esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A One-Time Pad with True Randomness
Figure 1: Diagram depicting key agreement between \(P_{1}\) and \(P_{2}\) from the private inputs \(x_{1}\) (in red) and \(x_{2}\) (in green) by using \(P_{3}\) (in blue) as an auxiliary unit in the SMPC protocol. The total sum is \(K=s_{1}+s_{2}+s_{3}=x_{1}+x_{2}+x_{3}\), where \(x_{3}\) is random and cannot be traced by observation of the communication. Our newly developed protocol employs a blend of secure multiparty computation (SMPC) and one-time pads (OTPs), effectively tackling the challenges of key agreement and the demand for cryptographically effective TRNGs. Instead of solely relying on TRNGs, employing cryptographically strong pseudorandom number generators (CSPRNGs) is a feasible substitute. An upcoming paper will detail a hybrid method where CSPRNGs, known for their faster and more dependable random number generation than TRNGs, are incorporated into an SMPC setting. This integration ensures that our protocol achieves the necessary randomness for cryptographic security and the practicality for application in real scenarios. This comprehensive method resolves the critical issue of key agreement in multiparty systems, guaranteeing secure, efficient, and dependable cryptographic processes.
## 3 Ground Dataset  
### Ground-Based Observations for Validation (GBOV) Database  
The CGLS GBOV [53] initiative aims at facilitating the use of observations from operational ground-based monitoring networks and their comparison to Earth Observation (EO) products. In the case of the SA, GBOV provides the Reference Measurements (RMs) and upscaled Land Products (LPs) based on the RM data [54; 55] from over 24 sites coming from different networks, such as BSRN, FLUXNET, or SURFRAD. Currently, the GBOV database is made available for the 2012-2018 period [53]. The RM GBOV data corresponds to blue-sky albedo, which is defined as the fraction between the downward and the upward shortwave radiative flux. The diffuse fraction is also included in the RM dataset.  
The innovative approach for the generation of GBOV LPs could be very useful for the validation of satellite SA products since it can be applied to both homogeneous and heterogeneous land surfaces. However, we can expect large discrepancies over heterogeneous sites compared to homogeneous sites [54; 56]. The CEOS LPV albedo validation protocol [21] recommends the use of ground values measured at the flux tower (i.e., the RM) for the direct validation of EO products. For those reasons, GBOV RMs are used in this study for the accuracy assessment. Note that RMs are provided daily in the GBOV database with a typical temporal step of 30 or 1 min depending on the station. The estimation of the uncertainty associated with a solar radiation measurement by a commercial pyranometer is around 5% (at a 95% confidence level) for daily values under ideal conditions [57]. Since satellite products are defined to provide SA retrievals at solar local noon, the RMs used in this study have been taken at the same time. For this study, the concomitant period to PROBA-V was used (i.e., 2014-2018). The main characteristics of the 20 GBOV sites providing the RMs at solar local noon for the period under study are summarized in Table 4.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Experimental Setup  
We performed experiments based on a computer simulation to investigate the performances of the ML algorithm candidates. The simulation followed the methodology illustrated in Figure 10.  
First, we explored variables within the dataset for their correlations, characteristics, and anomaly types. Then, we manually categorized each observation point according to the anomaly definition for evaluation purposes. We applied some pre-processing techniques to the entire dataset using the following steps. By this point, we expected the data formed a distinct pattern or region density for each class. Afterwards, we randomly split the dataset  
Figure 10: Experiment methodology.  
into a training set (70%) and a test set (30%) to prepare for the ML modelling stage. The ML model learned from the training set to generate a decision function. The training stage also introduced optimization, or tuning, for some variables within the ML model. Finally, we validated the optimum ML model with the test set and evaluated its performance.
## 2 Results and Discussion
### Gas Chromatography Fingerprint  
The separation and identification of the mangoes' VOCs were performed using GC-MS. A total of 123 VOCs was identified based on the comparison of mass spectra of unknown with the NIST library. Figure 3 shows the chromatograms of Harumanis mangoes from three sources in Perlis. All chromatograms showed similar pattern with three significant chemical markers that can only be found in Harumanis mangoes which were _beta_-ocimene, _trans-beta_-ocimene, and _allo_-ocimene at these retention times 11.144 min, 11.418 min, and 13.874 min, respectively.  
Figure 3: Chromatograms of the real Harumanis mango in three locations with three marker compounds. (a) _beta_-ocimene, (b) _trans-beta_-ocimene and (c) _allo_-ocimene.  
Figure 2: Acceptance test of the parameters for (**a**) whole Harumanis and Tong Dam samples and (**b**) slices of Harumanis and Tong Dam samples, evaluated using a 9-point hedonic sensory evaluation. Different letters indicate significant differences of mean concentrations (\(p\leq 0.05\)) between the two mango cultivars.  
Two of these chemical markers, _beta_-ocimene and _trans-beta_-ocimene, had been reported in a previous study [13]. Meanwhile, Figure 4 showed the comparisons of the chromatograms for the three varieties of mangoes. Surprisingly, the chromatograms for both of unauthentic Harumanis mangoes, Tong Dam and Susu, showed similar pattern, whereas the chromatogram of the authentic Harumanis showed a significantly different pattern. In addition, the three chemical markers of Harumanis were not found in either Tong Dam or Susu mangoes.
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 7 Conclusions and Future Directions  
This study provides a detailed comparative analysis of 58 lightweight block ciphers, examining their internal structures, hardware efficiency, and security trade-offs. The findings demonstrate that lightweight cipher design is inherently multi-objective, requiring careful balancing of area, performance, and cryptographic strength. No single cipher dominates across all metrics; rather, each reflects distinct design priorities shaped by application-specific constraints.  
Recent trends in cipher development reveal a growing emphasis on tweakable, involutive, and hybrid designs--highlighting a shift toward more flexible and implementation-aware cryptographic primitives. These architectures not only support efficient deployment in constrained environments but also introduce structural enhancements that can improve resilience against evolving attack vectors.  
For researchers, this survey offers a benchmark for identifying design gaps and performance bottlenecks in existing ciphers. For practitioners in the IoTs domain, it serves as a practical reference for selecting appropriate lightweight cryptographic solutions based on specific resource, latency, or interoperability needs. Future research should focus on comprehensive platform-based benchmarking, rigorous evaluation of security margins, and the integration of lightweight cryptography into broader secure communication frameworks, including post-quantum and adaptive IoTs ecosystems.  
Conceptualization, S.S. and R.M.; methodology, S.M.A.-N.; software, S.M.A.-N.; validation, S.S. and R.M.; formal analysis, S.M.A.-N.; investigation, S.M.A.-N.; resources, S.M.A.-N.; data curation, S.S. and R.M.; writing--original draft preparation, S.M.A.-N.; writing--review and editing, R.M.; supervision, S.S. and R.M.; project administration, S.S. and R.M.; funding acquisition, S.M.A.-N., S.S. and R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank the editor and the anonymous reviewers, whose insightful comments and constructive suggestions helped us to significantly improve the quality of this paper. Additionally, we would like to acknowledge the assistance provided by ChatGPT (4o model, GPT-4-turbo) in refining the grammar and enhancing the readability of this paper.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 5 Energy Cost Analyses for PV-Powered Charging Station with V2G Service
Moreover, in the \"sim w/ opti\" scenario, the amount of PV energy used for charging EV1 is significantly higher than in the \"sim w/o opti\" scenario, as shown in case 1. Given that EV2 is charging in fast mode, the primary source of its charging power is the public grid. On the other hand, because EV4 is charged in average mode, it depends on PV and stationary storage energy. Similarly, because EV5 is charged in slow mode, it is mainly charged by PV sources. However, in case 2, EVs require frequent charging from the public grid due to the fluctuation of PV power. V2G service can improve the energy efficiency of EVs by allowing the EV batteries to be used as a source of energy for the grid during times of high grid demand (and/or high-tariff, low-renewable production) and then recharge from the grid during times of low grid demand (and/or low-tariff, high-renewable production). By providing energy back to the grid, EVs can help balance the electrical load, which can improve the overall efficiency and reliability of the distribution grid. As for the EV users, V2G can provide a way to earn revenue by selling energy stored in their EV batteries back to the grid during peak times. Additionally, the EV batteries can be charged with clean and low-cost renewable resources (e.g., photovoltaics) and can be discharged later to the grid at high-grid-consumption moments via V2G; thus, the electricity grid can use local renewable production more efficiently.
## 3 Results and Discussion
### Deployment and Optimization of Advanced Energy Storage Systems
#### 3.3.4 Research on Hybrid Storage Systems and Circular Economy Approaches  
Recent research underscores the growing interest in hybrid energy storage systems, which combine multiple technologies to capitalize on their complementary characteristics [22; 47]. For instance, coupling fast-response BESSs with high-capacity hydrogen or PHS systems enables microgrids to manage both short-term fluctuations and long-term energy balancing requirements effectively [23; 27].  
In parallel, circular economy strategies are being actively explored to minimize the environmental impact of storage technologies. These include battery recycling programs, second-life applications for retired batteries, and the design of modular, easily recyclable storage units [48; 61].  
Future research should focus on developing integrated frameworks that combine hybrid storage optimization with circular economy principles, ensuring both economic and environmental sustainability [34; 42]. Establishing policy incentives and regulatory frameworks to support the deployment of hybrid systems and promote sustainable materials management will be critical for scaling advanced energy storage solutions in microgrid environments [43; 70].
## 2 System Reliability Evaluation Method of a Single Failure Criterion
### System Reliability Estimation Based on Multivariate Copulas  
According to the three commonly used bridge failure criteria (failure of any one component, failure of any two adjacent components, and failure of any three adjacent components), methods for evaluating the system reliability based on copulas are respectively proposed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Copula** & **Copula Function \(C(u_{1},u_{2},\ldots,u_{d}|\theta)\)** & **Generator Function \(\varphi(t)\)** & **Range of \(\theta\)** \\ \hline Clayton & \(\left[\left(\sum\limits_{i=1}^{d}u_{i}^{-\theta}\right)-d+1\right]\) & \(\frac{1}{\theta}(t^{-\theta}-1)\) & \((0,+\infty)\) \\ [PERSON] & \(\exp\[PERSON]{\{}-\left|\sum\limits_{i=1}^{d}\left(-\ln u_{i}\right)^{\theta} \right|^{1/\theta}\[PERSON]{\}}\) & \((-\ln t)^{\theta}\) & \([1,+\infty)\) \\ [PERSON] & \(-\frac{1}{\theta}\ln\left[1+\frac{\prod_{i=1}^{d}e^{-u_{i}-1}}{(e^{-\theta}-1 )^{d-1}}\right]\) & \(-\ln\frac{e^{-\theta}-1}{e^{-\theta}-1}\) & \((-\infty,+\infty)\backslash\{0\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the adopted Archimedean copulas.
## 1 Introduction
Failure to account for the varying quantities of different fault types during model training limits the ability to handle the randomness of fault types and quantities in real-world scenarios. 2. The lack of high-precision lightweight models for fault diagnosis of one-dimensional signals in rotating machinery limits their application in real-time monitoring within prognostics and health management (PHM) systems. 3. Traditional oversampling methods struggle to generate enough high-quality samples near class boundaries, increasing the risk of misclassification around decision boundaries. To address these challenges, this paper introduces the KDE-ADASYN algorithm, which utilizes a Gaussian kernel function to balance the data. Additionally, a lightweight 1D convolutional neural network with an attention mechanism is proposed, designed to enhance diagnostic performance while minimizing computational resource usage. The main contributions of this research are as follows:  
1. The Gaussian kernel-based KDE-ADASYN algorithm calculates the probability density distribution and weight of each minority class sample, adjusts the number of new samples generated near various minority class boundaries, and improves the classifier's decision boundary accuracy in complex imbalanced data scenarios. 2. A lightweight 1D CNN for fault diagnosis is developed, incorporating MobileNet with an embedded SE block. The attention mechanism enhances feature extraction, and the lightweight network drastically reduces model parameters and conserves computational resources, making it ideal for mobile machinery and real-time monitoring applications. 3. Training sets with varying sample sizes for different fault types are constructed using the PU and HUST datasets, addressing the challenge of model applicability in real-world conditions. The remainder of this paper is organized as follows: Section 2 provides a detailed explanation of the proposed KDE-ADASYN-based MobileNet with SENet (KAMS) model. Section 3 focuses on the experimental validation and the analysis of the results. Finally, Section 4 summarizes the conclusions of this paper.
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 6 Numerical Simulation Results Analysis
### Impact of SNR on Estimation Results  
The SNR of the received data is a critical factor influencing the radiated noise estimation results. It can be altered by adjusting the power of the radiated noise. The power of the radiated noise varies within the range of 70 to 110 dB, and the corresponding SNR of the received data is shown in Figure 2. Furthermore, the estimation results and estimation variances through different methods are shown in Figure 3.  
Figure 1: The sketch of a typical shallow-water waveguide environment that is used in the following simulations.  
Figure 2: The SNR of the received data corresponding to different radiated noise power.  
It can be seen that the SNR of the received data varies linearly with the radiated noise power. The estimation errors of different methods decrease as the radiated noise power increases, and the estimation variances tend towards CRLB with the increasing power. Among these methods, the CBF method exhibits the highest estimation error and variance, primarily due to the failure of the far-field assumption at close ranges. Consequently, the NFBF method effectively reduces the estimation error caused by range mismatch compared to CBF, but its measurement error remains relatively large. The MFP method achieves the CRLB in terms of estimation variance under different radiated noise power, while its estimation error is relatively larger at lower radiated noise level due to the influence of ambient noise. The RPCA method reduces the impact of ambient noise through denoising, thereby decreasing the estimation error, but its estimation variance remains higher than CRLB. In contrast, the MLE method is least affected by variations in the radiated noise power, providing the most accurate estimation results. Moreover, its estimation variance reaches the CRLB, demonstrating the optimal estimation performance.
## 3 Results  
### Spatio-Temporal Variations of Precipitation and Air Temperature  
Climate change showed a warming and wetting trend in the SRYR at annual and seasonal scales from 2001 to 2019. The average annual precipitation and air temperature were 545.2 \(\pm\) 64.7 mm and 1.15 \(\pm\) 0.39 \({}^{\circ}\)C, respectively. Both annual precipitation (Figure 2a) and air temperature (Figure 2b) had significantly increasing trends (\(p<0.05\)) at rates of 6.37 mm yr\({}^{-1}\) and 0.043 \({}^{\circ}\)C yr\({}^{-1}\), respectively. The average precipitation and air temperature in spring were, respectively, 107.1 \(\pm\) 19.7 mm and 1.55 \(\pm\) 0.56 \({}^{\circ}\)C, with significantly increasing trends at rates of 2.4 mm yr\({}^{-1}\) (\(p\) < 0.01) (Figure 2c) and 0.034 \({}^{\circ}\)C yr\({}^{-1}\) (\(p\) < 0.05) (Figure 2d), respectively. The average precipitation and air temperature in autumn were 125.2 \(\pm\) 24.9 mm and 1.24 \(\pm\) 0.66 \({}^{\circ}\)C, respectively. The autumn temperature also showed a significantly increasing trend (\(p\) < 0.05), with a rate of 0.065 \({}^{\circ}\)C yr\({}^{-1}\) (Figure 2h), while autumn precipitation showed an insignificantly increasing trend (\(p\) > 0.05), with a rate of 1.54 mm yr\({}^{-1}\) (Figure 2g). The average precipitation and temperature in summer were 297.6 \(\pm\) 46.1 mm and 10.84 \(\pm\) 0.62 \({}^{\circ}\)C, respectively. There were insignificant and increasing trends for both summer precipitation (+2.
## 4 Experimental Analysis
### Experiment #3  
In the final experiment, our inquiry was directed towards ascertaining the potential enhancements in recognition performance attainable through the application of the proposed preprocessing methodology, predicated upon the trained DCGAN model. The primary focus of this investigation was to evaluate the efficacy of the preprocessing technique in augmenting the performance of the Mean-CAM-CNN model. To investigate this, we generated new versions of the MAI and AWE datasets, subjecting them to preprocessing involving colorization and enhancement using the trained DCGAN model. Subsequently, we applied the Mean-CAM-CNN framework to the newly generated datasets. The results of recognition, both with and without preprocessing, are presented in Table 4, showcasing the outcomes across the three previously defined performance metrics. The findings indicate a positive impact of the proposed preprocessing on recognition performance, as evidenced by improvements in recognition rates. Specifically, the Rank-1 recognition rates increased from 99.67% to 100.00% for MAI and from 74.50% to 76.25% for AWE.
## 3 Reconstruction Method and Protection Algorithm  
To address the adaptability of FDP to PV station access, a new protection algorithm is proposed that leverages the image recognition capabilities of deep learning. This algorithm does not rely on fixed protection thresholds but instead distinguishes waveform differences.
## 5 Conclusions and Prospect  
In this study, we propose a network framework named APMG for video Re-ID tasks. The APMG network comprises two modules: a CAP module, which partitions feature embeddings, and an MG module, which extracts feature information at different levels of granularity. To verify the re-identification performance of the APMG network, we conducted experiments on the MARS, iLIDS-VID, and PRID-2011 datasets. The results show that the performance of the APMG network is superior to other state-of-the-art methods, which also proves the feasibility of applying transformer structures in the Re-ID task. The application of transformer structures in Re-ID is at the initial stage. We believe that a more effective transformer-based network structure can be designed and improved by absorbing a lot of mature experience based on CNN methods, which can be further explored. In future work, we will introduce sliding windows with variable sizes into the ACP module to generate feature embeddings, and to make the network adapt to image tasks more effectively.  
Conceptualization, B.H.; methodology, B.H.; software, B.H.; validation, B.H., Y.P. and Y.T.; formal analysis, B.H.; investigation, B.H.; resources, B.H. and Y.P.; data curation, B.H.; writing--original draft preparation, B.H.; writing--review and editing, B.H. and Y.P.; visualization, B.H.; supervision, B.H. and Y.T.; project administration, Y.P.; funding acquisition, Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jilin Provincial Department of Science and Technology grant number YDZJ202102 CXJD062, and Jilin Province Machine Vision Intelligent Manufacturing and Inspection Technology Innovation Centre grant number 20180623039 TC.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://blog.csdn.net/qq_34132310/article/details/83869605](https://blog.csdn.net/qq_34132310/article/details/83869605)]([https://blog.csdn.net/qq_34132310/article/details/83869605](https://blog.csdn.net/qq_34132310/article/details/83869605)) (accessed on 9 November 2022).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Settings**} & **Parameter** & **MARS** & **iLIDS-VID** & **PRID-2011** \\ \cline{2-5}  & **Quantity** & **mAP** & **Rank-1** & **Rank-1** \\ \hline
16 \(\times\) 16, 8 \(\times\) 16, 4 \(\times\) 16 MG & 126 M & 88.4 & 93.6 & 97.1 \\
1. 16 \(\times\) 16 & 114 M & 86.6 & 90.0 & 94.9 \\
2. 16 \(\times\) 16, 8 \(\times\) 16 & 119 M & 87.5 & 92.7 & 95.8 \\
3. 16 \(\times\) 16, 8 \(\times\) 16, 4 \(\times\) 16, 2 \(\times\) 16 & 135 M & 88.2 & 92.9 & 97.2 \\
4. MG without weighted sum & 121 M & 87.0 & 90.8 & 95.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ablation experiment results of MG module.
## Results and Discussion
### Effect of Microchannel Size
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Microchannel Shape**} & \multicolumn{3}{c}{**Contact Area (m\({}^{2}\))**} \\ \cline{2-4}  & **0.2273 mm\({}^{2}\) (64 Pixel\({}^{2}\))** & **0.1741 mm\({}^{2}\) (49 Pixel\({}^{2}\))** & **0.1279 mm\({}^{2}\) (36 Pixel\({}^{2}\))** \\ \hline
1. Circle & 1.02 \(\times\) 10\({}^{-5}\) & 9.37 \(\times\) 10\({}^{-6}\) & 7.43 \(\times\) 10\({}^{-6}\) \\
2. Oval & 1.17 \(\times\) 10\({}^{-5}\) & 1.05 \(\times\) 10\({}^{-5}\) & 7.77 \(\times\) 10\({}^{-6}\) \\
3. Square & 1.13 \(\times\) 10\({}^{-5}\) & 9.73 \(\times\) 10\({}^{-6}\) & 8.19 \(\times\) 10\({}^{-6}\) \\
4. Rectangle & 1.43 \(\times\) 10\({}^{-5}\) & 1.00 \(\times\) 10\({}^{-5}\) & 8.17 \(\times\) 10\({}^{-6}\) \\
5. Triangle & 1.60 \(\times\) 10\({}^{-5}\) & 1.14 \(\times\) 10\({}^{-5}\) & 9.18 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Contact area of microchannels with variable shape and cross-sectional area. by the software rather than manually controlled. Thus, there is an inevitable error because of the meshing methodology used for the development of the simulation model.