esa-sceva/satcom-chunk-collection huggingface dataset:
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 4 System Model
### Cryptography  
In order to develop a secure and privacy-preserving authentication and billing scheme, different cryptographic methods are utilized in this work. In particular, the following cryptographic protocols are used:  
* The SHA-256 algorithm is adopted for the hash function with 256 bits (32 bytes) output.
* The Advanced Encryption Standard (AES) algorithm is used for symmetric encryption of messages exchanged with the charging segments, with a 256-bit key and an output size of 16 bytes.
* The RSA algorithm is used for asymmetric encryption of messages exchanged between the EVs, the CSC and the PO, with a 2048-bit key and an output size of 256 bytes.
* The elliptic-curve digital signature algorithm (ECDSA) is used to sign the messages exchanged between the EVs, the CSC and the PO, resulting in 448-bit signatures.  
Furthermore, additional communication security can be achieved by implementing advanced physical-layer security (PLS) techniques on the 5G wireless communication link [21; 54; 55], but this is beyond the scope of this work.  
Table 1 lists each symbol used in the proposed authentication and billing strategy and the corresponding symbol length given the aforementioned cryptographic protocols.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Symbol** & **Description** & **Length (bytes)** \\ \hline \(C\) & ID of the charging service company & 8 \\ \(PO_{i}\) & Pad Owner ID & 8 \\ \(s\) & charging segment ID & 8 \\ \(I_{e}\) & Real identity of EV \(e\) & 128 \\ \(p\) & Pseudo identity of EV \(e\) & 32 \\ \(k_{p}\) & Session key for the EV with pseudo identity \(p\) & 32 \\ \(t_{x}\) & Time stamp of the message generated by \(x\) & 8 \\ \(reqE\) & Required energy by EV \(e\) & 8 \\ \(SIG_{gk_{x}}\) & Message signature with the signing key of \(x\) & 56 \\ \(\varepsilon_{k_{p}}(.)\) & Symmetric key encryption using session key, \(k_{p}\) & 16 \\ \(E_{pk_{x}}(.)\) & Asymmetric key encryption using public key, \(pk_{x}\) & 256 \\ \(HashChReq\) & Hash chain request & 8 \\ \(\mathcal{H}_{i}()\) & Hash function & 32 \\ \(h_{n}\) & Hash key for authentication with the \(n\)th segment & 32 \\ \(param\) & Charging parameters & 32 \\ \(cost\) & Unit cost of the charging energy, in \$/kWh & 8 \\ \(P_{s,p}\) & Power supplied by \(s\) to the EV with pseudonym \(p\) & 8 \\ \(E_{p}\) & Total energy supplied to the EV, calculated by the PO & 8 \\ \(E\) & Actual energy received by the EV & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description and length, in bytes, of the different symbols used in the proposed authentication and billing model.  
Figure 3: Overview of the different communication technologies utilized in the proposed model.
# Demodulation Technique Based on Laser Interference for Weak Photo-Acoustic Signals on Water Surface
## 1 Introduction  
When an acoustic wave originating from an underwater object reaches the water/air interface, it will generate an elastic surface wave that transmits transversely along the interface in the medium's superficial layer, with a penetration depth of one wavelength approximately [1]. It has been discovered through studies that if an underwater acoustic source causes water surface vibration, only those vibrations sharing the same frequency as the acoustic source are significant in amplitude [2]. Hence, detection of the water surface acoustic waves (WSAWs) resulting from acoustic waves provides an indirect way of obtaining sound-generating information of an underwater acoustic source.  
Currently, sonar remains the main technology for underwater sound field detection, but the detection coverage is limited by low-speed movement of vehicles. Researchers have therefore been highly interested in laser-based WSAW detection techniques [3, 4]. One of the earliest studies with this regard was reported in the paper of [PERSON] [5], which illustrated the mechanism of WSAWs stemming from underwater acoustic signals. In his experiments, he also obtained optical signals with the same frequency as their acoustic source. Since then, a number of techniques have been brought forward by other researchers, including those based on laser scattering, laser diffraction, luminous flux, and laser interference [6, 7], all contributing to direct processing of light intensity signals. By using a laser sonar technique measuring Doppler vibrations, [PERSON]. [PERSON] et al. [8, 9] successfullydetected WSAWs, and later completed single-point detection at different locations of dynamic water surface. [PERSON] et al. [10] conducted laser interference measurements of acoustic waves on a low-frequency liquid surface and obtained modulation interference patterns. Information like surface acoustic wavelength, frequency, and amplitude was then demodulated from such interference patterns. Great efforts have been made by our project team in the research of underwater target detection through aerial laser interference and effective extraction of complicated underwater sound field signals, and so far, concrete results have been achieved under laboratory conditions (in the optical dark chamber with a high SNR). Detection of underwater acoustic sources in the frequency range of 1 kHz-18 kHz has been made possible by us with techniques such as spectrum analysis, wavelet transform analysis, and local turning point data demodulation [11; 12; 13]. However, all the above techniques fail to cover the whole acoustic wave frequency range, and are unable to provide information on variations of acoustic source signal density. In contrast, the new interference demodulation method proposed in this paper enables acquisition of underwater acoustic source vibration information and tracking of varying vibration density based on detection of WSAW. The effectiveness of the method has been proven by us through tests.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 3 Construction of Computing Platform in Micro-Nano Satellite
### Intelligent Application  
A variety of advanced on-board processing tasks can be realized through the high-performance processing platform, which can simplify massive remote sensing images into valuable information and greatly improve the data utilization rate. We receive near real-time downlinked data from satellite through the data service operation platform and analyze it in some intelligent application (APP). The received data will be graphically displayed by the graphical display module for the convenience of users. Through the monitoring and management software, the ground users can realize the monitoring of the data transmitted from the satellite and the communication supervision of the ground instructions uploaded to the satellite. Besides, the algorithm model and code of the new task can be uploaded to the information flow unit of the satellite through the satellite upload channel, so as to accomplish new applications.
## 3 Results and Discussion  
### Case Studies  
This section uses users from a certain region in China as an example to validate the electricity sales package recommendation method based on dynamic hybrid multi-attribute group decision-making with two reference points. The load data of 500 typical customers \(R~{}=~{}\{R_{1},R_{2},\ldots,R_{500}\}\) collected by smart meters from 1-31 January 2020, is used as the basis. A leave-one-out cross-validation method is applied for validation of the proposed method. In each iteration, one user is selected as the new user, and the remaining 499 users are used as the sample set for the decision-making process.  
Based on the data from the provincial trading center and the actual conditions of the users, the electricity sales company provides the following set of packages: \(A~{}=~{}\{A_{1},A_{2},A_{3},A_{4}\}\). The attributes of these electricity sales packages are detailed in the appendix. Considering that the main goal of this paper is to study the recommendation method for electricity sales packages, the load data mentioned above is directly used as the new user's monthly load for analysis. The electricity sales package attributes considered in this paper include electricity price, additional premium, value-added services, and reward policies. The meanings of these attributes are provided in Table 2.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.14. Domain of Unknown Function (DUF)  
Generally, every protein domain has a distinct structure and function. However, there are several domains which have no known role, and these are referred to as domains of unknown function (DUFs). Most of the time these domains were ignored as having little relevance, but now there are reports which show that many DUFs are essential, as they are crucial for protein function. Basing themselves on sets of bioinformatic analyses of several uncharacterized DUFs, [PERSON] et al. speculated about probable tasks which may be related to ATP binding or transcription [133].
## 2 Applications
### Military  
The military often reflects the ability of a country to defend itself against any form of attack, including underwater attack. IoUT is required for defense purposes, and can be applied to submarine detection [22], underwater mine detection [23], and underwater surveillance systems [24]. These applications have great potential for the future naval forces.  
* For submarine detection, the authors in [22] investigate the design of the sensor deployment, and propose a scheme to optimize the monitoring coverage. By taking the water depth, transmission range, and attenuation into account, the proposed scheme uses Particle Swarm Optimization (PSO) to determine the positions of the sensor nodes. The authors provide both the mathematical analysis and NS3-based simulations to verify the performance of the proposed scheme in terms of the monitoring coverage and number of sensor nodes required.
* In [23], [PERSON] et al. analyze the design of underwater mine detection systems. Specifically, they consider two sonar alternatives and five towing-vehicle alternatives (i.e., one underwater vehicle alternative, two surface vehicle alternatives, and two airborne vehicle alternatives). They use a computer model to simulate the process of detecting underwater mines, and test each of the design alternatives. The results show that the underwater vehicle alternative consumes the least amount of energy. They also take the safety, speed, fuel economy, and probability of detection into account, and provide the corresponding analysis to indicate the best alternative.
* In [24], the authors propose a novel UWSN architecture for underwater surveillance systems. In the proposed architecture, when sensor nodes are deployed, the sensor nodes are first placed in surface buoys; then, the sensor nodes are lowered to different depths. The depths are determined by the proposed scheme for maximizing the surveillance coverage. Moreover, each node is equipped with multiple different types (e.g., acoustic, magnetic, radiation and mechanical) of micro-sensors. Reading the data collected from the micro-sensors, the authors further present a data-mining-based scheme for classifying the submarines, mines, and divers.
## 2 Materials and Methods
### Water Contact Angle (WCA)  
In order to analyze the effect of the coating on the hydrophilicity of the surface, the WCA was measured through the sessile drop method. For this technique, as it is recommended to use flat-surface samples [31], the samples obtained by compression molding described in Section 2.2 were used. The different group of samples tested were PLA, PLA plasma, PLA aloe vera pH 3, PLA aloe vera pH 4 and PLA aloe vera pH 5. The WCA measurements were carried out at room temperature using a Kruss DSA100 water contact angle measuring device (KRUSS GmbH, Germany) and the open source software ImageJ was used to measure the static contact angle of 2 \(\mu\)L distilled water droplets placed onto the surface of the samples. Reported contact angles are the average of six measurements per group of samples.
## 5 Conclusions  
A relatively important node mining method based on DDMF is proposed in this work. The DDMF method is mainly based on the distance distribution information of nodes. Starting from known important nodes, it aims to find relatively important nodes in a network. The detailed comparative experiments with five other algorithms for mining relatively important nodes in four real networks reveal that the DDMF method performs well in terms of precision and applicability. Moreover, the DDMF method can not only be used to mine the relatively important nodes in a network, but also be considered as a new idea for community detection and link prediction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Ksmar** & **PPR** & **Katz** & **NMF** & **NLD** & **DDMF** \\ \hline SARS & 0.686 & 0.683 & 0.650 & 0.635 & 0.667 & 0.692 \\ Genepath & 0.545 & 0.526 & 0.482 & 0.568 & 0.565 & 0.675 \\ Mouse & 0.696 & 0.693 & 0.685 & 0.654 & 0.669 & 0.737 \\ Yeast & 0.596 & 0.582 & 0.564 & 0.686 & 0.665 & 0.669 \\ \hline \hline \end{tabular}
\end{table}
Table 2: AUC results in four networks.  
Figure 3: Recall rate results in four networks: (**a**) SARS network; (**b**) Genepath network; (**c**) Mouse network; (**d**) Yeast network.  
Mining relatively important nodes in complex networks is a challenging task with practical value. The DDMF method can be effectively used to find relatively important nodes in networks and provides a new idea and direction for the related work of network information mining in the future. With that being said, the limitation of the DDMF method can be summarized as something that it only considers mining relatively important nodes in single-layer networks. In the future, our relatively important nodes mining method can be applied to complex and diversified multilayer networks. Random walk could also be considered as a direction in future research.  
Conceptualization, N.Z. and Q.L.; methodology, Q.L.; software, J.W.; validation, Q.L., M.J. and J.L.; formal analysis, N.Z.; investigation, J.W.; resources, Z.Z.; data curation, M.J.; writing--original draft preparation, Q.L.; writing--review and editing, Q.L.; visualization, N.Z.; supervision, J.W.; project administration, Z.Z.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Plan of Yunnan Province Major Science and Technology Plan (202102 AA100021), the National Natural Science Foundation of China (62066048), the Yunnan Natural Science Foundation Project (202101 AT070167) and the Open Foundation of Key Laboratory in Software Engineering of Yunnan Province (2020 SE311).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
###### Abstract  
Precise estimation of reference evapotranspiration (ET\({}_{0}\)) is of significant importance in hydrologic processes. In this study, a genetic algorithm (GA) optimized back propagation (BP) neural network model was developed to estimate ET\({}_{0}\) using different combinations of meteorological data across various climatic zones and seasons in China. Fourteen climatic locations were selected to represent five major climates. Meteorological datasets in 2018-2020, including maximum, minimum and mean air temperature (T\({}_{\text{max}}\), T\({}_{\text{min}}\), T\({}_{\text{mean}}\), \({}^{\circ}\)C) and diurnal temperature range (AT, \({}^{\circ}\)C), solar radiation (R\({}_{\text{a}}\), MJ m\({}^{-2}\) d\({}^{-1}\)), sunshine duration (S, h), relative humidity (RH, %) and wind speed (U\({}_{2}\), m s\({}^{-1}\)), were first subjected to correlation analysis to determine which variables were suitable as input parameters. Datasets in 2018 and 2019 were utilized for training the models, while datasets in 2020 were for testing. Coefficients of determination (r\({}^{2}\)) of 0.50 and 0.70 were adopted as threshold values for selection of correlated variables to run the models. Results showed that U\({}_{2}\) had the least r\({}^{2}\) with ET\({}_{0}\), followed by AT. T\({}_{\text{max}}\) had the greatest r\({}^{2}\) with ET\({}_{0}\), followed by T\({}_{\text{mean}}\), R\({}_{\text{a}}\) and T\({}_{\text{min}}\). GA significantly improved the performance of BP models across different climatic zones, with the accuracy of GABP models significantly higher than that of BP models. GABP\({}_{0.5}\) model (input variables based on r\({}^{2}\) > 0.50) had the best ET\({}_{0}\) estimation performance for different seasons and significantly reduced estimation errors, especially for autumn and winter seasons whose errors were larger with other BP and GABP models. GABP\({}_{0.5}\) model using radiation/temperature data is highly recommended as a promising tool for modelling and predicting ET\({}_{0}\) in various climatic locations.  
BP models; climatic zones; model performance; multi\(-\)layer perceptron; seasons Article
# An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
## 1 Introduction
### Challenge  
Due to the mutual exclusivity among multiple edge nodes in the smart grid of Internet of Things (IoT), data sharing between these edge nodes becomes challenging, affecting the data aggregation capability in the smart grid of IoT. To address this issue, the federated learning (FL) framework enables data sharing among multiple edge nodes, ensuring effective data aggregation in the power sector.  
While federated learning is the way to go with training models in privacy-dependant systems, it does pose some problems that need to be addressed. One of the biggest concerns is data privacy. Firstly, attackers may infer original data by analyzing the shared parameters (parameter inversion). Even without direct access to raw data, they could deduce sensitive information by observing updates to the shared model's parameters. Secondly, in some cases, the shared model might contain data labeling or partial data, leading to the potential leakage of sensitive information. Lastly, if the parameter updates lack noise, attackers may infer individual data by monitoring the gradient updates, thus compromising privacy.  
Consequently, we propose an enhanced FL system that is specifically designed for data aggregation in the smart grid domain. The secure multi-party computation (SMC) [11] and differential privacy (DP) [12] is integrated with FL to mitigate inference attacks during the learning process and output process, thereby ensuring ample privacy guarantees. Within this system, a trusted third party can securely acquire model parameters from data holders and securely update the global model through aggregation. Throughout this entire process, the integrity of data from each party is maintained, ensuring that private information cannot be inferred from the global model [13].
## 7 Concluding Remarks  
In Vietnam, there is a problem of water scarcity in Dak Lak Province due to enhanced requirements for agricultural development and the occurrence of frequent drought conditions in recent years. Erratic and decreased rainfall in the area has led to overexploitation of groundwater reservoirs to meet the supply of water for day to day requirements of the province for drinking, cultivation, and industrial uses. Therefore, there is a great need for the assessment of groundwater potential and identification of the suitable areas of recharge. To address this need, four ensemble models, namely BLR, DLR, CGLR, and RSSLR, were developed to produce groundwater potential maps for the province. All four models performed well (AUC\(>\) 0.7) for the assessment of groundwater potential and generation of potential zone maps. Among the studied models, the DLR model with AUC = 0.769 and RMSE = 0.444 was the best model in comparison to the other ensemble models and the single LR model. Therefore, a groundwater potential map generated using the DLR model can be used by decision-makers in the development of effective adaptive groundwater management plans for Dak Lak Province. The models proposed in the present study can be applied in other areas for better groundwater potential mapping considering local geo-environmental factors.  
**Author Contributions:** Conceptualization, P.T.N., D.H.H.H., N.A.-A., and B.T.P.; methodology, B.T.P., P.T.N., T.V.P., A.J., and N.A.-A.; validation, B.T.P., P.T.N., N.A.-A., M.A., L.S.H., and I.P.; formal analysis, P.T.N., T.V.P., and D.H.H.; data curation, D.H.H., T.V.P., H.D.N., and H.V.L.; writing--original draft preparation, all authors; writing--review and editing, P.T.N., L.S.H., B.T.P., A.J., N.A.-A., and I.P.; visualization, T.V.P., L.S.H., R.K., R.S., H.V.L., and D.H.H.; supervision, P.T.N., B.T.P., N.A.-A., and I.P.; project administration, P.T.N., B.T.P., and N.A.-A.; funding acquisition, N.A.-A. and B.T.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.08-2019.03.  
**Acknowledgments:** We thank the Vietnam Academy for Water Resources (VAWR) for providing the data for carrying out this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.