esa-sceva/satcom-chunk-collection huggingface dataset:
# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 5 Experiments
### Baselines  
To evaluate MVCG-SPS, we compared it against representative methods from various categories, including traditional machine learning models, homogeneous and heterogeneous graph neural networks (GNNs), and state-of-the-art perturbation-based multi-view methods commonly used in blockchain fraud detection tasks:  
1. Machine Learning Methods: Random Forest (RF) employs ensemble decision trees combined with manually engineered transaction features to classify accounts [35]. XGBoost is an optimized gradient boosting framework that is effective at handling structured tabular data and widely applied in fraud detection tasks [36].  
2. Homogeneous Graph Methods: GCN aggregates neighborhood structural information via convolutional operations, capturing relational patterns among homogeneous nodes [10]. Simple Graph Convolution (SGC) streamlines a GCN by removing non-linearities between layers, reducing computational complexity for larger graphs [37]. Graph Isomorphism Network (GIN) employs injective aggregation strategies to effectively distinguish unique graph structures, improving the representational capability [38]. GraphSAGE generates inductive node embeddings by sampling local neighborhoods and aggregating their features, making it scalable for large graphs [39]. Graph Attention Network (GAT) enhances node embedding by assigning adaptive attention weights to neighbors, emphasizing significant local interactions [11].  
3. Heterogeneous Graph Methods: RGCN extends graph convolutions to heterogeneous graphs, explicitly modeling different node and edge types [29]. Relational Graph Attention Network (RGAT) combines multi-head attention mechanisms with relation-specific transformations, capturing heterogeneous relationships [40]. Heterogeneous Graph Transformer (HGT) utilizes transformer-style attention to model complex interactions across multiple types of nodes and edges, enhancing representational accuracy [41].  
4. Perturbation-Based Methods: Contrastive Multi-View Representation Learning (MVGRL) creates augmented graph views through random perturbations and aligns embeddings via contrastive learning, improving robustness [42]. Multi-View Embedding with Re-weighted Instance and Transformer (MERIT) dynamically integrates multiple graph views through transformer-based re-weighting of instances, enhancing representation flexibility [27].  
These baselines cover a diverse set of methodologies, ensuring a comprehensive comparison of MVCG-SPS's performance in detecting Ponzi schemes within Ethereum-based smart contracts.
## 3 Results  
### Cutting Speed and Cut Edge Quality  
The present investigation analyzes static beam shaping and three different beam oscillations. The cutting speed and focal plane have been optimized regarding the lowest burr and surface roughness at the highest possible feed rate. The resulting parameters are summarized in Table 4, and Figure 9 depicts the corresponding cut edges.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Set \#1** & **Set \#2** & **Set \# 3** & **Set \#4** \\ \hline beam shaping & static & & & longitudinal, linear & \\ amplitude [\(\upmu\)m] & 0 & 300 & 550 & & 300 \\ frequency [Hz] & 0 & 500 & & 500 & 2000 \\ focal plane [mm] & \(-\)10.5 & \(-\)3.2 & 3.2 & \(-\)2.8 \\ cutting speed v\({}_{\text{c}}\) [m/min] & 0.5 & 0.85 & \(\upalpha\) +70\% & 0.7 & \(\upalpha\) +40\% & 0.7 & \(\upalpha\) +40\% \\ burr height \(\upalpha\)\({}_{\text{b}\text{bThe results of set #1 (Figure 9a) correspond to the state-of-the-art LBFC with 3 kW laser power. This is the basis for the comparison with beam oscillation. Set #2 (Figure 9b) achieved the highest cutting speed with a 70% increase. Moreover, the burr has the greatest reduction by 41%. Set #2 is the only one to obtain a slightly reduced surface roughness by 4%. The results of set #2 are remarkable, as they cannot be achieved with static beam shaping with this laser power, even by adapting the focal plane. The extended amplitude in set #3 (Figure 9c) still results in a 40% faster cutting speed and 29% less burr height, but the surface roughness increased by 48%. Raising the frequency in set #4 (Figure 9d) achieves a similar cutting speed as set #3. However, the burr height increased by 25% as well as the surface roughness rise by 59%.
## Challenges and Potential Solutions  
The challenges regarding 6G communications stem from five key components: provision of network security and data privacy enforcement, attainment of a cost-effective approach towards rapid network deployment and expansion with an emphasis on remote and stand-alone areas, reduction in the price of mobile communications utilization, approaches to extend mobile equipment battery life longevity, and attainment of a higher data rate buoyed with end-to-end, ultra-reliable low latency regime. Notably, satisfying all features is impossible, but a scheme must be devised to balance these features adeptly. This section highlights some of the anticipated challenges in 6G communications and the potential solutions.
## 4 Patterning towards Devices: Conventional and Unconventional Methods
### Direct-write techniques
#### 4.3.1 Laser scanning  
Laser scanning is a patterning technique in which UV, nanosecond pulsed, excimer and Nd:YAG lasers are used to generate patterns by scanning the surface of a resist. This technique typically applies the same processing steps described in section 4.1 for lithography [136].  
Nanocomposites on barium titanate (BaTiO\({}_{3}\)) powders 0.12 \(\upmu\)m in size and an epoxy resist have been irradiated by a frequency-tripled Nd:YAG laser (355 nm) in order to fabricate arrays of flexible/rollable embedded capacitors. The nanocomposite films present an uniform distribution of NPs and show high dielectric constant (>3 \(\times\) 107 V/m _@_ 1 MHz), high mechanical strength, high capacitance density with a capacitor thickness ranging from 4 \(\upmu\)m to 70 \(\upmu\)m [167]. Silver metal 1D, 2D and 3D structures have been fabricated by laser writing a polymer nanocomposite based on an organic soluble silver salt (AgBF\({}_{4}\)) precursor for Ag NPs, a photoreducing dye sensitizer, ligand-coated silver NP seeds and polyvinylcarbazole (PVK) polymer. The polymer acts both as host matrix and sacrificial reducing agent. Here, the generation of Ag NPs occurs by photoreduction under laser irradiation. 3D free standing, continuous Ag patterns have been written by scanning a 100 \(\upmu\)m thick nanocomposite film with a tightly focused two-photon femtosecond pulsed laser beam at 730 nm and scan rate of 25 \(\upmu\)m/s. 1D and 2D metal patterns 500 nm wide and large pads have been fabricated by using a single photon CW 514.5 nm laser excitation with an intensity of 4 \(\times\) 105 W/cm\({}^{2}\) and scan rate of 25 \(\upmu\)m/s [168].
## 3 Experiments and Results
### Simulation Experiment
As the iterations of the algorithm continuously increase, the SKF results become worse from 5.7 s. Conversely, VC-AKF can effectively overcome problems of filtering divergence and accuracy degradation in SKF. The derived displacements are improved by 46% compared with SKF, which verifies the effectiveness and superiority of the presented VC-AKF approach in this study. (2) SKF + RTS can decrease the errors carried by filtering initial values and increase the estimation precision of the SKF. After smoothing, the accuracy of displacements is improved by 22%, which is less than that of VC-AKF. Moreover, SKF + RTS is incapable of modifying the filter divergence, which indicates that it cannot completely solve the problem of system noise variance variation.
## 3 Results and Discussion  
As mentioned in the previous section, experiments were conducted with three types of oils under two conditions: with and without the additive. These experiments were performed at temperatures ranging from 25 \({}^{\circ}\)C to 85 \({}^{\circ}\)C using the Rhotest VR2 viscometer with appropriately selected parameters to simulate real-world operating conditions for these oils. The measured shear stress values for the different oils and oil-additive mixtures at various rotational speeds are listed in Tables 3-5, corresponding to industrial oils A, B, and C, respectively. Both cases with the additive (at 0.5% concentration) and without the additive were tested and recorded comprehensively. It can be observed that for each type of industrial oil, the shear stress varies with the corresponding shear rate. The changes in shear stress with temperature can be explained by variations in the viscosity characteristics of the oils. As the temperature increases, the molecular motion within the oil accelerates, leading to a decrease in viscosity and changes in load-carrying capacity. Consequently, the shear stress decreases at higher temperatures. However, these changes differ at each experimental temperature, especially with noticeable differences between the cases with and without the additive. When the additive is introduced, the shear stress increases correspondingly, particularly at any given temperature where the shear stress in the additive-containing case is higher than the case without the additive. The presence of the fly-ash additive can impact the structure and properties of the oil, altering its viscosity and load-carrying capacity. For Newtonian fluids, the relationship between shear stress and shear rate is linear. From the experimental results, empirical regression equations can be determined by linearizing these relationships. The results of the shear stress-shear rate relationship for oils A, B, and C at different temperatures are shown in Figures 2-4, respectively. In these figures, both cases without the additive and with the 0.5% additive are depicted in detail. It can be observed that these linear curves have different slopes, which characterize the viscosity of the corresponding fluids. As known, the viscosity of a fluid is the tangent of the slope of these linear curves. Thus, as the temperature increases, this slope decreases. This phenomenon holds true for both cases with and without the additive. Additionally, the empirical regression equations have R-Square values greater than 0.95, ensuring the reliability of the empirical regression equations.
## 2 Methods
### Eddy Covariance Towers  
The eddy covariance technique is used to measure the net ecosystem exchange of CO\({}_{2}\) between the biosphere and the atmosphere [33]. Micrometeorological EC towers measure fluxes of energy, water vapor, and carbon dioxide covering a longitudinal length of 100-2000 m depending on sampling height and atmospheric conditions [33]. CO\({}_{2}\) and water vapor concentrations are measured with an open path infrared gas analyzer, and wind speeds from a sonic anemometer at 20 Hz. Data are collected by a Campbell Scientific CR 3000 Datalogger.  
The data were processed following AmeriFlux standards (see [34] for details on the post-processing algorithm), including a coordinate rotation using the planar fit method [35] as well as the standard corrections for density [36] and sonic-anemometer derived estimates of temperature [37]. In addition, corrections were made for de-spiking, lag removal, and sonic temperature heat flux for humidity, sensor separation, and spectral attenuation. Daily averages of CO\({}_{2}\) concentration for 2007-2010 were calculated from 48 half-hour measurements in a 24-hour period for comparison with AIRS daily observations. Missing daily values for all three EC towers (18.57\(\%\) at KFS; 6.43\(\%\) at KZU; 5.16\(\%\) at K4B) were replaced with the mean for the entire time series at each tower. This type of data filling is selected specifically for the wavelet multi-resolution analysis that does not allow for missing values. Filling the missing data with the mean of the entire time series for each site results in a more predictable effect on longer timescales versus a less predictable effect on all timescales. Because KFS is missing more data on weekly to monthly scales, the influence of data filling may be more evident on these scales than for sites KZU and K4B.
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
## 1 Introduction
AE ringing counts, energy, and \(b\)-value (the ratio of amplitude to the number of hits) analyses have investigated crack propagation in ultra-high-performance concrete (UHPC) hybrid composite beams [26], and \(lb\)-value (improved \(b\)-value) analysis has examined crack development in ECC-normal concrete beams and failures of steel plate reinforcements [27]. However, AE applications in evaluating crack development and failure modes in ECC-LWSCC composite beams are limited. Specifically, research gaps exist regarding \(b\)-value analysis, \(lb\)-value analysis, and rise time/amplitude analysis for categorizing cracking mechanisms and damage stages in large-scale ECC-LWSCC composite beams. The combination of ECC and LWSCC materials is expected to affect wave propagation characteristics due to the potential signal attenuation [28], necessitating further exploration of AE parameters' response to different fiber types (PVA or SF) in ECC layers. This study aims to assess ECC-LWSCC composite beam cracking behavior using AE parameters such as RA, \(b\)-value, and \(lb\)-value analyses.
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 4 Discussion  
During the actual optical manufacturing process, there may be various errors that can affect the performance of the system. Therefore, it is necessary to use software simulations to analyze the impact of these errors on the system's performance [24].  
To ensure that the CCD sensor has a high resolution during the detection process, tolerance analyses of the imaging optical system are required to ensure that the MTF is not lower than 0.3. In this study, sensitivity analysis was used for the tolerance analyses of the imaging system, with a test wavelength of 525 nm and 500 Monte Carlo samples. The geometric mean MTF of the optical system at 71.4 lp/mm was taken as the standard. The first set of tolerance settings and analysis results are shown in Table 5, and the worst deviation types are shown in Figure 6.  
According to the first tolerance analysis results, it can be observed that the thickness deviation of the fifth lens element (H-ZLAF50D) has the greatest impact on the MTF of the imaging system, while the influence of other parameters is relatively small. Therefore, after readjusting the tolerance settings, a subsequent analysis was conducted. The analysis results are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Aperture & \(\pm\)4 & \multicolumn{2}{c}{Tolerance Analysis} \\ \hline Thickness & \(\pm\)0.04 & Geometric Mean MTF & Result \\ \hline Surface Roughness & \(\pm\)0.4 & \(\geq\)98\% & 0.54257240 \\ \hline Decenter X & \(\pm\)0.01 & \(\geq\)90\% & 0.58823616 \\ \hline Decenter Y & \(\pm\)0.01 & \(\geq\)80\% & 0.61704130 \\ \hline Tilt X & \(\pm\)1\({}^{\prime}\) & \(\geq\)50\% & 0.66834423 \\ \hline Tilt Y & \(\pm\)1\({}^{\prime}\) & \(\geq\)20\% & 0.69281911 \\ \hline Refractive index & \(\pm\)0.0003 & \(\geq\)10\% & 0.69708111 \\ \hline Abbe number & \(\pm\)0.5\% & \(\geq\)2\% & 0.70288977 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the first tolerance analysis.  
Figure 6: Worst deviation types for the first tolerance analysis.  
These analysis results indicate that over 98% of the samples have an MTF decrease of less than 0.1, achieving satisfactory tolerance analysis results that meet practical requirements.
# Efficient Online Log Parsing with Log Punctuations Signature
## 2 Problem Description  
As shown in Figure 1, the goal of log parsing is to extract the static template, dynamic variables, and header information from a raw log message. As the header information usually follows a fixed format in the same system, regular expressions are commonly used to extract the header information and the log content.  
Log content is the central processing object of log parsing, composed of static templates and dynamic variables. It can be defined as a tuple of \(EV=\{(e_{i},v_{i}):e\in E,\ i=1,2,\cdots\}\), where \(E\) is the set of all log templates, \(k=|E|\) is the number of all distinct templates and \(v_{i}\) is a list of variables.  
Formally, given a set of log messages \(L=\{l_{1},l_{2},\cdots,l_{m}\}\) that is produced by \(k\) logging statements (log templates) from the source code, where the \(k\) is unknown. A log parser is to parse \(L\) to get all \(k\) log templates.  
After extracting the log content from the raw log, it is common to process the content with string splitting. By splitting the content, we get a split list, and each element in the list is called a token. Log message length is defined as the number of tokens in the split list. Formally, each log message consists of a bounded list of tokens, \(\mathbf{t}_{i}=\{t_{j}:t\in T,j=1,2,\cdots,n\}\), where \(T\) is a set of all tokens, \(j\) is the token index within the split list and \(n=|\mathbf{t}_{i}|\) is the number of tokens (message length).  
The token is usually the smallest granularity to determine the template and variable part. A token is either part of the static templates or dynamic variables. Therefore, how to split the content has a significant influence on the parsing results. Different delimiters result in different split token lists. Previous studies often use spaces as delimiters.  
Online algorithms usually have two core steps: first, find the candidate template set; and then traverse the candidate set to find the most suitable template.  
Quickly finding candidate sets is critical in the first step. Inspired by the prefix tree data structure, Spell [26] and Drain [17] adopt a tree structure to find candidate sets to filter out most irrelevant templates. Such a tree structure is usually very complex. Some tree nodes have hundreds of children, and the tree needs frequent maintenance. In addition, the tree-based method does not guarantee that the returned template is the longest common subsequence. For example, the log message \(l=DAPBC\) (each letter represents a token, and the space between tokens is omitted) and the template set \(s=\{DA,ABC\}\), the prefix tree returns \(DA\) instead of \(ABC\). Since the second step will traverse the candidate set, the size of the candidate set also matters.  
In the previous benchmark [15], Spell and Drain failed to reach the first level efficiency, which means there is still room for improvement. Our goal is to find a more efficient method to obtain smaller candidate sets.
## 5 Zero-Knowledge Protocol
### Authentication Protocol  
The complete authentication protocol is outlined by the following steps, which are performed between [PERSON] (the prover) and [PERSON] (the verifier):  
**Key Generation**:  
1. [PERSON] picks a graph \(G\) and randomly generates a permutation of the set \(\{1,\ldots,n\}\). This permutation is used to create the isomorphic graph \(H\) together with its isomorphism \(\phi\), and then, the public key \(F_{0}\) using the technique aforementioned. The private key is the pair \((F_{0},\mathbf{x_{0}})\), which consists of the public polynomial system together with a solution to the system.  
**Authentication**:  
1. [PERSON] generates a permutation \(\sigma\) for the set \(\{1,\ldots,n\}\) at random and computes the polynomial system \(F_{1}\), which is sent to [PERSON] as a _compromise_.
2. [PERSON] creates a challenge by selecting at random \(b\in\{0,1\}\). [PERSON] sends \(b\) to [PERSON].
3. Once [PERSON] has received \(b\) she must answer accordingly: * If \(b=0\), she sends the transformation \(\Psi\) to [PERSON]. * If \(b=1\), then she sends the solution \(\mathbf{x_{1}}\) of \(F_{1}\).
4. According to the value of \(b\) [PERSON] performs the following to authenticate [PERSON]: * If \(b=0\), he computes the system \(F_{1}^{\prime}=\Psi(F_{0})\) and verifies whether he \(F_{1}^{\prime}=F_{1}\). * If \(b=1\), he checks whether \(F_{1}(\mathbf{x_{1}})=0\) or not.
# Interval-Based LDA Algorithm for Electrocardiograms for Individual Verification
## 2 Materials and Methods
### Threshold Values for Classification
#### 2.3.1 Threshold of Amplitude Similarity: \(TH_{\phi}(i,v)\)  
**Step 1:** Consider a subject A for the training set and let define the ACP as \(\phi_{c}^{A}(m)\) that consists of the ACPlets \(\phi_{c}^{A}(i,v)\) for \(c\in\{0,\dots,C-1\}\):  
\[\phi_{c}^{A}(m)=\left\{\phi_{c}^{A}(1,v),\phi_{c}^{A}(2,v),\cdots,\phi_{c}^{A }(i,v),\cdots,\phi_{c}^{A}(NI,v)\right\} \tag{7}\]  
In matrix form,  
\[\left[\begin{array}{c}\phi_{0}^{A}(m)\\ \phi_{1}^{A}(m)\\ \vdots\\ \phi_{c}^{A}(m)\\ \vdots\\ \phi_{\text{C-1}}^{A}(m)\end{array}\right]=\left[\begin{array}{cccccccc}\phi _{0}^{A}(1,v)&\phi_{0}^{A}(2,v)&\cdots&\phi_{0}^{A}(i,v)&\cdots&\phi_{0}^{A} (NI,v)\\ \phi_{1}^{A}(1,v)&\phi_{1}^{A}(2,v)&\cdots&\phi_{1}^{A}(i,v)&\cdots&\phi_{1}^{A }(NI,v)\\ \vdots&\vdots&&\vdots&&\vdots\\ \phi_{c}^{A}(1,v)&\phi_{c}^{A}(2,v)&\vdots&\phi_{c}^{A}(i,v)&\vdots&\phi_{c}^ {A}(NI,v)\\ \vdots&\vdots&\cdots&\vdots&\cdots&\vdots\\ \phi_{\text{C-1}}^{A}(1,v)&\phi_{\text{C-1}}^{A}(2,v)&\cdots&\phi_{\text{C-1 }}^{A}(i,v)&\cdots&\phi_{\text{C-1}}^{A}(NI,v)\end{array}\right] \tag{8}\]  
Figure 7: The relationship between the MSE (Mean Squared Error) \(\zeta\) and the IDA (Identification Accuracy) \(\eta\) according to NI (Number of Interval) values to show the decision of the optimal NI. In this experiment, the optimal NI is set as the value when \(\eta\ =99.646\%\) and \(\zeta=\varepsilon\)  
**Step 2:** Calculate the average in a vertical way to obtain the reference profile, \(\overline{\phi}^{A}(m)\) :  
\[\overline{\phi}^{A}(m)=\left[\overline{\phi}^{A}(1,v)\ \ \overline{\phi}^{A}(2,v)\ \cdots \overline{\phi}^{A}(i,v)\ \cdots\overline{\phi}^{A}(NI,v)\right] \tag{9}\]  
where  
\[\overline{\phi}^{A}(i,v)=\frac{1}{C}\sum_{c=0}^{C-1}\phi_{c}^{A}(i,v)\text{ for a given $i$ and $v$} \tag{10}\]  
**Step 3:** Define \(\Delta\phi_{c}^{A}(i,v)\) as the amplitude distance between \(\overline{\phi}^{A}(i,v)\) and \(\phi_{c}^{A}(i,v)\) :  
\[\Delta\phi_{c}^{A}(i,v)=\left|\overline{\phi}^{A}(i,v)-\phi_{c}^{A}(i,v)\right| \tag{11}\]  
where the distance \(\Delta\phi_{c}^{A}(i,v)\) is large if the descriptors are further apart in the descriptor space and small if they are closer together.  
We obtain:  
\[\begin{array}{l} TH_{\phi_{\text{max}}}(i,v)=max\{\Delta\phi_{c}^{A}(i,v) \big{|}\ \forall c\in\{0,1,\cdots C-1\ \}\},\\ \text{and}\ TH_{\phi}(i,v)=\mu_{\phi}^{A}(i,v)+\sigma_{\phi}^{A}(i,v)\text{ for a given $i$ and $v$}\end{array} \tag{12}\]  
where  
\[\begin{array}{l}\mu_{\phi}^{A}(i,v)=mean\{\Delta\phi_{c}^{A}(i,v)\big{|}\ \forall c\in\{0,1,\cdots C-1\}\}\\ \sigma_{\phi}^{A}(i,v)=std\{\Delta\phi_{c}^{A}(i,v)\big{|}\ \forall c\in\{0,1, \cdots C-1\ \}\}\end{array} \tag{13}\]  
Note that the amplitude distance \(\Delta\phi_{c}^{A}(i,v)\) is small and thus \(TH_{\phi}(i,v)\) is most likely smaller than \(TH_{\phi_{\text{max}}}(i,v)\), since this parameter reflects the distance between intra-subjects.