 & 25 \\ \hline \hline \end{tabular} a \% III/II is the ratio of the height of the longest-wavelength absorption peak, designated III, and that of the middle absorption peak, designated as II, taking the minimum between the 2 peaks as baseline.  
\end{table}
Table 1: High Performance-Liquid Chromatography (HPLC)/DAD characteristics of carotenoids found in Nebbiolo grapes.
# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 2 Background and Motivation  
Solar PV generation increased by a record 270 TWh (up to 26%) in 2022, reaching almost 1300 TWh. It demonstrated the largest absolute generation growth of all renewable technologies in 2022, surpassing wind for the first time in history. This generation growth rate matches the level envisaged from 2023 to 2030 in the Net Zero Emissions by 2050 Scenario [13].  
The worldwide growth of photovoltaics is extremely dynamic and varies strongly by country. In 2022, the leading country for solar power was China, with about 393 GWp, accounting for nearly two-fifths of the total global installed solar capacity, as the total global solar capacity reached 1 TWh. As shown in Figure 2, Turkiye ranks 14 th.  
In 2022, fossil fuels, notably natural gas, continued to dominate Turkiye's energy generation landscape. Additionally, hydroelectric power plants, categorized as renewable energy sources, significantly contribute to electricity generation. Over the past two decades, there has been a steady increase in both overall electricity generation and the capacity for renewable energy generation including solar energy as well. In other words, solar energy is a very new energy source for our country, and it also leads to the development of technology and ease of use in this field. One of the most important eases of use in question is the locally and nationally developed systems for maintenance and repair.  
Today, due to the growing capacity of PV systems, there has been an increase in the power conversion units, monitoring systems, communication equipment, and protection devices being added to PV installations [37]. As a result, massive PV data, both instantaneous and historical, become available. Various PV data are available from PV arrays, PV inverters, and the utility grid. IoT technology has various benefits for PV monitoring, predictive maintenance, and real-time data collection. Reliability and maintenance are just the two downsides that have been stated; there may be other issues as well.  
To overcome this and also maintenance challenges, the use of UAVs and thermography is increasing. Thermal imaging allows for non-intrusive assessment of the temperature distribution across PV panels, aiding in identifying hotspots and inefficient regions that may impact overall efficiency. Anomalies and defects in PV cells can result in localized heating.  
Thermal imaging helps in identifying these irregularities, enabling timely maintenance and repair. During manufacturing and installation, thermal imaging can ensure uniform temperature distribution and detect potential defects, contributing to higher-quality PV panels. Continuous thermal monitoring provides insights into panel performance, facilitating predictive maintenance and reducing downtime. Drones equipped with thermal cameras provide a larger-scale view of PV arrays, enabling efficient inspection and monitoring.
## 6 Conclusions  
This paper developed an alpine vegetation photosynthesis model for estimating the net primary productivity under the framework of the MOD17. This model introduced a soil moisture content index, which used the exponential function of minimum relative humidity and maximum vapor pressure deficit to estimate the impact of water on plant photosynthesis. Using NDVI, LAI, solar radiation, specific humidity, maximum and minimum temperatures, land use, and elevation, the net primary productivity of the TRH region from 2012 to 2019 was estimated. The results showed the AVPM had higher accuracy and significantly outperformed the MOD17. Compared with the MOD17, the average bias of the AVPM was \(-9.8\) g\(\mathrm{cm^{-2}yr^{-1}}\), which was reduced by 91.8%; the average MAPE was 57.0%, which was reduced by 68.2%; the average r was 0.4809, which was improved by 30.0%. The improvement in the NPP estimation was mainly attributed to the decreasing estimation of the water stress coefficient on the NPP, which was considered the higher constraint of water impact on plant photosynthesis. Therefore, the newly introduced model can accurately estimate the NPP in the TRH region. This is of great help for assessing the vegetation growth conditions of alpine ecosystems against the background of grassland degradation and black soil beach management. However, limited by RS resolution, the growth conditions of small-plot artificial grassland cannot be accurately estimated, which is important to quantify the effectiveness of black soil beach management. Therefore, in follow-up research, the model needs to improve the resolution of the derived RS data due to spatial heterogeneity, which is of great importance for the assessment of the growth condition of small plots of artificial grassland.  
Conceptualization, W.L.; methodology, W.L., Y.L. and R.L.; software, W.L.; validation, Y.L., R.L. and Y.J.; formal analysis, W.L.; investigation, W.L.; resources, W.L.; data curation, W.L., Y.L. and Y.J.; writing--original draft preparation, W.L.; writing--review and editing, W.L. and Y.Y; visualization, W.L.; supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. U2243206) and the National Key Research and Development Plan of China (Grant No. 2022 YFC3800803, Grant No. 2021 YFD1300501). This research was sponsored by the Ministry of Science and Technology of China.  
The authors do not have permission to share the data.
## 3 Results
### Time-Domain HRV Measures  
Mixed ANOVA analyses did not show any statistically significant interactions between group and assessment time in any of the measures (\(p>0.05\)). However, there were main effects of time in SDSD (\(F(2,\,32)=3.674\), \(p=0.037\), _partial_\(\eta^{2}=0.187\)), SDNN (\(F(2,\,32)=4.237\), \(p=0.023\), _partial_\(\eta^{2}=0.209\)), RMSSD (\(F(2,\,32)=4.303\), \(p=0.022\), _partial_\(\eta^{2}=0.212\)), and a marginally important effect of time in pNN50 (\(F(2,\,32)=3.019\), \(p=0.063\), partial \(\eta^{2}=0.159\)). Pairwise comparison results, with mean differences (_MD_), showed that, regardless of the group, there were significant reductions, measurable in milliseconds (ms), from the baseline to the mid-term intervention in SDSD (\(MD=0.059\) ms, \(p=0.033\)), SDNN (\(MD=0.066\) ms, \(p=0.037\)), and RMSSD (\(MD=0.079\) ms, \(p=0.044\)). No other statistically significant differences were found between the mid-term and the end of the intervention assessments, or between the baseline and the end of the intervention assessments for those measures. In pNN50, pairwise comparison results did not reveal any statistically significant differences between time conditions (\(p>0.05\)). Overall, these tendencies (i.e., a significant reduction in scores between the baseline and mid-term) were \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Group 1 (\(n=9\))** & **Group 2 (\(n=9\))** & \multirow{2}{*}{**\(t\)-Test**} & \multirow{2}{*}{\(p\)**} \\  & \(M\) (SD)** & & \(M\) (SD) & & \\ \hline Age (years) & 48.20 (7.28) & 47.22 (10.40) & 2.39 & 0.814 \\ Education (years) & 12.40 (0.246) & 14.33 (3.54) & \(-\)1.396 & 0.192 \\ Body mass index (kg/m\({}^{2}\)) & 25.92 (3.45) & 26.25 (6.13) & \(-\)0.142 & 0.889 \\ IPAQ—physical exercise (METS) & 887.15 (691.09) & 1791.87 (1109.32) & \(-\)2.123 & 0.069 \\ GAD-7—anxiety symptoms & 9.10 (5.63) & 11.75 (5.65) & \(-\)0.991 & 0.336 \\ PHQ-9—depressive symptoms & 14.50 (5.21) & 15.38 (5.53) & \(-\)0.345 & 0.735 \\ MoCA-Global cognition & 25.80 (3.19) & 25.73 (2.15) & 0.063 & 0.951 \\ \hline \hline \end{tabular} Note: Group 1 (reduced multimodal training), group 2 (extended multimodal training). Mean (\(M\)) and standard deviation (\(SD\)). International Physical Activity Questionnaire (IPAQ). METS = Metabolic equivalent, Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9) and Montreal Cognitive Assessment (McCA).  
\end{table} 
Table 1: Descriptive results, including the demographic and clinical characteristics of both groups.