esa-sceva/satcom-chunk-collection huggingface dataset:
# ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
## 6 Results and Discussion
### Segmentation
\begin{table}
\begin{tabular}{l l l} \hline
**ISCC–NBS System Name** & **Algorithm’s Classification** & **Algorithm’s Full Description** \\ \hline Red (R) & Red & 86.09\% Red and 13.91\% Pink \\ Yellowish Pink (Ypk) & **Red** & 58.77\% Brown, 40.04\% Red, and 1.19\% Pink \\ Reddish Orange (Ro) & Deep Orange & 78.67\% Deep Orange and 21.33\% Red \\ Orange (O) & Light Orange & 62.69\% Light Orange and 37.31\% Deep Orange \\ Orange Yellow (OY) & Light Orange & 93.41\% Light Orange and 6.59\% Yellow \\ Yellow (Y) & Yellow & 55.69\% Yellow and 44.41\% Light Orange \\ Greenish Yellow (Gy) & Yellow & 97.24\% Yellow and 2.76\% Green \\ Olive (Ol) & Yellow & 97.61\% Yellow and 2.39\% Light Orange \\ Olive Green (Olgr) & Green & 74.38\% Green and 25.62\% Yellow \\ Yellow-green (YG) & Green & 64.75\% Green and 35.24\% Yellow \\ Green (G) & Green & 62.73\% Green and 37.27\% Teal \\ Yellowish Green (Yg) & Green & 98.27\% Green and 1.73\% Teal \\ Blushi Green (Bg) & Teal & 86.26\% Teal and 13.71\% Green \\ Greenish Blue (Cb) & Teal & 75.88\% Teal and 24.12\% Blue \\ Blue (B) & Blue & 88.12\% Blue and 11.78\% Ultramarine \\ Purplish Blue (Pb) & Ultramarine & 100\% Ultramarine \\ Violet (V) & Purple & 57.27\% Purple and 42.73\% Ultramarine \\ Purple (P) & Purple & 73.47\% Purple and 26.53\% Pink \\ Pink (P) & Pink & 74.31\% Pink and 25.69\% Red \\ Reddish Purple (Rp) & Pink & 100\% Pink \\ Purplish Red (Pr) & Pink & 82.42\% Pink and 17.58\% Red \\ Purplish Pink (Ppk) & Pink & 85.01\% Pink and 14.99\% Purple \\ Brown (Br) & Brown & 77.72\% Brown, 7.16\% Light Orange, and 15.12\% Deep Orange \\ Reddish Brown (Rbr) & Brown & 50.10\% Brown, 8.28\% Deep Orange, and 41.62\% Red \\ Yellowish Brown (Ybr) & Brown & 69\% Brown and 31\% Light Orange \\ Olive Brown (Olbr) & Brown & 64.40\% Brown, 26\% Light Orange, and 9.60\% Yellow \\ White (Wh) & Neutral & 67.50\% Yellow and 32.50\% Light Orange \\ Gray (Gy) & Neutral & 67.50\% Yellow and 32.50\% Light Orange \\ Black (Bk) & Neutral & 67.50\% Yellow and 32.50\% Light Orange \\ \hline \end{tabular}
\end{table} 
Table 1: Full analysis of each shade of the official ISCC–NBS color classification Level 2.  
methods built for object segmentation, based on annotations of colors as attributes, will not take notice of these variations. The particular task will thus define the adequacy of the method. Nevertheless, in order to provide a quantitative comparison of ABANICCO to the state-of-the-art methods, we incorporated a simple step of class remodeling into the output provided by ABANICCO, resulting in accurate image segmentation.
# Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm
## 3 Mathematical Formulation of the Optimization Problem
### Chu-Besley Genetic Algorithm Applied to the Problem of Parameter Estimation
#### 3.1.2 Descendant Generation  
In this iterative process, a child is produced in each generation as a result of three steps: (1) selection of individuals from the initial population, known as parents; (2) recombination of the information contained in the selected parents; (3) mutation of the combined information of the parents. These three steps are explained in detail below.  
* Selection: Two parents are randomly selected from the initial population, where the two parents should be different individuals. In this work, the function \(randperm(p,2)\) from MATLAB was used, which returns a vector with two unique integers selected randomly from 1 to \(p\). Figure 2 shows an example population with five parameters and six individuals, where Individuals 2 and 6 have been randomly selected as parents.
* Crossover: At this step, information from both parents is combined to obtain descendants (offspring). For this purpose, a crossover point should be randomly selected in the vectors of the individuals selected as parents. The function \(randi\) from MATLAB allows users to generate a value, vector, or matrix with random integers uniformly distributed in a predefined interval; thus, it was used in this work to obtain the crossover point. In this case, since this work is intended for parameters with real values, the position of each one of the parameters should be respected because their search ranges are usually different. Figure 3 shows an example of recombination where Parameter 3 was selected as the crossover point.  
Figure 2: Selection process in the genetic algorithm.  
* Mutation: At this step, variations are introduced in the offspring while preserving information inherited from their parents. Initially, it is decided whether or not both offspring will be mutated based on a randomized number that can take the values 0 or 1. If the value is 0, no mutation is performed; but, if it is 1, mutation takes place. If the decision is to mutate the offspring, the parameter to be mutated is randomly selected and replaced by a random value within its search range. Figure 4 shows this process. _Random selection_
###### Abstract  
With the wide application of deep learning, power inspection technology has made great progress. However, substation inspection videos often present challenges such as complex backgrounds, uneven lighting distribution, variations in the appearance of power equipment targets, and occlusions, which increase the difficulty of object segmentation and tracking, thereby adversely affecting the accuracy and reliability of power equipment condition monitoring. In this paper, a pixel-level equalized memory matching network (PEMMN) for power intelligent inspection segmentation and tracking is proposed. Firstly, an equalized memory matching network is designed to collect historical information about the target using a memory bank, in which a pixel-level equalized matching method is used to ensure that the reference frame information can be transferred to the current frame reliably, guiding the segmentation tracker to focus on the most informative region in the current frame. Then, to prevent memory explosion and the accumulation of segmentation template errors, a mask quality evaluation module is introduced to obtain the confidence level of the current segmentation result so as to selectively store the frames with high segmentation quality to ensure the reliability of the memory update. Finally, the synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. Experimental results show that the method performs excellently on real substation inspection scenarios and three generalized datasets and has high practical value.  
substation intelligent inspection; deep learning; object segmentation and tracking; feature matching; memory updating +
Footnote †: journal: _Algorithms_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Segmentation and Tracking Based on Equalized Memory Matching Network and Its Application in Electric Substation Inspection. _Algorithms_**2024**, 17, 203. [[https://doi.org/10.3390/a17050203](https://doi.org/10.3390/a17050203)]([https://doi.org/10.3390/a17050203](https://doi.org/10.3390/a17050203))
# Airport pavement Maintenance Decision-Making System with Condition Cases Optimization
## 2 Airport Pavement Management System Architecture
### SL Performance Curve  
A relationship between the maintenance history and SL scores was analyzed to develop performance curves for pavement serviceability. Data collection commenced after the establishment of the airport pavement management system in 2011, and given that regular assessments are conducted every five years, as of 2023, a maximum of three sets of evaluation data had been accumulated. To reflect the actual behavior of pavements, various curve shapes such as linear, nonlinear, and polynomial were considered. Considering the limited number of data and continuity with KAC's existing analysis methodology, a linear model was selected in the initial stage of establishing PMS. It was assumed that the pavement would return to its original design condition (100 points) upon reconstruction or overlay. Figure 2 illustrates the SL performance curves for runways, taxiways, and aprons.  
Figure 2: SL performance curve for flexible pavement.
# Continuous Variable Quantum Secret Sharing with Fairness
## 4 Conclusions  
We have suggested a CVQSS scheme with fairness to resist dishonest participants keeping silence or returning error shares after receiving other ones' shares, which would be detected in verifying process. The participants release their shares interactively without simultaneity, but they can or can not reconstruct secret simultaneously. The above perspectives can be concluded from the discussion on fairness property, in which all five cases are enumerated and analyzed in detail. Of course, without loss of generality, participants cannot deduce the secret from their own shares independently.  
The two-mode squeezed states are indispensable in our scheme and are exploited to transmit deterministic shares. As proved, this communication can send shares successfully, as long as setting proper the squeezing and modulation parameters according to the quantum channel transmission efficiency and the Shannon information of shares. Although, there must be eavesdropping in the channels, owing to the communication based on the two-mode squeezed states, legal communicators can detect the eavesdropper. In order to ensure the message directly transmitted succesfully, the channel transmission efficiency should be greater than 0.79. But, if the encrypted message is sent in the quantum channel, the infomation leakage of shares can be neglected, so that the demand of channel transmission efficiency can be regard as the conditon under no attack, that is lower to 0.08. However the increment of participants in the quantum network would greatly add the times of communication in this protocol. With the rapid development of \(n\)-particle CV entanglement, the CV GHZ can be invited to distribute shares which will make our scheme higher efficiency and more practical.  
[PERSON] gave a general idea of the study, designed the concept of the study and wrote the article and completed the derivation of formulae and simulation analysis. [PERSON] and [PERSON] provided practical recommendations for this study. [PERSON] and [PERSON] revised some of the content in the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61572529, 61871407, 61872390, 61801522), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 18 KJB510045).
# Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication
###### Abstract  
In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.  
ECG; authentication; RNN; LSTM; deep networks Article
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 8 Evaluation
### Evaluation of Code Footprint  
Our bootstrapping solution on the peer devices consumes approximately 69 kB of flash memory. The maximum RAM consumption of the device is approximately 20 kB. In Figure 7, we illustrate the sizes of each component of our bootstrapping solution. The left hand side shows the flash memory consumption and the right hand side shows the RAM consumption.  
For detailed usage of each component, we extracted and divided the data into seven modules as follows: First, we use a basic implementation of Contiki-NG to get the memory requirements of the OS itself. These data are obtained by running the _Hello World_ example. We then add the CoAP library with a basic example of receiving and sending messages. The CoAP module also includes the integrated UDP library. The evaluation of the remaining modules is performed over the Contiki-NG base plus the CoAP (over UDP) implementation.  
The measurements for EAP-NOOB include all of its sub-components, such as its state machine and the JSON parsing functionality. However, it does not include the memory consumed by cryptographic operations. This information is separately illustrated in the ECC Cryptography and SHA256 modules. This is because we use the Public Key Hardware Accelerator (PKA) driver available on the Firefly motes for elliptic curve secp256r1 and SHA-256 hash operations. Keeping these data separate can also help in future work to investigate if these operations can be performed without additional hardware. OOB LED shows the memory requirements for the blinking LED light process.  
Contiki-NG offers two MAC layer scheduling options: Carrier Sense Multiple Access (CSMA) and Time Slotted Channel Hopping (TSCH). It allows for communication with other motes with CSMA without any scheduling tool. In Figure 7, we indicate the overhead TSCH Scheduling when compared to the  
Figure 7: Memory consumption in Contiki. (**Left**) Static memory: Flash; (**Right**) Dynamic memory: RAM.  
standard CSMA. It is evident that TSCH scheduling significantly increases the memory consumption and should only be used in critical networks that require high reliability and deterministic behavior.
## 4 Results and Discussion
### Frequency Sweep Test  
The bio-asphalt with 5% bio-oil and base asphalt were selected for the frequency sweep test, according to the results of conventional tests and the temperature sweep test. The changes of phase angle, storage modulus, loss modulus, and complex modulus with frequency for the unaged and RTFO-aged were analyzed.  
Figure 11 shows the changes of phase angle, storage modulus, loss modulus, and complex modulus of unaged bio-asphalt with 5% bio-oil content. As can be seen in Figure 11, for the unaged bio-asphalt with 5% content bio-oil, with the increase of frequency, the phase angle reduced gradually at the same temperature, while the storage modulus, loss modulus, and complex modulus all increased gradually. This indicated that the asphalt exhibited greater elastic properties as the frequency increased, while the phase angle and the storage modulus had a certain fluctuation at 76 \({}^{\circ}\)C, which may be a measurement error caused by the increase in temperature. In the case of lower frequencies, the phase angle, storage modulus, loss modulus, and complex modulus varied greatly with the change of frequency. When the frequency was higher than 5 Hz, the viscoelastic curve stabilized gradually. As the temperature increased, the phase angle increased gradually, and the storage modulus, loss modulus, and complex modulus decreased gradually and the viscosity component of the bio-asphalt increased; this was consistent with the results of the temperature sweep.  
Figure 11: The changes of different parameters of unaged bio-asphalt with 5% content bio-oil. (**a**) Phase angle; (**b**) Storage modulus; (**c**) Loss modulus; (**d**) Complex modulus.  
Figure 12 shows the changes of phase angle, storage modulus, loss modulus, and complex modulus of RTFO-aged bio-asphalt with 5% content bio-oil. It can be seen that the changes of phase angle, storage modulus, loss modulus and complex modulus of asphalt were similar to those of the unaged asphalt.
## 4 Results and Evaluation
### Objective Evaluations
#### 4.1.1 Evaluation of Noise Reduction Performance
Therefore, while MSE may not be a perfect indicator of audio quality, it does serve as a useful metric for evaluating how closely the processed image resembles the original one in terms of their pixel-level similarity. SNR works with the converted signal and reflects whether the amount of noise in the signal is \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use Case** & \multicolumn{1}{c}{**MSE**} & \multicolumn{1}{c}{**SNR**} \\ \hline \multirow{3}{*}{Aircrafts} & Wiener & \(0.0006\pm 0.0035\) & \(16.378\pm 7.4762\) \\  & DAP Design & \(0.0761\pm 0.1621\) & - \\  & Our model & \(\mathbf{0.0182\pm 0.0234}\) & \(\mathbf{1.3702\pm 0.8983}\) \\ \cline{2-4}  & Wiener & \(0.0007\pm 0.0064\) & \(16.908\pm 6.8374\) \\ \multirow{3}{*}{Storms and rain} & DAP Design & \(0.0877\pm 0.0879\) & - \\  & Our model & \(\mathbf{0.0202\pm 0.0758}\) & \(\mathbf{-1.2728\pm 3.7621}\) \\ \cline{2-4}  & Wiener & \(0.0006\pm 0.0036\) & \(17.328\pm 13.1234\) \\ \multirow{3}{*}{Traffic jams} & DAP Design & \(0.0693\pm 0.3245\) & - \\  & Our model & \(\mathbf{0.0154\pm 0.2522}\) & \(\mathbf{0.4991\pm 1.3253}\) \\ \cline{2-4}  & Wiener & \(0.0004\pm 0.0003\) & \(17.505\pm 9.6523\) \\ \multirow{3}{*}{Wind} & DAP Design & \(0.0687\pm 0.0319\) & - \\  & Our model & \(\mathbf{0.0182\pm 0.9325}\) & \(\mathbf{-0.1870\pm 3.2167}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison against baselines. high or low. A combination of both metrics provides a more comprehensive evaluation of the model's performance in both the image and audio domains. Based on the results shown in Table 3, our model outperforms in all scenarios, reducing both the image noise and the actual noise in the subsequently converted signal. In terms of MSE, Wiener filters perform poorly across all four use cases, as the audios being compared are nearly identical. The DAP Design shows better results, but it is important to note that this method removes all information, including speech, resulting in silent audio. Obviously, in this case, the MSE value should be high, as noisy audio differs significantly from silent audio. MSE can still be calculated when comparing images, even if the audio signal itself is inaudible. Regarding the Signal-to-Noise Ratio (SNR), our model significantly outperforms Wiener filters. On the other hand, DAP Design's approach leads to silent audio, rendering the SNR value empty. When we look at the results for different situations, it seems that storm and rain noises are easier to eliminate when evaluated using both MSE and SNR metrics. When considering the standard deviation, instances involving wind noise tend to yield poorer results in some examples. This variability in performance across different types of environmental noise underscores the complexity of the problem and the need for a more nuanced approach to noise reduction in audio signals.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 2 Mathematical Model of the Mobile Photovoltaic-Diesel-Storage Microgrid System  
The mobile photovoltaic-diesel-storage microgrid system (MPDSMS) consists of a variety of renewable energy generations in addition to conventional power generation and storage. The energy sources considered in this work include photovoltaic power generation, diesel generators, primary batteries, and secondary batteries. The photovoltaic generator energy storage system is based on integrating small-scale generators with corresponding AC/DC conversion devices.  
This system operates by supplying freshly generated electricity to the load for immediate use, and then recharging the battery pack with any surplus energy once the load's consumption is complete. To ensure quick power restoration, the diesel generator activates only when the energy from the battery pack combined with the electricity produced by the new energy generation component is insufficient to meet the load's requirements. Employing a diesel engine as a backup power supply can significantly enhance the power supply's overall efficiency and stability. The structure of the mobile microgrid system is shown in Figure 1.
## 4 Results
### Additional Biochemical Traits Detected
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Biochemical Traits and Plant** & **Spectral Range and Instrument** & **Dimensionality Reduction Techniques** & **Accuracy and Technique** & **Number of Samples** & **Ref.** \\ \hline Vitamin C, Lettuce & Hyperspectral Camera: 350 nm-2500 nm & Data dimensionality: First-order derivative (FDR) of reflectance was used to find the 13 most important principal components. & Multivariate PCR & \\ \hline Vitamin C, SelectTran & Near-Infrared Spectroscopy (NIRS): 1600 nm-2400 nm & Data Dimensionality: Principal Component Analysis (PCA) was used to decompose and compress the data matrix and detect outliers before calibration modeling. & & \\ \hline  & Data dimensionality: Three methods: - Competitive adaptive reweighted sampling (CARS) was used to eliminate the irrelevant variables of P1. & CARS-PLSR & & \\ Water Gentent, Spectrometer: 305 nm-1800 nm & Dual-channel diode array - Vis (PRI, PRI 500, PRI norm, RDVL, WPI 8 = 0.16 \(p\) = 0.019 & & \\ Water Gentent, RVC), Spinach & 305 nm-1800 nm & VOGRE13, NDVI, mrNDVL, ICARI, REP, WI & PRI 8 = 0.16 \(p\) = 0.019 & \\  & calculated from the spectrum & Best PI inner model: & & \\  & - Parameters based on infection (IPs) of the spectrum & IP1 \({}^{2}\) = 0.05 \(p\) = 0.001 & & \\ Water Stress, SelectTran & Spectrograph + CCD camera: 400 nm-1000 nm & Data dimensionality: to reduce the computational load of the CNN, an attention mechanism was employed to focus on significant features of the spectra & Prediction of water stress based on time series & 433 spectra phenotypes combined with the best deep learning (560 plants: (Optimal-Spec) models based on & 433 spectra (Optimal-Spec) models based on & 436 \\  & & mechanism was employed to focus on significant features of the spectra & hyperspectral data & 200 head; and Optimal-Spec:TDA Accuracy = 96.59 percent & 100 leaf; and Optimal-Spec:TDA Accuracy = 98.86 percent & 100 Roman) \\ \hline \end{tabular}
\end{table} 
Table 19: Additional Biochemical Traits. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Biochemical Traits and Plant** & **Spectral Range and Instrument** & **Dimensionality Reduction Techniques** & **Accuracy and Technique** & **Number of Samples** & **Ref. ** \\ \hline Calcium, Latitude & & Data dimensionality: Four linear regression fit models were used to identify the 24 most significant wave bands (out of 448). The inputs of these models were reflectance, the first derivative of the reflectance, and two spectral indices (RSI, NDSI). After that, the bands were reduced to 22 due to the very small improvement of the regression models using 24 bands.