esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Information Leakage Analysis of Merkle Tree-Based Authentication Schemes
### Analysis of Merkle Tree-Based Authentication
#### 3.1.1 Leakage of Data Size Information  
Looking at the communication between \(\mathcal{P}\) and \(\mathcal{V}\), an eavesdropper can figure out the approximate size of the underlying data from a single authentication proof. Specifically, the adversary can determine the length of sibling path(s) from the knowledge of the hash function in use, data block size, and the size of the proof transmitted by \(\mathcal{P}\). The minimum and maximum number of leaf nodes can be easily determined from the height of the tree (i.e., the length of the sibling path \(-1\)) when the Merkle tree is constructed in a left-to-right and bottom-up manner.  
Let us assume that the size of a single data block is \(|B|\), the size of hash value is \(|\mathcal{H}|\), and the size of the proof is \(|P|\). The length of the sibling path \(L\) can then be derived from \(L=|P|/|\mathcal{H}|\). When the length of the sibling path is acquired, \((L-1)\) becomes the height of the constructed Merkle tree, and the total size of target data \(S\) can be approximated as  
\[(2^{(L-2)}+1)\cdot|B|\leq|S|\leq 2^{(L-1)}\cdot|B|, \tag{1}\]  
where the right-hand-side of the inequality is the full and complete binary tree [24].  
This information about size obtained by eavesdropping can be used to narrow the attack space for the range of target sizes and filter out unnecessary data. It gives the attacker the powerful option to select target data of an appropriate size. Therefore, it is more desirable for an authentication method to hide size information of data.
## 1 Introduction
The results show that, compared with traditional tools, the micro-textured tool edge has the lowest interfacial friction and the cutting force is significantly reduced. Machining micro-textures on the rake face of the cutter can reduce the adhesion of the workpiece material [19]. [PERSON] et al. prepared symmetric conical micro-grooves and parallel micro-grooves on carbide cutters, and then studied the friction performance of the cutters [20]. [PERSON] et al. modeled the cutting force of the vertical milling cutter under the conditions of oblique cutting and proposed a mechanical model for predicting the cutting force of the vertical milling cutters [21]. [PERSON] et al. used a multi-level fuzzy comprehensive evaluation method based on multi-objective decision theory to evaluate the cutting performance of micro-textured cutters in titanium alloy processing [22]. [PERSON] studied the improvement of tribology and thermal environment of inconel-718 alloy by textured tools. The results reveal that the textured tools perform better, ensuring lower tool wear (VB), reduced cutting forces (Fc), lower surface roughness (Ra) and acceptable chip form [23,24]. In summary, placing micro-textures on the surface of a tool to improve the friction reduction and wear resistance of the tool has become a hot topic. However, in the process of milling titanium alloys, there is still a lack of theoretical research and experimental basis for in-depth study of the anti-wear and friction reduction mechanism of micro-textured cutters. Problems such as \"secondary cutting\" still exist during the cutting process of micro-textured cutters. Reasonable micro-texture arrangement can make the cutters have good anti-wear and anti-friction performance, and it can also solve the secondary wear problem of the micro-textured cutters, thereby improving the processing efficiency. Therefore, in this paper, by changing the single density distribution of the micro-texture in the cutter-chip close contact area, the change of the tool stress field during cutting titanium alloys with the variable density micro-textured cutter was studied. Based on the stress field, the texture variable density distribution of the micro-textured ball-end milling cutters was optimized. Experiment of Milling Titanium Alloy with the Variable Density Micro-Textured Ball-End Milling Cutter
# Computer Tools to Analyze Lung CT Changes after Radiotherapy
## 5 System Implementation
### Registration
#### 5.2.3 Elastic Lung Registration  
Another approach to the registration, implemented in our system, was the elastic registration. This algorithm was implemented using two libraries: SimpleITK and SimpleElastic. It needs to be noted that elastic registration is much slower and more sophisticated than affine. As seen in Figure 16, the first registration step gives quite satisfying results, so the elastic registration will not need to start the process from scratch. Many parameters are similar to those from the affine registration process, so below, we present only the chosen ones, which are different:  
1. Initial transformation: In this step, we chose the BSplineTransformation type.
2. Measure metric: It is analogous to the affine one.
3. Interpolator: It was also set as Linear.
4. Optimizer: The gradient descent optimizer was also chosen for elastic registration; however, its parameters slightly changed:  
* learningRate = 1,
* numberOfIterations = 255.
5. Multi-resolution framework: The parameters of the multi-resolution framework were also changed:
* shrinkFactors = [2,1],
* smoothingSignas = [1,0].  
The elastic registration algorithm was also executed for each pair of corresponding slices, where the moving slice was the one after affine registration, and the fixed one was invariably the segmented slice from the \"before\" series. An example of an image before and after elastic transformation is shown in Figure 19. The time of elastic registration for the whole series was 2103.4 s. Table 8 presents the SimpleITK \"one slice\" elastic registration results.  
In order to compare different implementations, the registration using the SimpleElastic library was also applied. It was also run for a pair of corresponding slices, where the moving slice was the one from the \"after\" series and the fixed one was the segmented slice from the \"before\" series. An example of an image before and after registration is shown in Figure 20.  
Additionally, in order to evaluate the registration process, the SimpleElastic library gives the possibility to display the final metric (the AdvancedMattesMutualInformation metric was used) value. Table 9 presents the SimpleElastic \"one slice\" registration results.  
Comparing these results with those presented in Figure 19, we can observe that these solutions differed from each other. Registering using SimpleITK did not change the lung structure significantly; however, at the same time, it did not make the images fully equivalent in terms of shape. The SimpleElastic approach preserved the shape better. Although SimpleElastic had a very good metric value (Table 9), we are not sure if the internal lung structure should be changed to such a large extent. Obviously, the parameters of the SimpleITK methods can also be changed in order to achieve results similar to those produced by SimpleElastic, but the calculation time increases dramatically.
## 2 Modeling of Subsystem
### Probability Density Function Fitting  
After the probability density of each interval is calculated, the \(k\) group data pair can be obtained as \((t_{i},f_{i})\). The probability density function can be fitted according to the data pair using the least squares method or maximum likelihood method for each subsystem by different distribution type, and the point estimation and interval estimation of the parameters of the probability density function can be executed [14].
## 1 Introduction  
Magnesium hydroxide was used as a potential coagulant for reactive dyes wastewater treatment for many years [1; 2; 3]. Typical characteristics of this kind of coagulant include nontoxicity, an environmentally-friendly nature, recoverability, and rapid reaction [4]. Magnesium hydroxide precipitate has a positive superficial charge and it can adsorb negative dyes through charge neutralization or precipitate enmeshment [5; 6]. Floc size settling properties are the main parameters influencing reactive dyes removal efficiency in real industrial scale unit operations [7; 8; 9]. A photometric dispersion technique and laser technique are useful in monitoring floc physical characteristics [10; 11]. As previously found [12; 13; 14], magnesium hydroxide nucleation and precipitation processes are very fast; floc size can reach to 15 m. Although floc size is not very large compared with other coagulants, the sedimentation process can meet the requirement of pollutant removal.  
During the magnesium hydroxide coagulation process, mixing conditions influenced the removal efficiency and floc properties. Rapid mixing brings the reactants together and homogenizes the solution. In the rapid stirring process, it will cause nucleation and precipitation of magnesium hydroxide. The magnesium hydroxide coagulation process had two stages including fast floc formation and growth of flocs. As for slow mixing, flocs remained stable and the re-growth process was not happened apparently in our previously found [13]. Several authors have found that operational conditions influenced the coagulation process and flocs properties especially for mixing conditions [11; 15]. Effects of shearing on floc formation and growth are also related to coagulation mechanisms [16; 17].  
Although there was a large amount of data on the characteristics of floc in the batch coagulation process, there have been limited studies on the relationship between floc characteristics and mixing conditions using magnesium hydroxide as a coagulant in the continuous process. The main objectives of this laboratory study were to evaluate the role of rapid mixing speed on coagulation performance and floc properties. Furthermore, the effects of retention time on floc characteristics are also assessed.
###### Abstract
These missions served as crucial stepping stones toward establishing a large, modular space station similar to the ISS [13]. - In 2021, China launched the Tianhe core module: the first module of its planned large modular space station known as the Chinese large modular space station (or the Tiangong Space Station). With this space station, China aims to sustain long-term human presence in space, conducting scientific and technological experiments just like the ISS [14]. - China's lunar exploration program has also been noteworthy. The Chang'e program, named after the Chinese moon goddess, has included multiple successful robotic missions to the Moon. Notably, Chang'e 5 successfully returned lunar samples to Earth in 2020, making China the third country to accomplish such a feat, after the U.S. and the former Soviet Union [15]. - China is also planning crewed lunar missions, potentially including the construction of a lunar base in the long term. In collaboration with Russia, China plans to construct an International Lunar Research Station (ILRS), a comprehensive scientific experiment base on the lunar surface and/or lunar orbit that will be built and utilized in multiple phases [16]. - Regarding Mars, China's Tianwen-1 mission successfully entered the Mars orbit in February 2021. This mission included an orbiter, a lander, and a rover, demonstrating a comprehensive approach to Martian exploration. The successful landing and operation of the Zhurong rover marked a major milestone for China's space program [17]. As is evident, the interest in exploring the Moon and Mars is truly international, with many nations planning missions or collaborations to extend humanity's reach and understanding of our solar system. Space stations have a complex and critical requirement for life support systems (LSSs) to maintain a safe and habitable environment for the crew. To achieve this, an intelligent ecosystem of life support systems must be designed and developed, which integrates multiple systems and functions to sustain the crew's life in space [18; 19; 20]. An intelligent ecosystem of life support systems must have several key features [21]. It must be robust and reliable, capable of functioning effectively in the extreme and harsh environment of space. The life support system must also be self-sufficient, capable of recycling and reusing resources as much as possible, such as water, air, and food, to minimize the need for resupply missions [22; 23]. This requires an analysis of alternative ways to provide the crew with the necessary means of life in order to optimize their methods of delivery and generation while ensuring their availability throughout the mission. At the same time, even at the early stages of design, arises the task of building a model that would make it possible to evaluate the economic efficiency of life support systems during the entire duration of the mission for various architectures of their construction. This article proposes one of the approaches to solve the problem of choosing the most effective architecture for the life support system, depending on the degree of autonomy and duration of the mission.
## 4 SMART DShot: ML-Enhanced Architecture
### Hardware Implementation Architecture  
The hardware architecture achieves efficient resource utilization through sophisticated pipeline design and resource sharing across modular components including APB interface for configuration, DShot protocol core supporting 8 channels, timing analyzer for error measurement, ML engine executing all four algorithms, neural network for anomaly detection, and feature extractor for real-time processing. Figure 6 presents the complete FPGA implementation architecture showing the modular design with detailed resource allocation and data flow paths.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric** & **KFTC** & **RLSTC** & **FLTC** & **HATC** \\ \hline Success Rate & 24.51\% & 26.94\% & 29.34\% & 31.01\% \\ Latency (ns) & 70 & 90 & 50 & 180 \\ Memory (KB) & 2.1 & 3.2 & 1.8 & 7.1 \\ Power (mW) & 3.2 & 4.1 & 2.8 & 8.9 \\ \hline Gaussian Noise & \(\star\star\star\star\) & \(\star\star\star\) & \(\star\star\) & \(\star\star\star\star\) \\ Linear Drift & \(\star\star\star\) & \(\star\star\star\star\) & \(\star\star\star\star\) \\ Burst Errors & \(\star\) & \(\star\) & \(\star\star\star\star\) & \(\star\star\star\star\) \\ Mixed Scenarios & \(\star\star\star\) & \(\star\star\star\) & \(\star\star\star\) & \(\star\star\star\star\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Algorithm performance characteristics.  
The hardware implementation demonstrates efficient resource utilization with detailed results presented in Table 4.
## 3 Results
### Fractal Dimension  
This study obtained the fractal dimension of coal samples based on AFM surface topography images, the cubic counting method [37; 38], the triangular prism method [37], and the variance partition method [39; 40]. The cubic counting method involves the following steps: A cubic lattice with a lattice constant I is superimposed onto the surface extended along the \(z\)-axis. Initially, I is set to X/2 (where X is the length of the surface side), resulting in a lattice of eight cubes. Then, N represents the number of all cubes containing at least one pixel in the image. Subsequently, the lattice constant I is progressively reduced by multiples of 2, and this process is repeated until I equals the distance between two adjacent pixels. The slope of the plot of lgN versus lg(1/I) represents the fractal dimension D\({}_{f}\). The calculation is as follows:  
\[\text{lg}N=D_{f}\text{lg}(\frac{1}{I})+B \tag{8}\]  
In the formula: N is the number of lattices filling the AFM topography image, dimensionless; D\({}_{f}\) is the slope, i.e., the fractal dimension; I is the side length of the smallest cubic lattice for the fractal, in nm; and B is a coefficient, dimensionless.  
The triangulation method, which bears a strong resemblance to the cubic counting method, operates as follows: The surface is overlaid with a grid of unit dimension I to ascertain the positions of multiple triangular vertices. For instance, when I = X/4, the surface is covered by 32 triangles of varying areas and tilt angles relative to the xy-plane. The areas of all triangles are calculated and summed to approximate the surface area S(I) corresponding to I. Subsequently, the grid size is continually halved, and the process is repeated until I corresponds to the distance between two adjacent pixels. At this point, the slope of the plot of lg(S(I)) versus lg(1/I) corresponds to D\({}_{f}-2\).  
The variance partitioning method is grounded in the variance scale dependence of fractional Brownian motion. This variance-based approach divides the entire surface into square boxes of equal size and calculates the variance (power of the root mean square of heights) for a specific box size. The fractal dimension D\({}_{f}\) is assessed based on the slope \(\beta\) of the least squares regression line fitted to the data points in the log-log plot of variance, i.e., D\({}_{f}\) = 3 - \(\beta\)/2.  
Due to the existence of different systematic errors among various fractal analysis methods, the fractal dimensions of each volatile coal sample based on the cube-counting method, triangulation method, and variance partition method were calculated separately. The data are presented in Table 3. According to the three-dimensional analysis theory [41; 42], the closer the fractal dimension is to 3, the more complex the pore shape; the closer it is to 2, the more regular the pore shape. The fractal results of different volatile coal samples using the cube-counting method are shown in Figure 3, where the slope of the equation represents
# A Lightweight Hash-Based Blockchain Architecture for Industrial IoT
## 3 Proposed Lightweight Hash Based Blockchain Architecture for IIoT
### Key Features of Proposed Blockchain  
#### 3.2.1 Selection of Hash Functions: Lightweight Hash List  
We created a lightweight hash list in the LW hash manager, containing lightweight hash functions that are used for block mining. When we constructed the hash list, we aimed to select the proper hash algorithm, which can serve security cryptographically and can be implemented using a small area. Moreover, we selected some lightweight hash functions according to our two conditions. First, the lightweight hash function has a 256-bit output size. If the output size is less than 256-bit, its security strength is too small. The practice case of the attack to SHA-1, which has a 160-bit output size, was completed successfully by SHAttered [32]. Second, because the proposed architecture focuses on resource-constrained devices such as IoT devices, they are compactly designed to perform their specific purpose. Therefore, they do not have sufficient memory or power to calculate area or implement a hardware module because the device can perform a specific purpose. The area for implementing the lightweight hash function is under the 5000 GEs, which is less than the area of the general hash function. In real-case, some microchips generally used in IoT, such as MSP and ARM processors, are providing cryptographic hash engine (e.g., SHA-1 and SHA-2) that implements in hardware and the area for implementation of SHA-1 and SHA-2 are approximately 5500 GEs and 10,900 GEs, respectively [33]. Cryptolux [34] provides a list of lightweight hash functions proposed recently  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Function** & **Description** & **Related Component** \\ \hline Block validation & Validating the block requested from the other node & Block Validation Manager \\ management & and return the result of the validation process & Shared ledger manager \\ \hline Block update & & \\ management & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Functions and related component of the storage node.  
Figure 1: Overview of proposed lightweight hash-based blockchain architecture.  
We select three hash functions in their hash list: QUARK, PHOTON, and SPONGENT; it can also be found in Table 5. These hash functions have different features in the perspective of security and performance. For example, even though QUARK shows the best throughput in the hash list, it has lower resistance than other hash functions. However, it does not mean this hash function is not secure. These hash functions have sufficient security level against cryptographic attacks. The hash list can support that block mining process quickly.
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 1 Introduction
According to the above research, we propose a new resilience measure and modeling method for LEO-SCS based on uncertainty theory. Our contributions are as follows:  
1. A belief resilience measure is proposed based on uncertainty theory [14; 15] and is used to quantify the system resilience as the chance that the system meets the service requirement under disruptions, considering both aleatory and epistemic uncertainties;
2. An uncertain satellite network evolution model is proposed for the LEO-SCS, which quantifies both the end-to-end aleatory and epistemic uncertainties and also considers dynamic topology changes;
3. A resilience evaluation method is proposed for the LEO-SCS, under which system resilience under different backup strategies can be compared and the results can be used in design decisions. The remainder of this paper is organized as follows. Section 2 briefly introduces uncertainty and chance measures. A belief resilience measure is proposed in Section 3. Section 4 introduces the LEO-SCS and provides an uncertain satellite network evolution model for it. A resilience evaluation method for the LEO-SCS is presented in Section 5. Section 6 illustrates the effectiveness of our proposed methodology through a specific case and discusses the impact of different backup strategies on system resilience. Concluding remarks are provided in Section 7.