esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 5 System Architecture
### Service Applications  
In an uncontrolled environment, the secure data asset sharing and circulation system provides multi-class cryptography services to the external application business system by deploying the edge server. As shown in Figure 7, based on the service-oriented architecture, the cryptography cloud service center is constructed, and the security service components such as cryptography are provided to realize the unified management of security control service. Based on security API and other technologies, a unified security API is constructed to realize the security authorization and standardized management of security service requirements. Based on the dynamic refactoring architecture, the edge security gateway is constructed to realize load balancing, on-demand configuration and secure stealth edge security control. Based on the edge computing mode, the new edge service is constructed to realize the fast adaptation between the protection system and the protected business system.
## 4 Numerical Results and Discussion
### Fatigue Analysis
#### 4.2.1 Effect of BCS on Number of Rainbow Cycles  
According to Equation (14), it is necessary for fatigue analysis to calculate the rainflow cycles based on the algorithm of rainflow counting [32]. In order to research the effect of BCS on number of rainflow cycles, we binned the fatigue cycle counts according to load range. The width of shear force (BFX and TBFX) and moment (BMY and TBMY) range bins are 20 kN and 300 kN\(\cdot\)m, respectively. And the number of cumulative cycles in the simulated ten minutes for each wind condition can be obtained. According to the analysis in Section 4.1, it is found that the influence of the BCS on the structural responses of the wind turbine is more obvious with the wind speed around cut-in speed and after the rated wind speed. In order to clearly reflect the effect of the BCS on the number of rainflow cycles, we chose 3 (cut-in), 11.4 (rated), 17, and 25 m/s (cut-out) four wind conditions and the comparison of binned range rainflow cycles of the four load components between  
Figure 9: Campbell diagram of the 5-MW wind turbine.  
the two cases for four wind scenarios are shown in Figures 10-13. It is observed that the number of cycles is large in the low load range and small in the high load range for two cases. In Figures 10 and 11, the number of cycles are similar between the two cases in the region of below-rated wind speed range and the number of cycles in high load range in No. 1 are slightly higher than that in No. 2 in the region of above-rated wind speed range. The reason is that the oscillation increase of aerodynamic loads during normal operation. From Figures 12 and 13, it is noted that in the region of below-rated wind speed range, the number of cycles in No. 1 are smaller than that in No. 2 because of the 3P of the rotor speed is close to the first tower FA natural frequency when the wind speeds are around the cut-in speed and in the above-rated wind speed range, the number of cycles are similar between the two cases for TBFX and the number of cycles in No. 1 are slightly smaller than that in No. 2 for TBMY. It can be concluded that compared with BFX and BMY in blade root, the effect of BCS on the number of rainflow cycles in high load range on TBFX and TBMY in tower base is more obvious.
## 3 Methodology
#### Data Preparation and Construction  
The data generation was primarily performed using a GAN network [31]. While we initially explored Stable Diffusion [32] as an alternative generative model, all the experiments reported in this paper were conducted using GAN-generated images to maintain consistency in our comparative analysis. Our preliminary tests with Stable Diffusion suggested similar patterns to those observed with GANs, but a comprehensive comparison between different generative models for Algorithm selection remains an interesting direction for future work.  
The specifics of the data generation methodology are as follows. Both parts of the GAN (a generator and a discriminator) are implemented as convolutional neural network. The schematic representation of the learning of the GAN network is shown in Figure 2. The general principle of learning GAN can be explained as follows. Propagate a latent vector \(z\) of randomly generated noise to obtain a set of synthetic samples that together with some real images create the discriminator training set. Then, train the discriminator by setting the Training Generator variable to 0 to generate an output from the discriminator and update its parameters (dashed-line Discriminator update). Repeat the process, but this time set the Training Generator variable to 1 to propagate the result of discriminator to the generator and update its parameters (dashed-line generator update). Repeating this process makes the discriminator converge to a high accuracy of discriminating between real and synthetic images and makes the generator converge towards generating synthetic samples very close to the real images.  
The generator takes a latent vector \(z\) and maps it to an \(8\times 8\) feature space using a fully connected layer. It then applies four transposed convolution layers with a kernel size of \(4\times 4\) and a stride of 2, progressively upsampling to \(128\times 128\). Each layer uses LeakyReLU activation (a = 0.2), except for the final convolutional layer, which applies Tanh to produce an image.  
The discriminator is a convolutional network that processes input images through five convolutional layers with a kernel size of \(5\times 5\), progressively downsampling the resolution from \(128\times 128\) to \(8\times 8\) with a stride of 2. Each layer uses LeakyReLU activation. The feature maps are then flattened, followed by a dropout layer (0.4) and a fully connected layer with a sigmoid activation for binary classification.  
We use the Inception Score (IS) and the Frechet Inception Distance (FID) to assess the quality and variety of generated images. The IS is calculated by a pretrained Inception V3 model applied to a sample of generated images. The FID is calculated by first extracting high-level features both from sets of real and generated images, then extracting the mean and covariance statistics of both samples and comparing them to each other.
## 5 Fault Location in Direct Current (DC) Grid Having Cable and Overhead Line Segments
### Three-Line Segment Model  
It is important to measure the traveling wave arrival time precisely because of the involvement of difference of arrival time of traveling waves at converter stations at two ends.  
A three-line segments model of HVdc system is shown in Figure 4.  
\(L_{1}\), \(L_{2}\), and \(L_{3}\) are lengths of the segments of a line. \(F_{1}\) is a fault located in segment (overhead line) \(L_{1}\). Similarly, \(F_{2}\) and \(F_{3}\) are the faults located within segments \(L_{2}\) (cable) and \(L_{3}\) (OH), respectively. \(x_{F1}\), \(x_{F2}\), \(x_{F3}\) are the distances of faults from the start of each segment \(L_{1}\), \(L_{2}\), and \(L_{3}\), respectively.  
Mathematical formulation involved in finding distance of fault from the VSC station in three-line segment model under different positions of fault is discussed through case-1 to case-3.
# Active Clamp Boost Converter with Blanking Time Tuning Considered
###### Abstract  
An active clamp boost converter with blanking time auto-tuned is presented herein, and this is implemented by an additional auxiliary switch, an additional resonant inductor, and an additional active clamp capacitor as compared with the conventional boost converter. In this structure, both the main and auxiliary switches have zero voltage switching (ZVS) turn-on as well as the output diode has zero current switching (ZCS) turn-off, causing the overall efficiency of the converter to be upgraded. Moreover, as the active clamp circuit is adopted, the voltage spike on the main switch can be suppressed to some extent whereas, because of this structure, although the input inductor is designed in the continuous conduction mode (CCM), the output diode can operate with ZCS turn-off, leading to the resonant inductor operating in the discontinuous conduction mode (DCM), hence there is no reverse recovery current during the turn-off period of the output diode. Furthermore, unlike the existing soft switching circuits, the auto-tuning technique based on a given look-up table is added to adjust the cut-off time point of the auxiliary switch to reduce the current flowing through the output diode, so that the overall efficiency is upgraded further. In this paper, basic operating principles, mathematic deductions, potential designs, and some experimental results are given. To sum up, the novelty of this paper is ZCS turn-off of the output diode, DCM operation of the resonant inductor, and auto-tuning of cut-off time point of the auxiliary switch. In addition, the efficiency of the proposed converter can be up to 96.9%.  
active clamp; auto-tuning; boost converter; ZCS; ZVS; FPGA; PWM +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Research Population and Technological Systems
#### 3.4.2.1 3D Scanning  
There are several technologies available to scan an object, both with contact and contactless. The most common 3D scanning systems are coordinate measuring machines (CMM) that physically scan ('touch') the part via tactile probing; and contactless 3D scanners that typically use a source of light, laser, ultrasound or x-ray and measure the wave reflection or residual radiation when sensing the part [95]. The 3D scanner used in this project was an Atos Q (manufactured by GOM, Braunschweig, Germany). This device works with structured light, which means that different light patterns are projected onto the object and the distortion is measured when the patterns are reflected on the object. Stereoscopic cameras take images of those patterns which are triangulated to calculate millions of data points forming a point cloud that resembles the scanned part. Thus, 3D scanning allows the sensing of the blade's contour and detects any irregularities, just like the operator with their hands.  
This technology has already been used in the quality assurance process of blade manufacturing to ensure that blade dimensions are correct and within limits, e.g., to confirm the minimum airfoil thickness of turbine blades is met [96]. However, it has not yet been applied to MRO whereby operational damages such as FOD must be detected, assessed and a serviceability decision made.
## 5 Conclusions  
In the scope of the Fourth Industrial Revolution, humans are being replaced by robots in several tasks. It aims to achieve more efficient mobility based on, among other things, self-driving cars, smart cities, intelligent industries, medical and lawyer counselling, and the use of intelligent drones for a plethora of areas, including in defense.  
Making use of mm-wave communications, massive MIMO, beamforming, and device-to-device communications, 5G communications are an enabler of the Fourth Industrial Revolution. Among the wide range of services supported by 5G communications, one can refer smart agriculture, smart cities, IoT, eHealth (using mMTC), autonomous vehicles, factory automation, remote surgeries (using URLLC), or providing wide coverage with an improved throughput several times higher than with 4G (using eMBB). Support a higher speed of communications and lower latency than 4G, as well as point-to-point communications, 5G is an important facilitator of the Fourth Industrial Revolution.  
NOMA is a strong multiple access candidate for new 5G releases, especially well-suited for mMTC. It was briefly shown that NOMA, using the block transmission technique SC-FDE and in the presence of a strong multipath fading channel, brings added value in terms of spectral efficiency as it allows multiple users' signals to be transmitted simultaneously in the same carrier frequency. Future research on NOMA will include performance evaluation in different user scenarios (more users, power levels, etc.).  
New services and requirements continue to advance and, consequently, the demands for communications increase. The future digital society, in the scope of increasing automation, namely the digital society of 2030 and beyond, comprises more and more IoT devices, including sensors, vehicles, aerial drones, and data. An advanced enabler of the Fourth Industrial Revolution, 6G will support higher communication speeds and lower latencies as well as new services, such as AR, XR, AI-infused applications, advanced IoT, BCI, holographic services, and mobility at higher speeds.  
Both authors have contributed equally to the article. Both authors have read and agreed to the published version of the manuscript.  
This work was funded by FCT/MCTES through national funds and, where applicable, co-funded EU funds under project UIDB/50008/2020.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Quantification of Individual Anthocyanins  
The analysis of anthocyanins was conducted by HPLC-DAD using an Agilent 1290 HPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped with a diode array detector (DAD). The separation was performed on a Poroshell 120 EC-C18 column (2.1 mm \(\times\) 100 mm; 4 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA) at 35 \({}^{\circ}\)C. The mobile phase consisted of 3% formic acid in water (A), and 90% Acetonitrile, 5% MeOH and 5% Water (B) at a flow rate of 0.35 mL/min. The gradient elution program was set as follow, modified from [PERSON], et al. (2014): 0-1.5 min, 5% B; 1.5-8 min, 5-15% B; 8-9.5 min, 15-20% B; 9.5-11.5 min, 20% B; 11.5-14 min, 20-22% B; 14-20 min, 22-28% B; 20-24, 28-30% B; 24-29 min, 30-90% B; 29-32 min, 90% B; 32-35 min, 90-5% B. The injection volume was 3 \(\upmu\)L and the wavelength used for anthocyanin detection was 520 nm. Quantification of cyanidin 3-galactosidase, cyanidin 3-glucoside (Cyn-3-Glu), cyanidin 3-arabinoside, penoidin 3-glucoside was carried out using standard curves of the analytical standards. The contents of penoidin 3-galactosidase and penoidin 3-arabinoside were determined by the sequence of peaks, and the concentrations were expressed as \(\upmu\)g Cyn-3-Glu equivalent per mL of wine.
## 1 Introduction
It provides three categories of lists for delineating amino acid properties. These lists delve into the biological and chemical attributes of single or paired amino acids, encompassing aspects like charge, polarity, mutability, and contact potential. Indexing amino acid features based on the amino acid index list has become a common method in bioinformatics [30]. Unified representation (UniRep) is a method that transforms any protein sequence into a fixed-length vector representation, addressing the scarcity of protein informatics data by leveraging full utilization of the original sequence [31; 32]. A key feature of UniRep is the numerical encoding of oligonucleotides, allowing for comparison and analysis of all oligonucleotide pairs occurring in the sequence (including overlaps). UniRep can learn the buried features of amino acids and incorporate the physicochemical properties of amino acid residue clusters in the protein. Using UniRep to predict amino acid features has significantly improved the performance of models and the method has been widely applied in protein engineering informatics. In this study, we developed the IL10-Stack model based on stacking using UniRep feature encoding. A non-stacking IL10-Fuse model was also constructed for comparison. Both models were used for IL-10-induced peptide identification. We also compared the performance of AAIndex and UniRep single and fusion feature extraction methods using different ML algorithms, and found that the UniRep single-feature stacking model had the highest accuracy. Notably, IL10-Stack outperformed existing prediction techniques in terms of five-fold cross-validation (MCC = 0.796 and ACC = 0.897) and independent testing (MCC = 0.820 and ACC = 0.910). Relative to IL-10 Pred and ILeukin10 Pred, the independent testing accuracy of IL10-Stack was improved by 12.1% and 4.0%, respectively. Overall, the IL10-Stack model developed in this study demonstrated higher accuracy and exhibited good robustness and generalization performance. Our main contributions are summarized as follows:  
* To address IL-10-induced peptide recognition from sequences, we transformed arbitrary protein sequences into fixed-length vector representations using sequence-based unified representation (UniRep). * We employed the powerful ensemble learning algorithm, stacking, to construct an IL-10-induced peptide prediction model, effectively enhancing the predictive accuracy. * After modeling single or fused sequence features using various machine learning algorithms, we observed that the stacking model based on UniRep encoding yielded the best results. Therefore, we proposed a novel IL-10-induced peptide recognition method, IL10-Stack, with significantly superior performance compared to existing methods.
## 3 Materials and Methods
### Remote Sensing ET Model (PT-JPL-Thermal)
#### 3.3.1. Canopy Transpiration  
Potential canopy evapotranspiration \(ETp_{c}\) was downscaled to actual \(ET_{c}\) by determining the fraction of the canopy actively transpiring (\(f_{g}\)) and considering some reductions to potential transpiration due to a moisture constraint (\(f_{m}\)) and a temperature constraint (\(f_{t}\)) for canopy transpiration.  
The green canopy fraction (\(f_{g}\)) is the ratio between the photosynthetic active radiation absorbed by the vegetation cover (\(f_{APAR}\)) acquired from the 8-day-composite MOD15A2, and the photosynthetic active radiation intercepted (\(f_{IPAR}\)), which is estimated as a function of the _NDVI_ acquired from the 16-day-composite MOD13A2 as in [PERSON] et al. [41]. The plant moisture constraint (\(f_{m}\)) considers a reduction of light utilization for canopy transpiration in response to water stress [41]. The plant temperature constraint (\(f_{t}\)) considers a decrease of canopy transpiration when the temperature differs from an optimum temperature range. Originally, \(T_{opt}\) was calculated individually for every pixel by selecting the value of air temperature associated with a maximum _NDVI_ and irradiance and minimum _VPD_, which is shown in [PERSON] et al. [41]. [PERSON] et al. [42] found that this approach led to unrealistic \(T_{opt}\) values in Mediterranean environments, obtaining better results using the Carnegie-Ames-Stanford Approach model (CASA) [42; 54]. In addition, the PT-JPL was not very sensitive to \(T_{opt}\) in arid areas tested by [PERSON] et al. [42] and, therefore, to avoid calibrations of \(T_{opt}\) depending on the site, it was fixed in 25 \({}^{\circ}\)C as in other global studies such as [PERSON] et al. [55] across a wide range of biomes.
# Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans
## 3 The RUL Criterion  
The RUL is defined as the time interval from the present moment until the connector reaches the end-of-life (EOL). The EOL indicates that the conductor has reached the end of its useful life, so it must be replaced by a new one because its thermal and electrical behaviors are below predefined levels. Therefore, a simple and unambiguous criterion to determine the EOL, and thus the RUL, is required. In this work, it is assumed that the EOL is attained when the resistance of the connector is increased by at least 40% with respect to its initial value \(R_{t\,=\,0}\), i.e., the RUL is the time interval between the current instant and the time in which the connectors resistance is 1.4\(\cdot R_{t\,=\,0}\). The 40% resistance increase corresponds to a limit value that takes into account an increase in power losses by 40% and a sufficient temperature rise to age the connector.  
Figure 1 shows how to determine the EOL and thus the RUL.  
Figure 1: Degradation trajectory of the electrical resistance and the proposed remaining useful life (RUL) criterion based on the end-of-life (EOL).  
The expected evolution of the resistance of the connector according to the IEC 61238-1-1 international standard [25] corresponds to a monotonic increase of resistance (see Figure 2), where three main stages are described. The first stage is known as initial formation region, where the resistance experiments an initial change just after the installation of the connector, during which stable constriction areas are stablished. This stage is followed by a relative stability region in which its resistance is almost constant. Finally, in the last stage, known as accelerated ageing or degradation, the resistance experiments a fast change because the connector is reaching its EOL.
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 6 Experiment and Analysis
### The Influence of Coverage Radius on Coverage Rate  
Keeping other parameter values unchanged, we studied the coverage rate changes with time when the swarm robots were not grouped or the swarm robots were divided into 3 groups, and the coverage distance \(d_{cover}\) was 1, 2, 4, 6, 8, and 10 respectively. Repeat the experiment for 30 times for each case and take the average result, as shown in Figures 17 and 18. As can be seen from the figures, the experimental effect is consistent whether the swarm robots are grouping or not. If the number of robots is large enough, as the coverage distance increases, the number of robots for coverage will decrease, and the  
Figure 16: The swarm robots were divided into 10 groups to cover the ‘K’ area.  
Figure 14: The swarm robots were divided into 3 groups to cover the ‘K’ area.  
Figure 15: The swarm robots were divided into 3 groups to cover the ‘house’ area.  
time for coverage rate to reach a stable level will also decrease, which means that the coverage time will also decrease continuously.  
If the coverage radius \(r\) of the individual robot remains unchanged, the factors affecting the coverage rate mainly include the coverage distance of the robot and the number of robots entering the coverage area. When the coverage distance of the robot increases to greater than its coverage radius (\(d_{cover}>r\)), it will lead to the coverage rate less than 100%. The larger the coverage distance is, the smaller the coverage rate will be.  
By comparing Figures 17 and 18, we can see some differences between ungrouped coverage and grouping coverage. With the same coverage distance, the stable coverage time of grouping coverage is significantly less than that of without grouping coverage. When the coverage distance is no more than 8, the coverage rate of the two cases is the same. When the coverage distance is equal to 10, it can be seen that in the case of grouping, the coverage rate decreases greatly. The main reason is that when the coverage distance is large, the gaps caused by conflicts between groups will also become larger and larger.