esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
## 4 Scalable Distribution-Based Clustering Algorithms
### Spkms  
The latest algorithm proposed in this work, _Scalable PK-Means for Stream_ (SPKMS), is a derivation of SPGMS that aims to improve the computational complexity of the scalable EM algorithm, known to be costly [27]. Furthermore, the authors of PG-means [19] suggested other clustering algorithms as an alternative for EM if processing time is relevant. Following such advice, we have chosen \(k\)-means due to its influence and low computational complexity. While EM clusters data as a mixture of distributions, \(k\)-means partition the data, which may obtain different results.  
Presented in Algorithm 3, the SPK-means performs projections over data and model and uses KS statistics to test their goodness-of-fit, similarly to SPG-means (Algorithm 2). If a projection leads KS to reject the null hypothesis that the data and model belong to the same distribution, SPKMS prepares to learn a new cluster. In line 10, the algorithm selects ten new prototypes \(C\): half of them randomly, and the other half are the farthest data points from the current centroids, as done in \(k\)-means++ [18]. This selection is made by a map job, which calculates the distance between each data point and its closest centroid, followed by a reduce job, which obtains the farthest data point. In line 11, a set of ten models \(M^{\prime}\) are derived from the already learned one (\(M\)), each containing one of the selected prototypes \(C\).  
```
Data:\(X\), confidence level \(\alpha\), number of projections \(r\) Result: clustering model \(M\)
1 let \(k\gets 1\);
2 model \(M\leftarrow\) 1-cluster by mean of \(X\);
3\(v\leftarrow\) draw randomly \(r\) projection vectors;
4\(p_{j}\leftarrow\) projection of \(X\) against \(v_{j}\), \(\forall j\in v\);
5\(m_{j}\leftarrow\) projection of \(M\) against \(v_{j}\), \(\forall j\in v\);
6\(ks_{j}\leftarrow\) KS(\(p_{j}\), \(m_{j}\), \(\alpha\)), \(\forall j\in v\);
7 ifno\(ks_{j}\) rejected null hypothesis, \(\forall j\in v\)then
8 return \(M\);
9
10 end if
11\(C\leftarrow\) select 10 prototypes;
12\(M^{\prime}\gets M+c_{s}\forall c_{s}\in C\);
13\(M^{\prime\prime}\gets\)\(k\)-means(\(X,M^{\prime}\));
14\(M\leftarrow\) best \(M^{\prime\prime}_{l}\in M^{\prime\prime}\);
15\(k\gets k+1\);
16 if\(k=k_{max}\)then
17 return \(M\);
18
19 end if
20 go to line 3;
```  
**Algorithm 3**SPK-means overview  
In line 12 of Algorithm 3, a scalable version of \(k\)-means refines the set of models \(M^{\prime}\), using each data reading to update all the models at once. Then, the scalable version of the SS index evaluates the refined models \(M^{\prime\prime}\) in parallel and replaces \(M\) with the best-evaluated model. Similar to SPG-means, \(M\) is returned if \(k_{max}\) is reached; otherwise, the process continues iterating from line 3.
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 2 Literature Review  
_selfish mining_ (SM) was first introduced as an attack vector by [PERSON] and [PERSON] [2] in 2014, and it is sometimes referred to as _block withholding_[8]. This strategy shows that under certain conditions a deviation from the standard Proof-of-Work protocols may be a more profitable strategy for a miner. The definition of SM attack has attracted widespread attention and many extensions to the strategy have been proposed, such as stubborn mining and publish-\(n\) strategy [3, 9, 10]. As a response, many approaches to defending honest actors against selfish mining have also been proposed. They can be categorised into two streams: (1) making fundamental changes to the block validity rules, for example, _ZeroBlock_[11]: A timestamp-free solution which requires that each block must be generated and received by the network within a maximum acceptable time interval, and (2) decreasing the probability of honest miners working on the selfish miner's chain during a fork, for example, _weighted FRP_[12]: Here miners compare the weight of the chains instead of their length. In 2020, [PERSON] [13] summarised 20 primary selfish mining attack countermeasures using the proposed taxonomy of defensive strategies and analyzed the benefits and limitations. From his summary, one can find that most of the existing modelling methods of selfish mining are focused on optimizing the accuracy of detection, lacking the analysis of the importance of the underlying network dynamics in selfish mining.  
In 2021, [PERSON] et al. [14] introduced a stochastic model of consensus in blockchain-based systems, with Proof-of-Work (PoW) as a primary example. The authors show a rapid decrease in the fraction of time the system is in consensus when the network delay decreases, and further show that heterogeneity in mining power leads to an amplified inequality in blocks contained in the main chain, while improving the overall consensus efficiency. The results are consistent with [15] who deployed a synthetic Ethereum network with predefined, adjustable network delays. [PERSON]'s minimalistic model allows flexibly adjusting network delay and interblock time as control parameters, as well as the distribution of mining power, which provides the basis for further research of the dynamics of the peer-to-peer network under PoW protocol.  
In addition, it is debated whether the selfish mining strategy is profitable: Some studies indicate that selfish mining may let attackers gain extra revenue and break the balance between revenue and mining power [3], while others argue that selfish miners can never earn more revenue but only put themselves at risk for no gain [6]. Under which conditions selfish mining is a profitable strategy, and how the selfish mining influences the system's dynamic are questions that need more answers. In this paper, we try to answer these questions by extending [PERSON]'s stochastic method to agent-based modelling of the selfish mining strategy, and after simulation measuring various global and local properties of the system.
## 4 Conclusions  
This study employed three-dimensional numerical simulation methods to systematically investigate the impact of various antenna arrangement parameters, such as the arrangement number, arrangement form, and arrangement layers, on the wind load characteristics of telecommunication towers. It was found that the uniformity of antenna arrangements significantly reduces the sensitivity of the telecommunication tower's wind load to wind direction changes. The lateral force coefficient and the crosswind base overturning moment coefficient are sensitive to changes in the wind direction. With an increase in the antenna arrangement number, the growth rate of the tower's overturning moment coefficient is about twice that of the overturning force coefficient. Additionally, an increase in antenna arrangement layers leads to a linear increase in the tower's overturning force coefficient, whereas the tower's overturning moment coefficient exhibits a nonlinear increase. The rate of increase in the wind load with the antenna arrangement layers is significantly greater than that with the antenna arrangement number. Thus, to reduce wind load, it is advisable in practical engineering applications to increase the antenna arrangement number per layer, thereby reducing the antenna arrangement layers. The study also summarized a calculation method for the structural wind load of telecommunication towers, taking into account the influence of antenna arrangement parameters, providing a reliable basis for the structural design and safety assessment of telecommunication towers in practical engineering.  
Conceptualization, Y.J.; formal analysis, Y.J.; investigation, Z.Z. and M.D.; data curation, J.H., Z.Z. and M.D.; writing--original draft preparation, Y.J. and J.H.; writing--review and editing, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52208495, and the Natural Science Foundation of Hebei Province, China, grant number E2021210110.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 The WBANs Communication Stack
### Routing Protocols
#### 4.3.1 Multipath Routing Protocols
EEMR makes an attempt to reduce overheads in establishing multi paths in adaptive manner, but provides relatively lower load balancing, whereas N-to-1 protocols try to find multiple nodes disjoint paths in one route discovery process. By doing so, they optimize the energy consumption and provides higher reliability as well as security. Details about non-structure and coding based multipath routing protocols can be found in [76]. Furthermore, the authors in [78] gave a more detailed survey of multipath efficient routing in wireless sensor networks.
## 2 Finite Element Modeling for Thermal Analysis
### Material Properties  
In the LPBF process, the powder becomes a liquid state by high energy of the laser and becomes a solid state as it cools. As done by [PERSON] et al. [24], a global array is created using the UEXTERNALDB user subroutine for ABAQUS, and the temperatures of all integration points are monitored at every increment to check the material phase. Then, phase and temperature-dependent material thermal properties are applied using the UMATHT user subroutine for ABAQUS. Thermal material properties in the solid phase from Mills [25] are used, and the powder properties are determined as follows:  
The conductivity of powder is expressed \(\alpha\) times the conductivity of the solid, as shown in the following equation. \(\alpha\) is the coefficient multiplied by the conductivity of the solid to obtain the conductivity of the powder. The effective thermal conductivity of the metal powder with a diameter of 10-50 \(\upmu\)m is generally 0.1-0.2 W/mK at room temperature [2], the conductivity of 316L stainless steel powder is about 0.2-0.25 W/mK at 293 K [26]. Therefore, the range of \(\alpha\) is set to 0.0072-0.018 so that the thermal conductivity of AISI 316L powder is 0.1-0.25 W/mK at 293 K.  
\[k_{powder}=\alpha k_{solid} \tag{5}\]  
As the powder bed is considered as a mixture of solid and gas [4], the density of powder can be written as  
\[\rho_{powder}=(1-\varphi)\rho_{solid}+\varphi\rho_{gas} \tag{6}\]  
where \(\varphi\) is the porosity of the AISI 316L powder bed, which is the ratio of the volume of gas to the total volume of the cell, \(\rho_{solid}\) is the density of the solid phase, and \(\rho_{gas}\) is the density of the gas phase. The value of the porosity (\(\varphi\)) ranges from 0 to 1. \"\(\varphi=0\)\" means the cell only contains gas, and \"\(\varphi=1\)\" means the cell only contains metal. The porosity (\(\varphi\)) was assumed to be 0.4 in [PERSON] et al. [4], so the porosity range is set to \(\pm 10\%\) based on 0.4 for sensitivity analysis and parameter optimization.  
The heat capacity of powder can be written as  
\[\rho_{powder}C_{powder}=(1-\varphi)\rho_{solid}C_{solid}+\varphi\rho_{gas} C_{gas} \tag{7}\]  
where \(C_{powder}\), \(C_{solid}\), and \(C_{gas}\) are the heat capacity of the powder bed, solid phase, and gas phase, respectively. In many studies, it was assumed that the heat capacity of the metal powder and the heat capacity of the solid phase were the same [2; 4; 6; 7; 9], and it was confirmed by experiments [27].  
The material thermal properties of the powder and solid are fitted as a function of temperature as shown in Figure 3 in order to apply in the UMATHT user subroutine.
## 5 Numerical Simulations
### Simulation Results
#### 5.2.2 \(\Theta_{P}\) and Robustness  
We then investigated the relationship between the unintentional random initial failure rate \(\Theta_{P}\) and the ADN robustness under the ER topological structure, where the proportion of distributed generators was 0.8 and the value of the tolerance parameters of the nodes was 1.5.  
Figure 7 clearly shows the relationship between the final expected proportions \(R_{PF}(\infty)/R_{IF}(\infty)\) of the remaining normal operational nodes in the PN/CN and the unintentional random initial failure rate \(\Theta_{P}\). Of the nodes in the PN, 55.17% survived for \(\Theta_{P}=0.002\) when the proportion of distributed generators was 0.8 under the ER topological structure. Of nodes in the PN, 27.27% survived for \(\Theta_{P}=0.004\) when the proportion of distributed generators was 0.8 under the ER topological structure. Accordingly, 55.17% of nodes in the CN survived for \(\Theta_{P}=0.002\) when the proportion of distributed generators was 0.8 under the ER topological structure. Of nodes in the CN, 27.27% survived for \(\Theta_{P}=0.004\) when the proportion of distributed generators was 0.8 under the ER topological structure.  
Figure 6: \(R_{PF}(\infty)\) of different distributed generators proportions in the PN with Barabasi–Albert (BA)topological structure.  
Figure 7: The final expected proportion of normal operational nodes in the PN/communication network (CN) after the CCF stops (\(R_{PF}(\infty)/R_{IF}(\infty)\)) of different \(\Theta_{P}\) in the PN and CN with the ER topological structure.  
As shown in Figure 7, it can be seen that the robustness of the ADN deteriorated when the unintentional random initial failure rate \(\Theta_{P}\) in the PN was greater. That is, the more the unintentional random failure rate was, the robustness of ADN against the CCF was worse. We also observed that the transition phase of \(R_{PF}(\infty)\) was very sharp, and this indicated that the PN and CN either collapsed or was whole totally. Therefore, this agreed with our second conclusion. That is, the robustness of the ADN was negatively correlated with the unintentional random failure rate \(\Theta_{P}\). Once the value of \(\Theta_{P}\) was greater than the critical value \(\Theta_{P,critical}\), the whole ADN collapsed totally.  
The relationship between the final expected proportions \(R_{IF}(\infty)\) of remaining normal operational nodes in the CN and the unintentional random initial failure rate \(\Theta_{P}\) is similar.
# Analysis of Cost Functions for Reinforcement Learning of Reaching Tasks in Humanoid Robots
###### Abstract
Note that more classical gradient-based reinforcement learning approaches such as the Natural Actor Critic algorithm [22], Vanilla Policy Gradient methods [23], Finite Difference Gradients [24], etc., are not suitable for humanoid robots. This is because these algorithms depend on the estimation of the gradient, which is known to be a complex and computationally expensive operation that greatly influences the speed of RL. On the other hand, trajectory optimization methods such as the Cross-Entropy Method (CEM) [25] or Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [26] have been proven to be applicable to continuous control tasks, sometimes outperforming RL algorithms in terms of efficiency. In this work, we analyze an approach that combines learning from demonstration with reinforcement learning to adapt and enhance reaching-to-point tasks. The demonstrated trajectories serve as initial, usually suboptimal policy for the subsequent reinforcement learning phase. We employed the PI\({}^{2}\)-CMA policy search algorithm to optimize the demonstrated reaching-to-point trajectory while ensuring the stability of the robot. The manuscript is organized as follows. In Section 2, we first describe the details of the motion imitation framework, starting with the kinematic mapping in Section 2.1. This is followed by the explanation of the stability controller for generating stable humanoid robot motions in Section 2.2. Section 3 continues with the description of the adaptation process, which is based on a RL algorithm that exploits a specially designed objective function. In Section 4, we analyze how the parameters of the cost function affect the learned humanoid robot motions, while in Section 5 we observe the task performance of the learned motions on the real robot. Finally, we conclude the work with Section 6, where we present the ideas for future work.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### The Application Layer and Application Security Layer  
The Application Layer is situated above the Persistence Layer, which encompasses the basic business logic of the application. It comprises aspects like processing user inputs, enforcing business logic, and maintaining the integrity of the data.  
The Application Layer implements the data flow from the user interface to the database and vice versa. Within this layer, frameworks such as Django (for Python) [53], Laravel (for PHP) [54], Spring (for Java) [55], and ASP.NET (for C#) [56] are commonly and widely used.  
The Application Security Layer is in charge of protecting the application from outside threats and also maintaining a secure codebase. It implements secure coding practices, dynamic application security testing, and web application firewalls [57].  
Figure 3: Persistence layer and persistence security layer.  
Figure 2: Data layer and data security layer.  
Integrating security at the Application Layer prevents the exploitation of vulnerabilities and ensures that the application is not compromised easily through any kind of attack; this improves the overall security posture of the application.  
Figure 4 presents the Application Layer and the Application Security Layer.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 2 The Principle of the Fuzzy-FTA Analysis
### Application of the Fuzzy-FTA Method  
#### 2.3.1 Definition  
In Fuzzy-FTA, the fuzzy mathematics theory is introduced by:  
1. Fuzzifying the logical relationship between different levels. This indicates that when the fault tree of the system is established, the logic of each level causing the top event is not clear, but fuzzy [17];
2. Fuzzifying the occurrence probability of basic events. This indicates the replacement of the exact value of basic events with fuzzy numbers. The fuzzy number forms of basic events mainly include normal, triangular, and ladder types [18].
## 3 Attacks in Blockchain
### Detecting and Mitigating Malicious Blockchain Attacks
#### 3.3.7 Double-Spending Attack  
Double-spending attacks pose a significant threat to blockchain systems by allowing malicious actors to spend the same digital asset multiple times, undermining transaction integrity. Several detection and prevention mechanisms have been developed to mitigate this risk effectively.  
Key detection techniques include transaction and block verification processes, where the transaction time and block confirmation are monitored to identify inconsistencies or unusual behavior [36]. Updating the existing block rather than removing it and implementing timestamping during chain-building processes further enhance detection accuracy [86]. Approaches such as the Longest Chain Rule in Proof-of-Stake (PoS) protocols, key-evolving cryptography, and increasing the number of confirmed blocks have also been shown to effectively identify double-spending attempts [100]. Continuous monitoring of node connections, disabling unconfirmed connections, and employing transaction forwarding mechanisms ensure additional layers of security [37; 59].  
Mitigation measures for double-spending attacks include adopting recipient-oriented transactions and increasing block confirmation thresholds, which make fraudulent transactions more challenging to execute. Cryptographic methods such as key-evolving cryptography provide secure mechanisms to verify transaction authenticity. Additionally, ensuring a consistent ledger and disabling unconfirmed connections prevent the propagation of invalid transactions [39]. These strategies collectively enhance blockchain reliability and maintain transactional trustworthiness.
# An Improved Blind Recognition Algorithm of Frame Parameters Based on Self-Correlation
## 2 The Cumulative Filtering Algorithm  
The signals are usually transmitted in the form of frames in the channel, through the location of which, it can achieve the synchronization between the receiver and the transmitter. The typical frame format can be seen in Figure 1. S represents the frame synchronization word, the length of which is M. D represents the data payload, the length of which is N. The length of a whole frame L = M + N. The transmitter packages the frame synchronization words and data payloads into the channel. At the receiver, the frame synchronization word S is repeated in cycles, while the data payload is random.  
The start of each frame can be found by detecting the location of the frame synchronization word S, and then subsequent operations can be performed.  
In the background of non-cooperating communication, three key parameters: S, M, N are unknown parameters, which have to be recognized blindly [6].  
Blind recognition of frame parameters can be achieved by using the periodicity of the frame synchronization sequence. \(Y\) is the received bit stream while \(X\) is a receiving matrix in the size of \(u\times l\), which are as shown in the Equation (1).  
\[Y\ =\ [\ y_{1}\ \ \ y_{2}\ \ \ y_{3}\ \ \cdots\ ]\Rightarrow\left[\begin{array}{ ccccc}y_{1}&y_{2}&\ldots&y_{l}\\ y_{l+1}&y_{l+2}&\ldots&y_{2l}\\ \vdots&\vdots&\ddots&\vdots\\ y_{(u-1)l+1}&y_{(u-1)l+2}&\ldots&y_{ul}\end{array}\right]=\left[\begin{array}[ ]{cccc}x_{1,1}&x_{1,2}&\ldots&x_{1,l}\\ x_{2,1}&x_{2,2}&\ldots&x_{2,l}\\ \vdots&\vdots&\ddots&\vdots\\ x_{u,1}&x_{u,2}&\ldots&x_{u,l}\end{array}\right]=X \tag{1}\]  
When the number of matrix columns 1 is equal to the frame length, all elements of certain columns are either 0 or 1, while elements of other columns are randomly occurring as 0 or 1. The former ones are defined as synchronization columns, while others are defined as non-synchronization ones. The sum of each column is calculated, based on the results of which it can be recognized as a synchronization column when the result of a column is closed to 0 or u. Therefore, reasonable thresholds have to be elaborately designed [7,8].  
\(\delta_{up}\) and \(\delta_{down}\) are the upper and lower threshold respectively. Equations (2) and (3) can be acquired according to reference [9] and 36-criterion [10].  
\[\left\{\begin{array}{c}\delta_{up}{=}\left(\frac{u}{2}{+6\times}\sqrt{\frac {u}{4}}\right){=}\frac{1}{2}\left(u+6\sqrt{u}\right)\\ \delta_{down}{=}\left(\frac{u}{2}{-6\times}\sqrt{\frac{u}{4}}\right){=}\frac{1 }{2}\left(u-6\sqrt{u}\right)\end{array}\right. \tag{2}\]  
\[u\ >\frac{36}{\theta^{2}} \tag{3}\]  
\(\theta\) is a constant between 0 and 1. When \(\theta\) equals 0.5, then:  
\[u\ >\ 144 \tag{4}\]