esa-sceva/satcom-chunk-collection huggingface dataset:
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 1 Introduction
As for PointMBF [24], while a bidirectional fusion mechanism is employed, the lack of effective global information aggregation constrains its potential performance. Our work is inspired by [24; 25; 26], building upon its [24] bidirectional multi-scale information fusion framework with the further introduction of a global information aggregation mechanism. By effectively combining local features with global contextual information, we strengthen the interaction between global and local information, enabling the model to more accurately capture both global features and local details in point clouds. In this paper, we introduce a Transformer-based [25] self-attention mechanism within the fusion module of two branches [24]. Leveraging its global receptive field, this mechanism enables individual features to capture global information during the feature fusion stage. After completing feature extraction and fusion, we further employ Transformer-based self-attention and cross-attention layers to extract dense matching information from the processed image features. Subsequently, the top-K matching points with the highest confidence are selected from this dense matching information. By iteratively applying self-attention and cross-attention layers, the proposed module is capable of learning precise image registration information in an unsupervised manner. This approach not only en hances the global feature perception but also significantly improves the matching accuracy, providing an effective solution for unsupervised image registration tasks. We evaluated our proposed method on widely used indoor datasets, including ScanNet [19] and 3 DMatch [20]. Experimental results demonstrate that our approach achieved state-of-the-art performance in RGB-D unsupervised learning-based tasks. In summary, the contributions of this paper are as follows:  
* We improved the multi-scale bidirectional fusion module [24], enabling individual features to possess a global receptive field during the fusion stage. * We introduced Transformer-based self-attention and cross-attention layers to reallocate the fused features, resulting in more accurate matching information. * The proposed method achieved state-of-the-art performance on both the ScanNet [19] and 3 DMatch [20] datasets. Additionally, we report the stability of evaluation metrics under varying frame apart.
## 3 Results
### The Results of the Single-Factor Test for Sandstones with Different Pore Structures
#### 3.1.3 Influence of Temperature on Permeability Evolution  
The variations in the permeability of the four subcategories of sandstone under different temperatures can be calculated according to the stable seepage flow measured by the permeability test of the four subcategories of sandstone under the sole influence of temperature. The test results are shown in Figure 12.  
The test results show that the permeabilities of the four subcategories of sandstone decreased with increasing temperature, but the variation trends were different. With increasing temperature, the permeability of subcategory \(\mathrm{I_{1}}\) sandstone decreased linearly and slowly; the permeability of subcategory \(\mathrm{I_{2}}\) and \(\mathrm{II_{1}}\) sandstones decreased as a cubic curve (decreasing slowly at the beginning, but the deceleration increased significantly when the temperature was higher than 45 \({}^{\circ}\)C) (see Figure 12a-c); the permeability of subcategory \(\mathrm{II_{2}}\) sandstone decreased almost as a power function (decreasing rapidly at the beginning, but the deceleration slowed significantly or with little change when the temperature was higher than 45 \({}^{\circ}\)C) (see Figure 12d).
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
###### Abstract  
Hyperledger Fabric (HLF) is a leading permissioned blockchain platform designed for enterprise applications. However, it faces significant security risks from Denial-of-Service (DoS) attacks targeting its core components. This study systematically investigated network-level DoS attack vectors against HLF, with a focus on threats to its ordering service, Membership Service Provider (MSP), peer nodes, consensus protocols, and architectural dependencies. In this research, we performed experiments on an HLF test bed to demonstrate how compromised components can be exploited to launch DoS attacks and degrade the performance and availability of the blockchain network. Key attack scenarios included manipulating block sizes to induce latency, discarding blocks to disrupt consensus, issuing malicious certificates via MSP, colluding peers to sabotage validation, flooding external clients to overwhelm resources, misconfiguring Raft consensus parameters, and disabling CouchDB to cripple data access. The experimental results reveal severe impacts on the availability, including increased latency, decreased throughput, and inaccessibility of the ledger. Our findings emphasize the need for proactive monitoring and robust defense mechanisms to detect and mitigate DoS threats. Finally, we discuss some future research directions, including lightweight machine learning tailored to HLF, enhanced monitoring by aggregating logs from multiple sources, and collaboration with industry stakeholders to deploy pilot studies of security-enhanced HLF in operational environments.  
permissioned blockchain security; HyperLedger Fabric; DoS attacks; consensus protocols
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 4 Modal Analysis of the Substation Frame  
### Covariance-Driven Stochastic Subspace Identification  
For vibration testing of structures under ambient excitation, the excitation applied to the structure can be considered as stationary white noise. The core of the SSI-Cov method is to project the experimentally obtained responses into the state space of control theory. According to stochastic subspace theory, the discrete state-space model of an n-degree-of-freedom system under white noise excitation can be expressed as:  
\[x_{k+1}=A_{d}x_{k}+w_{k} \tag{2}\]  
\[y_{k}=C_{d}x_{k}+v_{k} \tag{3}\]  
where \(x_{k}\) is the discrete-time state vector, \(y_{k}\) is the output vector, \(A_{d}\) and \(C_{d}\) represent the state matrix and discrete output matrix, respectively, and \(w_{k}\) and \(v_{k}\) are zero-mean Gaussian white noise.  
By further expressing the response as a Hankel matrix and calculating its Toeplitz matrix, the system matrix, observability matrix, and controllability matrix can be obtained. Eigenvalue decomposition of these matrices, combined with the stabilization diagram method, allows for the extraction of the dynamic characteristics of the structure. Figure 9 presents the flowchart for identifying the dynamic characteristics of a structure using the SSI-Cov method. This method is exclusively employed in this study to identify the modal parameters and achieve accurate analysis results. The mathematical framework of the SSI-Cov method can be referenced in the literature [17].  
It should be noted that the SSI-Cov method exhibits strong robustness against noise when identifying dynamic parameters, primarily due to the following reasons: (1) The  
Figure 8: Wind profile of the test.  
Figure 9: Flowchart of SSI-Cov modal identification.  
SSI-Cov method identifies modal parameters by constructing the covariance matrix of the response time series. In this process, the covariance matrix primarily reflects the statistical characteristics of the stable components of the signal, while noise typically manifests as uncorrelated or high-frequency components. As a result, the noise components are naturally attenuated within the covariance matrix. (2) In the SSI-Cov method, the modal information of the system is obtained through the subspace decomposition of the system's covariance matrix. This process essentially distinguishes the true dynamic behavior of the system from noise by means of projection. (3) The SSI-Cov method is typically used in conjunction with the stabilization diagram method, which is highly effective in eliminating noise modes from the data.  
In addition, the SSI-Cov method assumes stationary excitation, which introduces certain limitations in its application. In contrast, when analyzing time-evolving behavior with the goal of identifying health status related to the rapid evolution of dynamic parameters, time-frequency analysis techniques such as the Short-Time Impulse Response Function [56], Curvature Evolution Method [57], Band Variable Filter [58], and Stockwell Transform are more effective [59].
## 5 Numerical Simulation
As shown in Figures 5 and 6, it is clearly observed that the tracking errors and the velocity errors of the proposed scheme converge to zero quickly and still keep at the balance point. However, the tracking errors and the velocity errors of ([PERSON], et al., 2012) have fluctuations and steady-state errors. Figure 7 depicts the control inputs \(\tau_{u}\) and \(\tau_{r}\) of two controllers. We can see that the surge force \(\tau_{u}\) has reached saturation at the beginning. This is mainly because a large gain or differential produces large initial value that exceeds the limit of the propulsion system. However, \(\tau_{u}\) and \(\tau_{r}\) of ([PERSON], et al., 2012) also have the problem of input saturation in the control process, and the moment \(\tau_{r}\) has the chattering problem due to saturation function. Figure 8 shows that the designed adaptive law can accurately estimate the bounds of the unknown disturbances. Figure 9 depicts that the approximation errors of neural  
Figure 10: Virtual control law \(\alpha_{u}\). \begin{table}
\begin{tabular}{c c} \hline \hline
**MIAC** & **Value** \\ \hline _the proposed scheme_ &
\begin{tabular}{c} \(\text{MIAC}_{v_{t}}=0.318\) \\ \(\text{MIAC}_{v_{t}}=17.11\) \\ \(\text{MIAC}_{v_{t}}=2.259\) \\ \(\text{MIAC}_{v_{t}}=29.45\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance index of MIAC. Figure 9: Approximation error results of neural network. network converge to zero. In addition, the virtual control law \(\alpha_{u}\) is shown in Figure 10, and it is clearly observed that \(\alpha_{u}\) is not equal to zero. From Table 3, we can see that the performance index of the proposed scheme is \(\left[0.318,17.11\right]^{\mathsf{T}}\), and it is only \(\left[14.08\%,58.09\%\right]^{\mathsf{T}}\) of ([PERSON] et al., 2012). Based on the above analysis, in terms of tracking precision, convergence speed and control efforts, the proposed scheme has the best control performance.
# Would Forest Regrowth Compensate for Climate Change in the Amazon Basin?
## 1 Introduction
[33; 34] found that due to reduced cloud cover, droughs induce a \"greening-up\" although other researchers have rejected this hypothesis, e.g., [35; 36; 37]. According to Ref. [35], analysis and model simulations of the impacts of Amazon deforestation over the past 40 years showed that more than 90% of studies agree on the sign of change which is a reduction in rainfall. But the amplitude, magnitude, and predictability are inconsistent since they highly depend on the spatio-temporal scale of analysis [15; 36; 37; 38; 39; 40; 41; 42; 43]. Even if the regional impacts of deforestation on precipitation patterns have been studied intensively e.g., [8; 21; 28; 44; 45; 46; 47], the reverse effects are still unclear. Therefore, in this study, we aim to examine the extent to which potential Amazon Forest regrowth may influence fluxes, precipitation, and temperature patterns during both wet (December-April) and dry seasons (May-November). We should note that wet and dry seasons are not consistent across the domain, but these timespans are a practical compromise for analysis. Thus, in this research, we examined the sensitivity and magnitude of changes to the surface energy budget, including precipitation, due to potential new growth forests across the Amazon Basin (Figure 1). Our prescribed reforestation scenario using the Weather Research and Forecasting model (WRF)V3.9 is designed to answer the following questions: (a) How might forest regrowth contribute to changes in fluxes, temperature, and precipitation amounts across the basin at monthly and seasonal timescales; (b) what are the spatio-temporal patterns of changes; and (c) Do any tele-connected processes develop due to forest rehabilitation?
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### Transmission Tower matching Network
#### 2.3.6 Matching Similarity Evaluation  
Each cropped transmission tower image, when passed through the matching network, produces a d-dimensional feature vector. This study opted to employ the Euclidean  
Figure 9: Training procedure of the matching network.  
distance to evaluate the matching results, using similarity assessment criteria illustrated in Equation (16). A smaller Euclidean distance indicates higher similarity.  
\[sim(A,B)=\sqrt{\sum_{i=1}^{n}(A_{i}-B_{i})^{2}} \tag{16}\]
## 5 Electrical-Resistivity Modeling Using Pore Combination Model (PCM)  
Electrical resistivity (inverse of conductivity) has been widely utilized to evaluate the hydrocarbon content of petroleum reservoirs. At the heart of formation evaluation, there exists \"[PERSON]'s equation\" [1] to compute water saturation knowing reservoir porosity, brine resistivity, lithology or cementation exponent (m), and saturation exponent (n).  
While [PERSON]'s model is mainly valid for single pore and clay-free formations, other models have been proposed for complex rocks with multiple pore structure. Pore combination modeling (PCM) methodology [2; 19] is one model that expresses the contribution of each pore structure and its fluid content in permeability and resistivity. In fact, PCM is an extension of [PERSON]'s model, representing rocks with multiple pore structures. Owing to a universal sandstone core measurement, [PERSON] [19] obtained [PERSON]'s lithology exponents for intergranular porosity and microporosity pore types of sandstone formations worldwide. As summarized by [PERSON] et al. [17], the following equation gives the formation factor of a water-bearing rock with two pore structures:  
\[F=\left(\frac{1}{\phi_{s}}\right)^{\lambda_{s}}\left(\frac{\phi_{s}}{\phi_{t} }\right)^{\lambda_{sh}} \tag{6}\]  
where \(\lambda_{s}\) is the lithology exponent for intergranular pores and \(\lambda_{sh}\) is the lithology exponent for micropores. \(F\) is the ratio of the resistivity of 100% water-saturated rock to brine resistivity, called the formation factor. Figure 1 represents the PCM methodology for a dual-pore sandstone formation.  
As explained in [PERSON] et al. [20], the intergranular pore and micropore lithology exponents can be obtained from the nonlinear optimization of resistivity core measurements. In the following, we will present a new methodology to obtain these parameters from conventional well logs.  
With the same notation used by [PERSON] et al. [17], the formation factor (\(F\)) and total porosity, i.e., \(F\)-\(\phi_{t}\) are plotted in the log-log plane. Starting from point (\(F=1\) and \(\phi_{t}=1\)), a line with slope of \(\lambda_{s}\) illustrates the gradual decrease in total porosity due to sediment compaction. At this stage, intergranular pores are the only pore system and total porosity (\(\phi_{t}\)) is equal to intergranular porosity of \(\phi_{s}\). Then, diagenesis comes into play and micropores begin to evolve. A line with slope \(\lambda_{sh}\) represents the evolution of the micropporosity \(\phi_{sh}\). This line has a different slope than the line of intergranular pores. When the diagenesis process is finished, the sample has a total porosity of \(\phi_{t}\), which is the summation of \(\phi_{sh}\) and \(\phi_{t}\). The line connecting the final position of sample in the \(F\)-\(\phi_{t}\) plane with the point of (\(F=1\), \(\phi_{t}=1\)) is [PERSON]'s line with slope m, which represents the lithology exponent [17].
## 3 FGRL for Mapless Navigation  
In this section, we will first detail the RL-based framework for mapless navigation of UGVs and then proceed to discuss how to improve the learning efficiency via the proposed FGRL approach. In this work, UGVs only rely on their own sensor readings, i.e., LiDAR sensors, to obtain the situational awareness about the environment. Sensor readings are represented as the state information in RL tasks, and the control policy can provide action commands for UGVs to navigate without the need for mapping.
# Reaction Time and Postural Control Under Dual-Task Conditions in Brazilian Jiu-Jitsu Athletes
## 1 Introduction
In addition to the studies mentioned, several more recent works have explored postural control using advanced measurement techniques. Force platforms, considered the gold standard for measuring center of pressure (CoP) displacement, are widely used in these studies, particularly in elite athletes. Linear metrics such as CoP path length, range, or velocity have been used to quantify postural control. However, these linear measures often fail to capture the full complexity of dynamic postural sway and do not fully explain the positive effects of sport expertise on postural control. More recent research [30] has emphasized the importance of nonlinear measures for analyzing CoP signals, focusing on temporal variations in CoP displacement in both anteroposterior (AP) and mediolateral (ML) directions. These nonlinear metrics allow for a more nuanced understanding of the regularity, adaptability, stability, and complexity of postural control. One of the most widely used nonlinear measures is Sample Entropy (SampEn), which assesses the regularity of a signal and the degree of attention applied during balance tasks [31]. High SampEn values indicate irregular CoP time series, reflecting a healthy and adaptive biological system. Conversely, low SampEn values indicate more regular time series, suggesting rigidity in postural control, which may be associated with potential pathology. Fractal dimension (FD) is a metric used to assess the complexity of the CoP signal. An FD value of 1 represents a theoretically impossible scenario for a human, as it would indicate completely still standing without any movement--an outcome unattainable for living humans due to natural postural sway [32]. The last nonlinear measure is the Lyapunov Exponent (LyE), which evaluates the resistance of the human control system to external perturbations [33]. A high LyE value indicates the ability to react faster to stimuli, and a low value indicates the rigidity of the system and problems with adapting to the environment. Recent studies have started to incorporate nonlinear measures into postural control assessments in Brazilian Jiu-Jitsu athletes. For instance, one study employed SampEn to assess postural control in elite Jiu-Jitsu athletes and a control group of non-athletes [34]. Given the limited research on reaction time and the use of nonlinear measures to describe postural control, this study aimed to determine whether Brazilian Jiu-Jitsu athletes exhibit better reaction times in specific positions compared to a control group. In addition, this study will assess whether Brazilian Jiu-Jitsu athletes display more complex postural control than non-athletes during tasks requiring focus and attention.
# Differential Evolution in Waveform Design for Wireless Power Transfer
## 3 Evolutionary Algorithms
### Modifications for Application in Waveform Design  
Here we describe the necessary modifications made in the original versions of the algorithms to apply them in the waveform design for the WPT problem. These include the initialization of the population and the enforcement of boundary constraints. In the optimization problem of waveform design for WPT, the population vectors of any EA coincide with the \(\mathbf{a}\) vectors, while the dimensionality of the problem is equal to the waveform's total number of sub-carriers (\(D=N\)). The population vectors of the first generation are initialized randomly inside the feasible solution space. According to (7), the minimum value of \(a_{n}\) is 0. On the other hand, the maximum value is \(\sqrt{2P_{t}}\), considering a scenario where all the available power would be allocated in only one of the sub-carriers. Hence, each element of the population vectors initially takes a random value between 0 and \(\sqrt{2P_{t}}\). Then, the vectors that violate the transmit power constraint are normalized so that the average transmitted power for each one of them is \(P_{t}\) before entering the main loop of the algorithm. Considering that each \(\mathbf{x}_{i,g}\) represents a vector of amplitudes \(\mathbf{a}\), the normalization is accomplished by multiplying the invalid vectors with a term \(Z_{i}\) that is calculated individually for each one of them:  
\[\mathbf{x}_{i,1}=Z_{i}\cdot(\mathbf{x}_{i,1})_{invalid}\text{ if }\sum_{j=1}^{D}\frac{(x _{j,i,1}^{2})_{invalid}}{2}>P_{t}\]  
\[Z_{i}=\sqrt{\frac{P_{t}}{\sum_{j=1}^{D}\frac{(x_{j,i,1}^{2})_{invalid}}{2}}} \tag{13}\]  
The invalid vectors generated during mutation and crossover operations in DE are also substituted by vectors that lie inside the feasible solution space. This process is applied in two steps right after the crossover operation in a generation basis. In the first step, the negative valued elements of each trial vector are substituted by the respective elements of the corresponding base vector multiplied by a uniformly distributed random number in the range \((0,1)\):  
\[u_{j,i,g}=rand_{j}(0,1)\cdot y_{j,i,g}\text{ if }u_{j,i,g}<0 \tag{14}\]  
The random number \(rand_{j}(0,1)\) is generated individually for each element substitution. In the second step, the trial vectors that violate the transmit power constraint are normalized in the same way as described earlier:  
\[\mathbf{u}_{i,g}=C_{i}\cdot(\mathbf{u}_{i,g})_{invalid}\text{ if }\sum_{j=1}^{D} \frac{(u_{j,i,g}^{2})_{invalid}}{2}>P_{t}\]  
\[C_{i}=\sqrt{\frac{P_{t}}{\sum_{j=1}^{D}\frac{(u_{j,i,g}^{2})_{invalid}}{2}}} \tag{15}\]  
Algorithm 1 summarizes the pseudocode of DE in the WPT optimization problem. The rest of the algorithms are modified and applied in the same manner.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 4 Jamming Utilization Method Based on IMSFH
### Data Preprocessing  
It should be noted that since the signal being input into the network is in the form of a spectral waterfall, preprocessing of the signal is also required, as shown in processes (2), (3), and (4). Firstly, we obtain the spectral state \(s_{k}^{m}\) at the \(k\) frequency \(m\) of the time slot in the form of the Discrete Fourier Transform (DFT), as shown in formula (2), where \(N\) is the number of sampling points in the DFT, \(f_{s}\) is the sampling frequency, and \(m_{index}=m\cdot N/f_{s}\) is the index of \(s_{k}^{m}\) in the DFT result. We superimpose and combine the DFT results of multiple consecutive time points to obtain a spectral waterfall. As shown in Formulas (3) and (4), after data preprocessing, we can use \(S_{k}^{m}\) as the input of the network in the form of a thermodynamic diagram and train it.  
\[s_{k}^{m}=x[m_{index}]=\sum_{n=0}^{N-1}x[n]\cdot e^{-\beta}\frac{2\pi}{N}m_{ index}n, \tag{2}\]  
\[s_{k}=(s_{k}^{1},s_{k}^{2},s_{k}^{3},\ldots,s_{k}^{m}), \tag{3}\]  
\[\mathbf{S}_{k}=\begin{bmatrix}s_{k-1}\\ s_{k-2}\\ \vdots\\ s_{k-L}\end{bmatrix}=\begin{bmatrix}s_{1-1}^{1}&s_{1-2}^{2}&\ldots&s_{k-1}^{m} \\ s_{k-2}^{1}&s_{k-2}^{2}&\ldots&s_{k-2}^{m}\\ \vdots&\vdots&\vdots&\vdots\\ s_{k-L}^{1}&s_{k-L}^{2}&\ldots&s_{k-L}^{m}\end{bmatrix} \tag{4}\]