esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It has been verified in the practical application of Fengman reservoir in China. [PERSON] et al. [25] verified the feasibility of enhanced version of extreme learning machine (EELM) model in river flow prediction in tropical environments. In the context of climate change, the increased frequency of extreme flooding poses a severe threat to economy and human security. Therefore, it is necessary to accurately predict the time and magnitude of peak discharge before the arrival of flood. [PERSON] [26] proposed that meta-heuristic techniques can be used to calibrate data-driven rainfall and runoff models to improve the prediction accuracy. In view of the lagged prediction effect that often occurs during neural network modeling, [PERSON] and [PERSON] [27] attempted to eliminate the lag effect from modular artificial neural network (MANN) and data preprocessing by singular spectrum analysis (SSA). [PERSON] et al. [28] adopted the particle swarm optimization (PSO) model and the independent of multi-layer perceptron (MLP) model to evaluate the prediction precision of MLP based on quantum-behaved particle swarm optimization (MLP- QPSO) model. The results show that the hybrid MLP-QPSO model is an optimal prediction tool for estimating daily evaporation, better than the hybrid MLP-PSO model and standalone model. The 400 mm annual precipitation line serves as a natural divide between China's semi-humid and semi-arid regions, marking a biome separator between forest and grassland vegetation, as well as China's regions for crop and livestock agriculture. In this study, in order to understand the spatial and temporal patterns of NPP in response to climate change within the fluctuation zone along the 400 mm annual precipitation line, dispersion degree of the annual mean NPP and the variation in the trends of NPP was analyzed by using the coefficient of variation and the unitary linear regression method. The significance of the variation in trends was verified. Previous studies have found that the main driving factors of NPP are temperature and precipitation [29; 30]. In this study, the second-order partial correlation analysis method was used to analyze the partial correlation between NPP and precipitation by limiting the influence of evapotranspiration (ET) and land surface temperature (LST). This can effectively avoid the interference of other variables on the analysis results and increase the degree of fitting between the analysis results and actual situation. Furthermore, the effects of ET and precipitation were limited, and the partial correlation between LST and NPP was analyzed. Finally, the Hurst index estimation method was used to predict future trends of NPP in the study area at both annual and seasonal scales. This study reveals the driving forces and limitations of climate factors' effects on NPP in China's ecologically sensitive areas and provides a scientific basis for formulating regional environmental governance and protection.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 4 Our Proposal
### Billing
#### 4.7.2 Pre-Decryption of the Electricity Billing Ciphertext  
1. Upon receiving the \(\mathcal{T}^{\prime}_{ij}\) from \(FN_{i}\), AC performs the batch verification to verify all \(\mathcal{T}^{\prime}_{ij}\) according to the following equation: \[e(\prod\limits_{j=1}^{n}\sigma_{FN_{ij^{\prime}}^{\prime}}g)=\prod\limits_{j=1 }^{n}e(H(ID_{FN_{i}}\left\|ID_{SM_{ij}}\left\|U_{ij}\left\|V_{ij}\right\|T_{FN_{ i^{\prime}}}\right),pk_{FN_{i}}).\] (5)
2. If Equation (5) holds, AC uses its private key to perform pre-decryption as follows: \(B^{\prime}_{ij}=\frac{V_{ij}}{(U_{ij})^{AC}}\).
3. AC utilizes its private key to generate the signature \[\sigma_{AC^{\prime}_{ij}}=H(ID_{AC}\parallel ID_{SM_{ij}}||U_{ij}\parallel B ^{\prime}_{ij}\parallel T_{AC^{\prime}})^{x_{AC}}.\]
4. AC sends \(\mathcal{T}^{\prime}_{AC}=(ID_{AC},ID_{SM_{ij}},U_{ij},B^{\prime}_{ij},T_{AC^ {\prime}},\sigma_{AC^{\prime}_{i}})\) to the SP.
## 2 Design of Analytic Collimating Lens for Light Collimation  
### Commercial UV-LED Light Source and Lens Material  
The goal of this work is for light collimation of UV-LEDs for lithography applications. Therefore, six high-power commercial UV-LEDs with a peak wavelength of 405 nm are chosen from three major UV-LED suppliers, namely, Luminus, Inc. (Sunnyvale, CA 94086, USA), NICHIA Co. (Anan-Shi, Tokushima 774-8601, Japan) and Seoul Viosys (Ansan-si, Gyeonggi-do, Korea). The main specifications of the six chosen UV-LEDs labeled from (I) to (VI) are listed in Table 1. The light-emitting area sizes of the chosen UV-LEDs are from 1 \(\times\) 1 mm\({}^{2}\) to 4.63 \(\times\) 2.6 mm\({}^{2}\), and the output optical powers range from 1.16 W to 19.5 W. The radiation patterns, or the angular distribution of emitted optical power intensity, of the six UV-LEDs are available and have been extracted from companies' websites. The optical simulation software Zemax OpticStudio (Kirkland, WA, USA) was used to export the radiation pattern of each commercial UV-LED from their corresponding ray data files. Figure 1 shows the obtained radiation patterns of each of the six UV-LEDs used in this work. We will use the angle of full width at half maximum (\(\theta_{FWHM}\)) of radiation pattern as the indication of directivity of the UV-LEDs, which covers from 46\({}^{\circ}\) to 120\({}^{\circ}\) for the six chosen UV-LEDs as shown in Table 1 and Figure 1.  
For the sake of mass production, the lens material is selected as PLEXIGLAS(r)8012 (Evonik Performance Materials GmbH, Essen, Germany) which is a type of polymethyl-methacrylate (PMMA) polymer material commonly used in plastic injection molding. This UV-graded PMMA lens material has excellent transmittance and an optical refractive index of 1.51 at the wavelength of 405 nm [26]. The availability of using this PMMA material for injection molding allows mass production of the collimating lenses at high throughput and low cost.
## 3 BeiDou PPP Strategy
### Residual Analysis  
Observations residuals mainly contain the observation noises, multipath errors, orbit errors and mismodelled errors so that the residuals can be used as an important index to assess positioning accuracy [23; 24; 25; 26; 27]. Figure 11 shows the residuals of BeiDou satellite ionosphere-free undifferenced phase (LC) and code (PC) observations residuals of 10 stations of \"BETS\" on September 24, 2012 (DOY is 268). It can be seen that the LC residuals are nearly within \(\pm\)2.0 cm and the PC residuals are within \(\pm\)4.0 m, but because the weighting of code observations is very small compared with that of phase observations, it does not influence the final results significantly.  
Figure 12 shows the RMS values of the residuals of BeiDou satellites for each site of \"BETS\" on September 24, 2012. The average RMS values of undifferenced LC and PC residuals are listed in Table 3. Figure 13 shows the RMS values of the residuals of GPS satellites for \"BETS\" on September 24, 2012. On the basis of these figures and table, we can find that:  
1. The RMS values of the PC residuals of BeiDou satellites for \"BETS\" are smaller than 3 m and the RMS values of the LC residuals of GEO and IGSO satellites are smaller than 1.5 cm  
Figure 10: Daily RMS values of BeiDou, GPS, and BeiDou/GPS kinematic PPP solutions of CHDU against the “ground truth” in the East, North and Up components from September 24 to September 30 in 2012 (Day of year is from 268 to 274).  
while those of MEO satellites are within 1-3 cm. By contrast, the RMS values of the LC and PC residuals of GPS satellites have the similar accuracy. The RMS values of the LC and PC residuals of GPS satellites are smaller than 2 cm and 2 m, respectively. The average values of the RMS of the undifferenced LC and PC residuals of BeiDou satellites for \"BETS\" are 0.95 cm and 1.42 m, respectively. And the average values of the RMS of the undifferenced LC and PC residuals of GPS satellites are 0.91 cm and 1.25 m, respectively. That BeiDou system has larger LC and PC residuals than GPS may be caused by the multipath error.
2. The RMS values of the residuals of GEO C01 and C03 are smaller than that of GEO C04 and C05 and the IGSOs, while those of the MEOs are the worst. This may be due to the fact that the elevations of GEO C01 and C03 are larger than those of other satellites and also do not change much, which leads to a smaller multipath error. Meanwhile, the orbit accuracy of GEO C01 and C03 is better than that of GEO C04 and C05 [3]. Furthermore, because the PCV of BeiDou satellites are not considered and the PCO may be not accurate enough, there may be a satellites-specific bias in the residuals. In addition, as shown in Figure 2, the GEOs can be seen all the day and the ambiguity terms of the GEOs are steadier than that of other satellites, which better fit the observations.  
Figure 11: Undifferenced LC and PC residuals of BeiDou satellites.  
Figure 12: RMS values of undifferenced LC and PC residuals for the BeiDou satellites of all individual sites.
## 3 Routing Issues and Challenges in WBSNs
### Quality of Service (QoS)  
In WBSNs different types of data require different quality of services as it deals with vital signs of the human body. The authors in [25,26] have classified the patient data into critical data (like EEG, ECG _etc._), delay sensitive data (for example video streaming), reliability-sensitive data (like vital signals monitoring respiration monitor, and PH monitor) and ordinary data (for example temperature, heartbeat, _etc._). The other data-centric applications of WSNs also cannot tolerate latency and/or any loss of packets [27]. The proposed protocols need to be aware of the different types of quality of service required for different types of patients' vital sign- related data.
## 5 Results
### PCM Volume Fraction Variations  
As the horizontal fins installed, the PCM volume decreases. For the base heat sink (without horizontal fins), the PCM volume fraction (\(\varphi_{PCM}\)) variations in terms of AR are shown in Figure 13. As the aspect ratio increases from 1 to 5, the PCM volume fraction (for base heat sink) rises from 0.926 to 0.965. To evaluate the variation in PCM volume fraction for heat sink equipped with the horizontal fins, the optimal number of fins along with appropriate length ratio (LR) must be determined from Figure 10 and then the optimal PCM volume fraction is calculated considering Equation (1). As shown in Figure 13, \(\varphi_{PCM}\) for heat sink with optimal fins number increases as the heat sink becomes wider.  
Figure 12: The variation in SOT with respect to LR at different heat sink height.  
Note that with increasing \(\varphi_{PCM}\), the fin volume fraction \(\left(\varphi_{fin}\right)\) decreases. As mentioned in Figure 11, with the increase in AR, the intensity of the convection heat transfer is amplified and therefore the presence of horizontal fins may disturb the liquid phase movement. Therefore with increasing AR, shorter fins perform better and therefore it is expected that with increasing aspect ratio, the fin volume fraction \(\varphi_{fin}\) for the heat sink equipped with the optimal number of fins diminishes. Figure 13 shows that as AR rises, the optimal PCM volume fraction increases, which corresponds to a decrease in \(\varphi_{fin}\). This means that as the heat well widens, less fin volume fraction is required. Figure 13 shows that the PCM volume fraction of the studied base heat sinks has changed in the range of 0.92 to 0.97. However, installing horizontal fins reduces \(\varphi_{PCM}\) so that the optimal \(\varphi_{PCM}\) varies in the range of 0.83 to 0.93.
## 3 Comparative Analysis of Different Binarization Methods
### Color-Sensitive Binarization Methods  
#### 3.1.1 Binarization Method Based on Gray Level  
Converting a color image to a grayscale image is a common method in image processing, as shown in the common grayscale transformation Formula (1):  
\[Gray\ =\ 0.299\times Red+0.587\times Green+0.114\times Blue, \tag{1}\]  
where \(Gray\) represents the calculated gray value, \(Red\), \(Green\) and \(Blue\), respectively, represent the RGB three-channel values of the pixel point.  
As shown in Figure 5, the background of the X-ray security image is white or close to white. After converting it into a gray image [18], the background color is brighter than the object color. A threshold is specified such that pixel points with a gray value greater than the specified threshold are zero, and points with a gray value less than the specified threshold are one, resulting in a binarized X-ray security image.  
Figure 4: Extraction of package region by binary images and the border detection algorithm.  
As shown in Figure 6, the binary image is operated to eliminate the noise in the image. However, there are also some problems: when the gray value of the background is within the range of the gray value of the object (that is, the gray value of part of the object is greater than that of the background, while the gray value of the other part is less than that of the background), it is impossible to find a suitable threshold to distinguish the object and the background. This method is more sensitive to background noise. When the background noise is complex, it cannot find an appropriate threshold value for binarization. When the threshold is too large, as shown in Figure 7a, the background noise cannot be removed; when the threshold is too small, as shown in Figure 7b, the object information is lost.  
Figure 5: The color image is converted into a grayscale image and binarized.  
Figure 6: Open operation on the binary image.  
Figure 7: (**a**) Binarization of the grayscale image when threshold is too large. (**b**) Binarization of grayscale image when threshold is too small.
## 1 Introduction  
In 2012, cochlear implants (CIs) had successfully restored partial hearing to over 324,200 deaf people worldwide [1]. In most cases, users of modern CIs perform well in quiet listening conditions. Four CI manufacturers are presently on the French market: Cochlear(r) and Neurelec(r)/Oticon Medical(r) for Channel-Picking (CP) strategies and Advanced Bionics(r) and Med-EI(r) for Fixed-Channel (FC) strategies. For most CI users, performances for speech perception significantly decrease in noisy environments [2].  
All modern sound coding strategies are based on the analysis of acoustic information by a bank of band-pass filters and each strategy has its own philosophy [3].  
Two coding schemes are mainly in use. FC strategies transmit all available channels to the electrodes, which usually stimulate at a high rate. CP strategies (sometimes called n-of-m strategies) use various stimulation rates (a high, medium or low rate), estimate the outputs of all the available channels (m) and select a subset of channels (n) with the largest amplitudes.  
The present study focuses on the relative contribution of FC strategies and CP strategies on syllable recognition in noise. We wish to compare the efficiency of the FC and CP coding strategies, first in simulation and secondly with CI users.
## 7 Conclusions  
The comparative analysis of machine learning models for predicting photovoltaic system efficiency indicated that CatBoost is the most effective model, providing enhanced accuracy and strong generalizability. Its sophisticated abilities in managing categorical data, mitigating overfitting, and capturing non-linear interactions render it an essential instrument for modeling photovoltaic efficiency based on internal properties. The ramifications of this study extend beyond model selection; precise efficiency calculation directly influences the optimization of photovoltaic system design, enhancing energy output and diminishing operational expenses. Moreover, the integration of XAI approaches like SHAP and LIME enhances comprehension of the determinants affecting efficiency forecasts, thus augmenting the interpretability and practical applicability of the models. These insights can enable precise interventions to enhance PV module performance, hence fostering more efficient and sustainable energy generation. The findings underscore the essential importance of sophisticated machine learning models and explainable artificial intelligence techniques in the renewable energy sector. Utilizing these tools allows stakeholders to acquire profound insights into PV systems, facilitating more informed decision-making that promotes the advancement and optimization of PV technology. Future research should investigate the incorporation of time-series analysis techniques like Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks to capture temporal dependencies in PV system performance. Furthermore, exploring advanced XAI methods beyond SHAP and LIME, such as counterfactual explanations or causal inference techniques, could provide deeper insights into the underlying factors driving PV efficiency. Finally, the impact of different data aggregation intervals (e.g., 15 min, 30 min) on model performance and interpretability warrants further investigation.  
Methodology, I.L., G.S., G.I. and W.G.J.H.M.v.S.; Software, G.S., G.I. and W.G.J.H.M.v.S.; Validation, G.S.; Formal analysis, I.L., G.S., G.I. and W.G.J.H.M.v.S.; Investigation, I.L., G.S., G.I. and W.G.J.H.M.v.S.; Writing - original draft, I.L., G.S., G.I. and W.G.J.H.M.v.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 2 Proposed Chaos-Based Cryptosystem
### Description of the Proposed Pseudorandom Number Generator of Chaotic Sequences (PRNG-CS)
The size of the secret key of the proposed PRNG-SC is:  
\[|K|=|XP0|+|XS0|+|XL0|+|XT0|+|QQ|+\big{|}P_{p}\big{|}+|P_{s}|+|T_{r}|+(9\times \big{|}\epsilon_{ij}\big{|})=278\ bits \tag{13}\]  
where \(|XP0|=|XS0|=|XL0|=|XT0|=|PQ|=|P_{s}|=32\ bits\), \(\big{|}P_{p}\big{|}=31\ bits\), \(\big{|}T_{r}|=10\ bits\), and \(\big{|}\epsilon_{ij}\big{|}=5\ bits\). Therefore, the keyspace contains \(2^{278}\) different values, which is large enough to make brute-force attacks infeasible.
## 3 Results  
The analysis presented below was conducted as there were no existing studies on the optimisation of nanosatellites in terms of the maximisation of the duration of flight over specific areas of interest. The research results point to the fact that an appropriate selection of the orbit parameters and of the weight of the satellite and fuel significantly influence the efficiency of the operational mission.
_Applied_  
_sciences_  
_Mdpi_  
_Mitigating Insider Threats Using Bio-Inspired Models_  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 2**  
1 Computer and Network Security Research, Faculty of Pure and Applied Sciences, Open University of Cyprus, 2220 Nicosia, Cyprus; [EMAIL_ADDRESS]  
2 School of Computing, Faculty of Technology, University of Portsmouth, Portsmouth PO1 2 UP, UK; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 23 June 2020; Accepted: 17 July 2020; Published: 22 July 2020  
**Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.**  
**Abstract:** Insider threats have become a considerable information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the threat from insiders is increasing. Many researchers are approaching this issue with various methods in order to develop a model that will help organizations to reduce their exposure to the threat and prevent damage to their assets. In this paper, we approach the insider threat problem and attempt to mitigate it by developing a machine learning model based on Bio-inspired computing. The model was developed by using an existing unsupervised learning algorithm for anomaly detection and we fitted the model to a synthetic dataset to detect outliers. We explore swarm intelligence algorithms and their performance on feature selection optimization for improving the performance of the machine learning model. The results show that swarm intelligence algorithms perform well on feature selection optimization and the generated, near-optimal, subset of features has a similar performance to the original one.  
**Keywords:** bio-inspired computing; insider threats; swarm intelligence (SI); feature selection (FS); optimization; machine learning (ML); outlier detection; anomaly detection
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 4 Artificial Muscle Design and Fabrication
### Linear Expansion DEA
The shorter the roll actuator, the greater the actuation tends to be. So, the fiber roll actuator was suggested to produce up to 35.8% strain at 21 kV, independent of its aspect ratio, by arranging the fiber in the circumferential direction [82]. Furthermore, applying internal pneumatic pressure to prevent buckling of the fiber due to excessive pre-strain has been reported [83]. The maximum performance of each configuration are arranged in Table 3.  
However, uneven thickness, non-uniform electric field, and stress concentration limit the performance of DEA. By adopting appropriate manufacturing approaches, such as microfabrication, the dielectric elastomer membrane's quality and uniformity can be improved, and consequently increase its performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Configurations** & **Nominal Electric Field** & **Pre-Strain** & **Material** & **Size** & **Load 1** & **Strain or Stroke** & **Ref.** \\ \hline \hline \multicolumn{8}{c}{**Linear Contraction**} \\ \hline Multi-stack & 4.2 kV/40 \(\mu\)m & 400\% biaxial & VHB 4910 + IPN & \(18\times 18\times 18.3\) mm\({}^{3}\) & 20.74 N & –10\% & [58] \\ Helical & 14 V/imm & None & TC-5005 & \(13\times 13\times 80\) mm\({}^{3}\) & None & –5\% & [65] \\ Folded & 12 V/imm & None & TC-5005 & \(25\times 25\times 85\) mm\({}^{3}\) & None & –15\% & [68] \\ Twisted & 6.5 kV/220 \(\mu\)m & None & Elastosil 7670 + PT8 & \(11.5\times 11.5\times 80\) mm\({}^{3}\) & 100g & –5.2\% & [70] \\ \hline \hline \multicolumn{8}{c}{**Linear Expansion**} \\ \hline Cone & 90 V/imm & 20\% biaxial & Elastosil film & \(86\times 86\times 25\) mm\({}^{3}\) & 100 N & +3 mm & [73] \\ Tubular & 7.5 kV/160 \(\mu\)m & None & CF 19-2186 & \(8\times 8\times 50\) mm\({}^{3}\) & –20 g & +14\% & [42] \\ Roll & 21 kV/1000 \(\mu\)m & None & VHB 4910 & 45\%(45\%40 mm\({}^{3}\) & –547 g & +35.8\% & [82] \\ \hline \hline \end{tabular}  
* Positive if the direction of the load is opposite from the actuation, negative if it is the same.  
\end{table} 
Table 3: Maximum performance of DE artificial muscle design. Figure 6: Schematics of Linear Expansion DEA. Transparent: Elastomers, Black: Electrodes (**a**) Cone actuator; (**b**) Tubular actuator; (**c**) Roll actuator.