esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 5 Results
### Evaluation
#### 5.2.3 Security  
The outcomes of the security evaluation of the proposed scheme are summarized below.  
First, the follower registration mechanism works as an authorization process since drones that have not been registered by the leader in the blockchain do not have the right to call the functions of the smart contract and therefore access sensitive information (data that are collected by sensors, such as the location of drones). Therefore, the risk of a network intrusion is reduced.  
Moreover, supposing that the private keys are safe, every transaction that occurs on the blockchain is unquestionably reliable. This is because they are verified for legitimacy by the other members using the matching public key after being signed with the drone's private key. Due to this functionality, data theft attempts are ineffective.  
Also, the chance that the data stored in the blockchain could be altered is negligible due to the unchangeable and open nature of blockchain technology. The smart contractitself has been recorded in a block of the chain, so there is no chance of alteration for the same reason. As a result, participants' sense of trust is boosted.  
Furthermore, another key feature of the proposed system is its robustness against isolated faults. Due to the nature of blockchain technology, even if one or more drone nodes malfunction, the operation of the system as a whole will continue to function normally since there will always be some members that are working normally.  
In addition, modifiers have been utilized in the smart contract to restrict followers' access to certain functions that belong exclusively to the leader. For instance, if a drone belonging to the followers is compromised, the threat actor will not be able to access the leader's sole function of registering new drones to the swarm (removing drones, creating missions, activating or deactivating missions, etc.).  
Since the leader in this particular scenario behaves as a central authority, the leader-follower model violates the blockchain's decentralized nature, which is a security drawback. However, this concept can be readily adapted by giving the swarm more leaders. As a result, the concept of decentralization is revived, and the system's defenses against leader-related attacks are strengthened (since the proposed model is weak in this area).  
Lastly, another drawback that has to be addressed is related to private key management. The possession of a private key could lead to the creation and signing of fault transactions, thus questioning the effectiveness and performance of the swarm. The development and integration with the proposed scheme of a mechanism for handling private keys could lead to a more reliable and secure model.
## 2 The Proposed Schemes: DAE-EDMR and T-DAE-EDMR  
### The System Model and Problem Formulation  
Consider a collection of \(N\) nodes in a \(k\)-dimensional Euclidean space, \(\mathbf{X}=[\mathbf{x}_{1},\cdots,\mathbf{x}_{N}]\in\mathbb{R}^{k\times N}\), where the positions of all \(N\) nodes, \(\mathbf{x}_{1},\cdots,\mathbf{x}_{N}\in\mathbb{R}^{k}\), are randomly distributed. With the knowledge of the positions of \(P(>k)\) reference nodes, we estimate the positions of the remaining \(N\)-\(P\) nodes. To accomplish this, we will utilize the concept of an EDM denoted by \(\mathbf{D}\in\mathbb{R}_{+}^{N\times N}\). It is a symmetric matrix whose \((i,j)\)-element can be represented as follows:  
\[\mathbf{D}(i,j)=d_{ij}^{2}=||\mathbf{x}_{i}-\mathbf{x}_{j}||_{2}^{2}=<\mathbf{ x}_{i}-\mathbf{x}_{j},\mathbf{x}_{i}-\mathbf{x}_{j}>, \tag{1}\]  
where \(d_{ij}\) is the true distance between nodes \(i\) and \(j\).  
To model practical measurements, we first consider three types of random variables due to the environment as follows:  
* \(B_{N}\): ranging errors dependent on the signal quality;
* \(B_{U}\): ranging errors due to clock asynchronization;
* \(B_{NLoS}\): non line-of-sight (NLoS) events.  
We assume that \(B_{N}\), \(B_{U}\), and \(B_{NLoS}\) follow normal, uniform, and Bernoulli distributions, respectively. Hence, we can define the random variable for the bias, \(B_{bias}\), as follows:  
\[B_{bias}=B_{N}+B_{U}+R_{NLoS}B_{NLoS}, \tag{2}\]  
where \(R_{NLoS}\) is the distance bias in the event of NLoS conditions. Note that \(B_{bias}\) does not follow any known probability distribution, as it is a convolution of three different distributions.  
Second, we assume that the distance is measured using a grid consisting of measurement resolutions, which is determined by the ranging configuration, e.g., the time of arrival (ToA). Thus, we define the quantization function \(\mathcal{Q}_{G}\) to represent the measured distance with a resolution of \(G\), e.g., \(\mathcal{Q}_{10}(23)=20\).  
Third, we formulate a function to indicate whether the distance is measured or not, based on the communication capability between nodes \(i\) and \(j\), as follows:  
\[\delta_{ij}=\left\{\begin{array}{ll}1&\text{if $d_{ij}$ is measured,}\\ 0&\text{otherwise.}\end{array}\right. \tag{3}\]  
Thus, the noisy measurement of the distance between the \(i\)-th and the \(j\)-th nodes is defined as follows:  
\[\tilde{d}_{ij}=\delta_{ij}\cdot Q_{G}(d_{ij}+e_{ij}), \tag{4}\]  
where \(e_{ij}\) is the realization of \(B_{bias}\). Then, we can define the noisy EDM using the following expression:  
\[\tilde{\mathbf{D}}=[\tilde{d}_{ij}^{2}]_{i,j=1}^{N}\in\mathbb{R}_{+}^{N\times N}. \tag{5}\]  
Finally, the objective of this paper is to find the denoising function \(\mathcal{H}\), which is formulated as  
\[\min_{\mathcal{H}}||\mathbf{D}-\mathcal{H}(\tilde{\mathbf{D}})||_{F}^{2}. \tag{6}\]
# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 2 Related Work
### Front-End Adaptation  
With the increasing adoption of the maximum margin criterion, more research relies on a straightforward cosine distance for scoring, and domain mismatch problems are primarily addressed by modifying the front-end embedding network. One approach is rooted in adversarial learning [36; 37; 38], where an additional adversarial domain discriminator is introduced to compel the network to learn domain-invariant features. Another approach is based on distribution alignment [28; 39; 40], leveraging first-order or second-order statistics to estimate the data distributions of the source and target domains and minimize their difference. For instance, deep CORAL [29] optimizes the network to bring the covariance matrices of the source and target domains closer, while maximum mean discrepancy (MMD) [39] minimizes the difference between the means of the two domains in the Hilbert space.  
In recent years, self-supervised learning (SSL) has made remarkable advances in speaker recognition, leading researchers to develop self-supervised strategies to tackle domain mismatch challenges. [PERSON] et al. [41] jointly trained on supervised loss from the source domain and contrastive self-supervised loss from the target domain. Building on this, [PERSON] et al. [42] proposed a clustering-based unsupervised domain adaptation (UDA) framework and conducted supervised training on both the source and target data. The core idea of these methods is to optimize the target domain data while maintaining strong recognition capabilities on the source domain. In our previous work [43], we introduced a multi-domain adaptation method that combines the concept of SSL adaptation [41] and distribution alignment [39], where the aligned distributions are from several parallel target domains. This represents the first attempt at simultaneous adaptation to multiple target domains.
# Analysis of Gut Microbiome Structure Based on GMPR+Spectrum
## 2 Materials and Methods
### Methods
#### 2.2.8 Evaluation Index of Clustering Algorithm  
In this paper, NMI, DBI, CH, and running time are employed to evaluate the performance of the clustering algorithm. These metrics are defined and formulated as follows:  
1. Normalized Mutual information (NMI)  
NMI ([PERSON] and [PERSON], 2018), which determines clustering quality, is a common method. The more significant NMI value means better performance. The joint distribution of random variables \(X\) and \(Y\) is \(p(x,y)\), and the edge distribution is \(p(x)\) and \(p(y)\), respectively. The mutual information \(I(X,Y)\) is the relative entropy of the joint distribution \(p(x,y)\) and the product distribution \(p(x)p(y)\):  
\[I(X,Y)=\sum_{x,y}p(x,y)\log\frac{p(x,y)}{p(x)p(y)} \tag{8}\]  
\[H(X)=\sum_{i=1}^{n}p(x_{i})I(x_{i})=\sum_{i=1}^{n}p(x_{i})\log_{b}\frac{1}{p( x_{i})}=-\sum_{i=1}^{n}p(x_{i})\log_{b}p(x_{i}) \tag{9}\]  
\[NMI(X,Y)=2R=2\frac{I(X,Y)}{H(X)+H(Y)} \tag{10}\]
2. Davies-Boulding Index (DBI)DBI, also known as the classification appropriateness index [48], is the maximum value of the sum of the average distance \(avg(C)\) between the samples of each of two clusters \(C_{i},\ C_{j}\) divided by the distance between the centroids of the two clusters. The larger the inter-class distance, the better the clustering effect.  
\[avg(C)=\frac{2}{|C|(|C|-1)}\sum_{1<i<j<|C|}dist\big{(}x_{i},x_{j}\big{)} \tag{11}\]  
\[DBI=\frac{1}{k}\sum_{i=1}^{k}i^{\text{ max}}j\left(\frac{avg(C_{i})+avg\big{(}C_{j} \big{)}}{dist\big{(}u_{i},u_{j}\big{)}}\right) \tag{12}\]  
where \(avg(C)\) means the average distance of cluster class C, \(|C|\) means the number of cluster classes \(C\), and \(dist(x_{i},x_{j})\) is the distance between two samples \(x_{i},x_{j}\), and \(u_{i},u_{j}\) are the center of the cluster class \(C_{i},\ C_{j}\), respectively.  
1. Calinski-Harabasz index (CH)  
The CH index is the ratio of inter-cluster distance to intra-cluster distance [49]. The larger the value \(CH(K)\), the better the clustering effect. The formula is as follows:  
\[CH(K)\frac{tr(B)/(K-1)}{tr(W)/(N-K)} \tag{13}\]  
where \(\text{tr}(B)=\sum_{j=i}^{k}||z_{i}-z||^{2}\) represents the trace of the inter-cluster distance difference matrix, \(\text{tr}(W)=\sum_{j=i}^{k}\sum_{x_{i}\in k}||x_{i}-z_{i}||^{2}\) represents the trace of the intra-cluster departure matrix, where \(z\) is the mean of the whole data set, \(z_{j}\) is the mean of the \(j\)th cluster \(c_{j}\), \(N\) represents the number of clusters, and \(K\) is the current class.
# Electronic Voting System Using an Enterprise Blockchain
## 4 System Proposal
### The Suffrage Chaincode
#### 4.2.1 Ballot  
It is a non-fungible token (NFT) and they can only be minted by an authorized organization (see Figure 8). Basically, this implies that the NFT is created on the blockchain.  
Identity Management and Access Control  
For each user account, an anonymous client identity is issued by the SUFFRAGE CA. When the ballot is issued, the smart contract obtains the client identity that submitted the request, and stores the identity and token ID (random uuid) as the owner in the asset key/value in the public chaincode world state (see Figure 9). Each ballot has access control based on MSP ID (organization name) and affiliation. Only identities from MINTER affiliation of SUFFRAGE organization can mint ballots (see Figure 10).  
Figure 8: Ballot written in GO.  
Figure 7: Access control in MINTER affiliation.  
Figure 9: NFT Data Model.  
The NFT that represents a ballot has a result field \(r\) to store the voter's response. According to the solution proposal [19], this field is defined in order to not constrain the solution, so in this way the system can be adapted to several use cases of an electoral process.  
We call the answers to the question \(q_{i}\) as \(A_{i}\), and \(|A_{i}|\) the number of all possible answers for the question \(q_{i}\). Then the first \(\lceil\log A_{1}\rceil\) bits of the field will be reserved for the response \(q_{1}\), the next \(\lceil\log A_{2}\rceil\) bits will be reserved for the answer to question \(q_{2}\), and so on. For example, suppose that the ballot contains three questions \(q_{1},q_{2},q_{3}\). For \(q_{1}\) we have 10 possible answers, for \(q_{2}\)--eight possible answers, and \(q_{3}\)--three possible answers. Therefore, the first \(\lceil\log 10\rceil=4\) bits will be reserved to encode the response \(A_{1}\), the next \(\lceil\log A_{1}\rceil=3\) bits will be reserved for \(A_{2}\), and the next \(\lceil\log 3\rceil=2\) bits for \(A_{3}\). This gives us \(4+3+2=9\) bits reserved for encoding responses. Therefore, this voting representation mechanism is a matter of bits interpretation, which means that it can be adapted to different data schemas. Then \(r\) is encrypted to avoid calculating the results during the election process, and is done using the public keys of the organizations.
# Fault Diagnosis of Vehicle Gerboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
## 2 Principle of the Method  
To effectively reduce the gearbox signal noise and improve the accuracy of fault recognition, this section proposes a gearbox fault recognition method integrating improved wavelet noise reduction, LT-PCA, and NGO-SVM. The method comprises three main components: signal noise reduction processing, signal fault feature extraction processing, and fault recognition. The following presents the detailed procedure of the integrated fault noise reduction and identification method.
## 4 Investigation of Proposed OAM-OSC System at Different Input Parameters
### Contributions of Proposed OSC-OAM System  
We investigated a PDM-256-QAM-based OSC system incorporating three different OAM profiles for the first time. A detailed literature review made us understand the shortcomings of OSC systems, such as low capacity, shorter OSC link length, high cost, mode crosstalk, and high BER. The performance of the OSC systems depends on different parameters such as pointing errors, LoS, transmitter power, antenna aperture size, antenna gain, beam divergence, etc. However, the two major performance-affecting entities are modulation and multiplexing. For a high bandwidth efficient carrier spectrum, PDM-256-QAM was considered in the proposed work, and for reducing mode crosstalk, OAM is selected. As explained earlier, the most significant limitation encountered in most of the MDM-OSC systems is the multiple modes placed with shorter gaps [1; 43; 56; 44], and this  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **[PERSON] et al. (2019) [41]** & **[PERSON] et al. (2021) [43]** & **[PERSON] et al. (2022) [44]** & **Proposed OAM-OSC System** \\ \hline Data rate & \(10,20,40\) Gbps & \(50\) Gbps & \(400\) Gbps & \(320\) Gbps \\ Channels & \(64\) & \(2\) & \(10\) & \(15\) \\ Capacity (max.) & \(2.5\) Tbps & \(100\) Gbps & \(4\) Tbps & \(4.8\) Tbps \\ OSC Link Length & \(3750\) km & \(20,500\) km & \(16,000\) km & \(22,000\) km \\ Modulation & DQPSK & \(4\)-QAM/PSK-OFDM & PDM-QPSK & PDM-256-QAM \\ Multiplexing & MDM & MDM & MDM & OAM \\ BER & \(10^{-9}\) & \(10^{-3}\) & \(10^{-3}\) & \(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed comparison of proposed OAM-OSC and reported OS systems.  
Figure 14: Representation of SNR versus OSC link lengths in the case of three OAM beams.  
issue led us to use OAM beams with a larger gap between LG modes. The cost of the system is a key concern where multilevel modulations such as QPSK, QAM, and OFDM are employed. Therefore, we employed a single laser for different three channels through three different OAM beams and reduced the cost of the system by one third. Further investigation of the proposed OSC-OAM system revealed some significant findings, such as the fact that higher LG modes require wider receiver TADs for better performance and that basic mode profiles, such as LG00, provide the best performance.
# Direct Measurement of Aerosol Liquid Water Content: A Case Study in Summer in Nanjing, China
## 4 Conclusions  
In this work, we first improved on our previous simple and low-cost method for the direct measurement of ALWC by replacing the highly expensive HR-AMS with a routine PM\({}_{2.5}\) mass monitor (5030i) and with two turbidimeters (PDRs) in parallel. The improved suite of instruments was successfully applied to the ALWC measurement of PM\({}_{2.5}\) in Nanjing for the summer of 2023. The average PM\({}_{2.5}\) concentration was determined to be 19.50 \(\mu\)g m\({}^{-3}\) and the ALWC was 3.85 \(\mu\)g m\({}^{-3}\); therefore, the ALWC was ~16.5% of the total PM\({}_{2.5}\) mass. The mass fraction of the ALWC also increased with the increase in the PM\({}_{2.5}\) concentration, partially due to the increased mass contribution of hygroscopic secondary species. The ALWC positively correlated with the RH and PM\({}_{2.5}\) concentrations,  
Figure 9: (**a**) Clustering results of the 36 h backward trajectories of air parcels arrived at the sampling site during the measurement period; (**b**) Average values of PM\({}_{2.5}\), AQI, ALWC, visibility, and RH of the three clusters.  
but negatively with the wind speed and temperature as well as the O\({}_{3}\) concentration; correspondingly, it did not always increase with the air quality index (AQI) either, due to the fact that large AQI values in summer are often governed by O\({}_{3}\) not PM\({}_{2.5}\) levels and differ from those in winter.  
Furthermore, we found that the ALWC also had a great impact on the visibility, which decreased with the ALWC rapidly at all PM\({}_{2.5}\) levels. The decrease in the visibility at higher PM\({}_{2.5}\) levels, however, appeared to be even quicker, dropping to \(<\)10 km when the PM\({}_{2.5}\) was \(\sim\)30 \(\mu\)g m\({}^{-3}\) or the AQI was between 75 and 100. This result suggests that with the consideration of the ALWC, some days with \"good\" air quality (classified by the CAAQS PM\({}_{2.5}\) standard or the AQI categories) might actually be \"lightly polluted\". We also found that the air mass originating from Northeast China (the Shandong peninsula) had the lowest PM\({}_{2.5}\) mass concentration yet the highest ALWC value mainly due to having the highest RH. In general, our findings presented here advance our understanding of aerosol chemistry and toxicology and may be valuable in future studies on air quality control in densely populated cities.  
Conceptualization, X.G.; Data curation, D.L.; Formal analysis, D.L. and S.C.; Funding acquisition, X.G.; Investigation, D.L., S.C. and J.W.; Methodology, J.W.; Supervision, Y.W., J.W. and X.G.; Writing--original draft, D.L.; Writing--review and editing, Y.W., J.W. and X.G. 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 numbers 42021004 and 42377100.  
The data presented in this article are available on request from the corresponding author.  
The authors would like to thank [PERSON] for his help during the experiment.  
The authors declare no conflicts of interest.
# The Effect of Pitch Surface on Match Running Performance in Women's Soccer
## 2 Materials and Methods
### Statistical Analysis  
The normality of the distributions was evaluated using the Kolmogorov-Smirnov test, and the data are presented as means \(\pm\) standard deviation (SD). The homoscedasticity of all variables was verified through [PERSON]'s test. To examine the effect of pitch type on MRP, four separate linear mixed models (LMM) were constructed. This approach was chosen for its ability to account for the nested structure of the data, where repeated observations of players across matches are non-independent. Thus, the LMMs included \"player identity\" as a random effect to control for inter-individual variability. Fixed effect included pitch surface (AT vs. NG). For this purpose, a dummy variable named \"pitch\" was created, coded as \"0\" for matches played on artificial turf and \"1\" for matches on natural grass, and introduced into the model. Due to the limited sample, contextual factors were not considered. The model fit was assessed through the use of residuals vs. fitted plots and Q-Q plots. The \(t\)-statistics from the mixed models were converted to effect sizes (ES) (Cohen's d), and interpreted as follows: \(<\)0.2, trivial; 0.2-0.5, small; 0.5-0.8, medium; \(>\)0.8, large (8). To confirm results, differences in MRP on AT and NG were additionally tested by \(t\)-test for independent samples. All analyses were conducted using SPSS version 25.0 for Windows (IBM, Armonk, NY, USA), with a significance level set at 0.05.
# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 4 Experimental Results and Analysis
### Structure Displacement Extraction
#### 4.3.2 Dynamic Load Structural Displacement Extraction  
In real-world scenarios, the load conditions that a structure is subjected to are often more complex than the static loads described above. Meanwhile, video capture is used to record the structural loading state, with a sampling rate higher than that of photography, significantly enhancing the effectiveness of structural state extraction and identification. In the dynamic load experiment, we simulate the moving load experienced by the structure during normal use by using a model car loaded with weight and driven by a motor. The geometric shape of the structure is recorded by video. We apply frame-by-frame processing to the extracted video, using the DoG algorithm to process the deformation edges of the structure in each frame. Using the first frame as the initial state, we track the displacement of the structure's deformation over time, which allows us to obtain the deformation image of the structure under load at any given moment [26]. By analyzing these graphical results through mechanical methods, we can derive the mechanical information of the structure at any time. In this experiment, we will use the above theory to extract the mechanical performance of the structure under dynamic loading to achieve displacement recognition and detection of the structure.  
Based on the results of the above algorithm, the approximate time-history displacement curve at the specified point can be obtained as follows (Figure 24):  
Figure 23: Error comparison between Canny operator and DoG.  
For the results mentioned above, the NRSME factor is used to evaluate both the algorithm's results and the LVDT results. From the above four test conditions, the NRSME values are calculated as follows: 0.0501 for 10 kg, 0.0568 for 20 kg, 0.0374 for 30 kg, and 0.0433 for 40 kg. In image processing, an NRSME value below 0.05 is generally considered to indicate very good algorithm performance and reliability, with high result quality. NRSME values between 0.05 and 0.1 are viewed as indicative of good algorithm performance, with relatively small errors and high result quality.  
From the time-history displacement curve results of the structural part mentioned above, it can be observed that the DoG algorithm proposed in this paper can achieve high-precision identification and extraction of displacement under the working conditions of the structure.
## 4 Analysis Example and Methods  
### Frequency-Domain Characteristic Analysis and Experimental Verification of the FSI Dynamic Model  
#### 4.1.1 Analysis of Frequency-Domain Characteristics of FSI Dynamic Model  
The specific parameters for the BHP are shown in Table 1 (Appendix A). A mechanical impact excitation is applied to the initial end of the BHP, where the mean value of the impact force \(F_{\mathrm{r}}\) is 15,000 N and the knocking time \(T\) is 2 ms. The boundary matrix, excitation vector, and pipeline structure parameters are substituted into the FSI 14-equation for the BHP for the modal solution. The result is shown in Figure 5.  
It can be observed that the frequency corresponding to each peak in the curve is the natural frequency of the BHP. By comparing the axial velocity response curve for the BHP with/without friction terms, it can be seen that friction coupling does not affect the resonant frequency, but affects its resonant peak.