esa-sceva/satcom-chunk-collection huggingface dataset:
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 7 Performance Analysis of the Proposed ECSSP-SC  
In this section, a comparison of the performance of the ECSSP-SC and other related protocols has been made concerning the computational cost consumed, the security attributes satisfied, and the resistance capability against attacks. This comparison has been presented to justify that the proposed ECSSP-SC is computationally more cost-efficient in comparison to the other related protocols, and at the same time, also satisfies all the essential security functionalities. Since the registration is a one-time activity, the computational time and communication overhead of the registration phase has been ignored in the analysis.  
Before proceeding with the performance analysis, it is significant to compare the proposed ECSSP-SC with the related schemes with respect to the technique used for mutual authentication. Thus, a comparison of different smart card mutual authentication schemes based on the technique used are shown in Table 3.
## 3 Results and Discussion
### Experimental Validation of the Proposed Model
This is defined by Equation (29):  
\[MAPE=\frac{1}{n}\cdot\sum_{i=1}^{n}\left|\frac{M_{i}-E_{i}}{E_{i}}\right| \tag{29}\]  
where \(M_{i}\) is the model's result, \(E_{i}\) is the experimental result, and \(n\) is the number of cases. Table 4 indicates that the mean pressure error is 7.0%. This reduced value of the MAPE means that the proposed theoretical model is quite accurate in predicting the values of the hydraulic pressures of each of the cylinders. It should be noted that a large part of this MAPE is due not only to the proposed theoretical model, but also to the variability of the measurements, which is 6.4%.
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
###### Abstract  
Focusing on the three critical factors influencing laser communication systems operating in marine environments: atmospheric turbulence disturbances, atmospheric attenuation, and optical aberration effects, in this paper, we employ numerical simulation methods to systematically investigate the influence of four typical Zernike aberrations (defocus, y-coma, spherical aberration, and y-secondary quadrupole) on laser atmospheric transmission characteristics and system bit error rates. A comparison of their atmospheric transmission performance with that of the aberration-free state is also presented. The results show that reducing turbulence strength or increasing receiver aperture radius can effectively mitigate the scintillation effect of intensity fluctuations. Among the four typical aberrations, the fluctuation range of the relative change rate of the scintillation index for y-coma aberration relative to the aberration-free state is the largest. In weak turbulence and short-distance laser transmission over the sea, the beam drift caused by these four aberrations is not significant, and stronger turbulence strength or higher weight coefficients lead to more severe beam expansion. The on-axis logarithmic intensity probability density distribution of laser beams with different aberrations approximately follows a log-normal distribution. The skewness (S) and kurtosis (K) of the logarithmic intensity distribution are negatively correlated and always satisfy S \(<\) 0 and K \(>\) 0. Additionally, we found that as turbulence strength increases, turbulence effects significantly raise the required signal-to-noise ratio (SNR) values to achieve a bit error rate of \(10^{-9}\). When turbulence strength reaches a certain level, the impact weights of different aberrations on system performance may undergo changes. These results can provide theoretical references for the design and optimization of laser system parameters in marine laser communication.  
Article
## 3 Results and Discussion
### Analysis of the Prevailing Wind Direction  
For the analysis of the meteorological conditions present during the monitoring campaign, the data acquired by the station positioned at a height of 120 m, located inside the CTE central station were analyzed. The available data covers the entire sampling period (Figure 2).  
During the study, the frequencies of the wind direction showed the expected seasonal variability. In general, the winds from the East have low frequencies, in some periods they seem to be absent. The most frequent directions are those associated with the whole sector that goes from South-West to North-West, the winds from the South are predominant in the months of October and February, whereas the North-East sector is present with higher frequencies in the months of September, October, November and March.  
The wind directions change during the hours of the day: this occurs during the whole year. Until mid-morning the main frequency of wind directions are from North-East and from South-East; in the afternoon, the wind direction turns, and the West and North-West directions are predominant, with the North-North West sector prevalent during 16-19.  
The wind speeds show a variability related to the sector of origin of the winds. On average, higher speeds are associated with winds from North-East and South-East (Figure 3).  
Figure 2: Frequency distribution of wind direction month by month.
## 4 Discussion
### Parameter Analysis
Whether it is deduced with the help of the existing theory, or fitted by the field-test data, the power of the water permeability of rock strata (\(\omega\)) is 1.5 in the relationship equation between the unit grouting quantity and the water permeability of rock strata in karst curtain grouting. This shows that the equation is reasonable in theory and applicable in engineering application. Applicability and Limitation of the Prediction Method of the Unit Grouting Quantity in Karst Curtain Grouting Based on the Water Permeability of Rock Strata  
The parameter value in the theoretical relationship equation between the unit grouting quantity and the water permeability of rock strata in karst curtain grouting (Equation (16)) can be easily determined by extracting experimental data from field prototype test and using function fitting. Moreover, the required data sources are rich, reliable, and low in cost, and the method adopted has strong engineering pertinence and obvious technical feasibility, and the results obtained are relatively reasonable. Therefore, the prediction method of the unit grouting quantity proposed in this paper has good applicability to the karst curtain grouting project and can meet the needs of the design, construction, and theoretical research of karst curtain grouting, as well as provide an important theoretical basis for reference. However, the theoretical relation equation is put forward on the basis of the assumption of the rock mass characteristics of karst fissures, which is different from the engineering practice and needs to be further improved in practice. In addition, although the prediction method of the unit grouting quantity in karst curtain grouting based on the water permeability of rock strata proposed in this paper can ensure the quality and anti-seepage effect of karst curtain grouting, the question of whether the prediction result of this method is the optimal solution needs to be further studied.
# Robot Bionic Vision Technologies: A Review
## 2 Literature Review
### Advantages and Disadvantages of Robot Bionic Vision  
Compared to human vision, robot bionic vision has the following advantages [100; 101; 102; 103]:  
1. High accuracy: Human vision is 64 gray-levels, and the resolution of small targets is low [104]. Machine vision can identify significantly more gray levels and resolve micron-scale targets. Human visual adaptability is considerably strong and can identify a target in a complex and changing environment. However, color identification is easily influenced by a person's psychology. Humans cannot identify color quantitatively, and their gray-level identification can also be considered poor, normally seeing only 64 grayscale levels. Their ability to resolve and identify tiny objects is weak.
2. Fast: According to [PERSON] [105], the human brain can process images seen by the human eye within 13 ms, which is converted into approximately 75 frames per second. The results extend beyond the 100 ms recognized in earlier studies [106]. Bionic vision can use a 1000 frames per second frame rate or higher to realize rapid recognition in high-speed image movement, which is impossible for human beings.
3. High stability: Bionic vision detection equipment does not have fatigue problems, and there are no emotional fluctuations. Bionic vision is carefully executed according to the algorithm and requirements every time with high efficiency and stability. However, for large volumes of image detection or in the cases of high-speed or small-object detection, the human eye performs poorly. There is a relatively high rate of missed detections owing to fatigue or inability.
4. Information integration and retention: The amount of information obtained by bionic vision is comprehensive and traceable, and the relevant information can be easily retained and integrated.  
Robot bionic vision also has disadvantages such as a low level of intelligence, for it is unable to make subjective judgments, has poor adaptability, and has large initial investment cost. Furthermore, by incorporating non-visible light filling and photosensitive technology, bionic vision achieves night vision beyond the sensitivity range of the human eye. Bionic vision is not affected by severe environments, is not fatigued, can operate frequently, and has a low cost of continuous use. It simply requires an investment at the beginning to set up. Continued operation incurs only energy and maintenance costs.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Autoencoder Architecture Selection and Training Process  
To determine the optimal architecture (number of layers and neurons, activation function, etc.) of the autoencoder, a grid search approach is used, multiple configurations are tested and the one obtaining the lowest reconstruction error is chosen. Training time and complexity of the network have been considered. A process of trial and error of different configurations is necessary to determine the best structure for the available data; thus, a different dataset could result in a different network structure. The best network layout is a fully connected network composed of six layers, having respectively 7-12-4-12-7 neurons activated by the rectified linear unit (ReLU) function and mean squared error was used to measure the distance between the input and output, and the optimization algorithm is \"adam\".  
Having found the best network layout, its predictions on the training data are created to obtain the distribution of the reconstruction error, which is the difference between the original and the processeddata. The assumption that the reconstruction error does not contain systematic errors is verified, analyzing its distribution that resembles a normal distribution. Using this information, it is possible to determine a critical value to identify anomalies. Three standard deviations from the central value are utilized.
## 3 Materials and Methods
### Application of the Generate-and-Solve
Note that, when the original value is 1 (one), the value remains unchanged in order to ensure that the neighbour instance preserves the current feasible solution. ```
parameters:G&S time limit \(\tau_{G\&S}\), solver time limit \(\tau_{solver}\), maximum age value \(\alpha\), and mutation rate \(\rho\). input:Set of cutting patterns and demands of each type of item. output:The number of objects cut according to each cutting pattern. 1 Generate the initial reduced instance \(\iota\) using the greedy strategy
2 whiletime limit \(\tau_{G\&S}\) not exceededdo
3 Submit the reduced instance \(\iota\) model to the ILP solver with time limit \(\tau_{solver}\)
4 Let \(x^{*}\) be the solution returned by the ILP solver for the reduced instance \(\iota\)
5 Apply the aging mechanism to the reduced instance \(\iota\) with maximum age \(\alpha\)
6 Obtain a neighbour reduced instance \(\iota\) via mutation operator with rate \(\rho\)
7 return\(x^{*}\)
```  
**Algorithm 1**Pseudocode of the generate-and-solve method implemented. At each iteration of the G&S method, a new reduced instance is generated and submitted to the SRI to obtain a new best solution. For each reduced instance, we adopt a time limit \(\tau_{solver}\) of computation of the ILP solver. Furthermore, we consider an execution time limit \(\tau_{G\&S}\) as a stopping criterion for the G&S method.
# An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet
## 3 Proposed Algorithm
### Enhanced Networks  
The enhanced network is a convolutional network model based on the Retinex theory. The network framework is composed of a decomposed network and an improved network.  
The RetinexNet algorithm is divided into three steps: (1) the decomposition model uses a low illumination image and standard illumination image to estimate the illumination component and realize the decomposition of the reflection component and illumination component; (2) the adjustment model is used to de-noise the reflection component of the low light image, and encoder-decoder is used to capture an extensive range of light distribution context information for adaptive adjustment of the light component; (3) the reconstructed model restores the enhanced image from the processed reflection component and illumination component. The decomposition model is composed of 5 convolution layers with ReLU activate functions. The parameters of the low-light image and the typical light image are shared, and the reflection component and light component of the low-light image and the reflection component and light component of the normal light image are obtained. The constraint relationship between each component is used to optimize the model. The loss function of this model is composed of reconstruction loss \(L_{recon}\), reflectivity loss \(L_{ir}\) and light smoothing loss \(L_{is}\):  
\[L=L_{recon}+\gamma_{ir}L_{ir}+\gamma_{is}L_{is} \tag{3}\]  
where reconstruction loss \(L_{recon}=\sum\sum\gamma_{ij}\|R_{i}\ast I_{j}-S_{j}\|\), its objective is to make it possible to reconstruct the corresponding original image from the decomposed reflection component and illumination component. Reflectivity loss \(L_{ir}=\|R_{low}-R_{normal}\|\), the purpose is to make the reflection components of the paired low/standard light images as consistent as possible. Illumination component loss \(L_{is}=\sum_{i}\|\
abla I_{i}\ast\exp\left(-\gamma_{g}\
abla R_{i}\right)\|\), this loss is to make the area with smooth reflection component as soft as possible on the corresponding illumination component.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Resistance Against Differential Attacks
#### 3.6.3 Unified Average Changing Intensity (UACI)  
The UACI is another metric that is used to assess an encryption algorithm's ability to resist differential attacks by measuring the average change intensity between two cipher images that are produced by encrypting two plaintext images that are the same but differ in only one pixel [58]. Similar to the NPCR, a higher UACI value indicates a better resistance to differential attacks. The ideal value for UACI is \(\geq 33.4635\%\), as calculated by [PERSON] et al. [59]. The UACI value can be calculated as follows:  
\[UACI=\frac{1}{H\times W}\left[\frac{\sum_{i=1}^{H}\sum_{j=1}^{W}|C_{1}(i,j)-C_ {2}(i,j)|}{255}\right]\times 100\%, \tag{30}\]
# Study on the Leakage and Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
## 5 Results and Discussion
### Leakage Performance of Two-Stage Finger Seal
#### 5.1.2 Factors Affecting the Leakage of Two-Stage Finger Seal  
It can be seen from Figure 12 that the leakage of 2 SFS increases with the increase in axial pressure difference, so this section will not discuss the effect of axial pressure difference on the leakage of 2 SFS.  
When the radial displacement excitation of rotor is 30 \(\upmu\)m and the rotor speed is 10,000 \(\tau\)/min, Figure 14 describes the effect of side leakage flow on the leakage of 2 SFS, in which, \(\sigma=0\) represents the leakage of 2 SFS without considering side leakage flow. When the side leakage flow is considered, the leakage of 2 SFS is a little higher, and this is easy to understand. It also can be seen that the difference of leakage of 2 SFS is very small whether  
Figure 12: Comparison of leakage between 2 SFS and 1 SFS.  
Figure 13: Comparison of contact pressure between 2 SFS and 1 SFS.  
the side leakage flow is considered or not when the RMS roughness of finger element is small (\(\sigma\) = 0.5), however, when the RMS roughness of finger element is large (\(\sigma\) = 1.5), the influence of the side leakage flow on the leakage of 2 SFS cannot be ignored (the relative increment of leakage of 2 SFS is more than 15% when the side leakage flow is considered). Therefore, when the surface of finger element is relatively rough, the side leakage flow must be considered to better predict the leakage performance of 2 SFS.  
When the RMS roughness of finger element is 1 \(\upmu\)m and the rotor speed is 10,000 \(\uptau\)/min, the effect of radial displacement excitation of rotor on the leakage of 2 SFS is shown in Figure 15. It is clear that the leakage of 2 SFS increases when the radial displacement excitation of rotor increases. The increasing radial displacement excitation of rotor increases the radial hysteresis clearance of each 1 SFS in 2 SFS, which results in a stronger main leakage flow, so the leakage of 2 SFS increases.
# Mechanical Properties and Characteristics of the
## 1 Introduction  
Anatomically, several ligaments are located around the knee joint. Among these, the major ligaments of the knee joint that contribute to stability include the anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), medial collateral ligament (MCL), and lateral collateral ligament (LCL) [1; 2]. In addition, the anterolateral ligament (ALL) has recently come to be considered a key ligament that ensures the stability of the knee joint [3; 4]. These are considered as important structures in the field of biomechanical studies, ligament reconstruction for knee replacement, or sports injuries [5; 6; 7]. Biomechanical studies for most of the major knee ligaments have already been performed to assess their mechanical properties, commonly including the ACL and PCL, which are considered the main knee ligaments. [PERSON] et al. studied the mechanical properties and strain rate of the ACL and LCL [8], while [PERSON] et al. investigated the properties of the ACL according to age and load direction [9]. Meanwhile, [PERSON] et al. researched the ACL's tensile properties according to sex [10] and [PERSON] et al. and [PERSON] each reported on the mechanical properties of the PCL [11; 12]. Existing biomechanical studies have also focused on collateral ligaments.  
In 1976, [PERSON] et al. reported the strength and yield load of the MCL and LCL using cadavers [13], while, elsewhere, [PERSON] et al. studied the elastic modulus and yield stress of the LCL using young cadavers [14]. Separately, [PERSON] et al. researched yield load using the superficial and deep layers of the medial collateral ligament [15] and [PERSON] et al. suggested mechanical properties of the medial and lateral collateral ligaments [16]. Several other researchers have also focused on understanding the existence, morphological features, and role of the ALL [17; 18; 19]. Conversely, the mechanical properties of the ALL have been researched but only to a basic level as few studies to date have been published [20; 21; 22; 23]. Moreover, although the various mechanical properties of the major knee ligaments have been studied, these studies only focus on individual target ligaments. Thus, there remains a lack of research assessing the relative mechanical properties of each ligament. This study therefore seeks to investigate the mechanical properties of the MCL, LCL, and ALL using the same knee and to analyze the relative variation between these ligaments.
# An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
## 3 Weather Effects  
Under ideal operating conditions, AV sensor technology should perform as expected when it comes to perceiving the surrounding environment and executing necessary actions. However, adverse weather conditions (e.g., rain, snow, fog, unfavorable lighting conditions, etc.) can impose serious challenges to AV sensors and the algorithms distilling information from them. This section will summarize the effects of several common adverse weather phenomena on AV sensors in a quantitative manner.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature** & **LiDAR** & **RADAR** & **Camera** & **Ultrasonic** \\ \hline Primary Technology & Laser beam & Radio wave & Light & Sound wave \\ Range & \(\sim\)200 m & \(\sim\)250 m & \(\sim\)200 m & \(\sim\)5 m \\ Resolution & Good & Average & Very good & Poor \\ Affected by weather conditions & Yes & Yes & Yes & Yes \\ Affected by lighting conditions & No & No & Yes & No \\ Detects speed & Good & Very good & Poor & Poor \\ Detects distance & Good & Very good & Poor & Good \\ Interference susceptibility & Good & Poor & Very Good & Good \\ Size & Bulky & Small & Small & Small \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of autonomous vehicles’ sensors.
## 4 Results and Discussion  
Waveguide WR-75 is used as a feed source with iris effect that shows matching behavior within the frequency band of operation from 10 GHz to 15 GHz. The extra metal plate is designed with the internal dimension of 15 mm \(\times\) 7.5 mm that is kept at the mouth of the waveguide. The angular rotation of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\) are maintained with anticlockwise rotation that shows the beam steering characteristics as described in further sections. We have considered the simulation mesh as cells per wavelength values to maintain the simulation resolution. The Model and Background are set to four and cells per maximum model box edge have a Model of 10 and Background of 1, which shows that the numerical convergence has been reached for the obtained simulation results. Similarly, the waveguide port boundary condition is considered to obtain the simulated values.