esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 3 AI-Driven Blockchain Solutions
### AI-Driven Consensus Algorithm  
AI-driven consensus algorithms are emerging as a core driving force in the intelligent evolution of blockchain systems, aiming to enhance security, efficiency, and scalability through advanced machine learning techniques. These algorithms primarily focus on three key optimization areas: dynamic node selection, malicious behavior detection, and resource allocation prediction. In dynamic adaptive consensus mechanisms, reinforcement learning can adjust PoW or PoS parameters in real time--such as dynamically modifying block generation intervals or probabilities--to improve system throughput and responsiveness. A notable example is XG-PBFT, which utilizes graph neural networks to optimize communication paths between nodes, reducing the communication complexity of traditional PBFT from O(n\({}^{2}\)) to O(n).  
In terms of security, deep learning models such as LSTM are employed to analyze historical behavior of nodes and identify abnormal patterns, including selfish mining, sybil attacks, or 51% attacks. These detection mechanisms are often integrated with reputation-based systems (e.g., CE-PBFT) to effectively filter out malicious nodes and reduce the risk of consensus centralization. For resource optimization, federated learning (FL) and distributed edge intelligence frameworks enable efficient task allocation across edge nodes, thereby minimizing energy consumption and network latency while preserving data privacy. Systems like FGADL-DEVCA have demonstrated the ability to save up to 30% of energy costs in multi-node environments.  
Based on a comprehensive analysis of existing research, current optimizations in AI-driven blockchain consensus primarily focus on improvements in POW and POS consensus mechanisms. These AI-driven consensus algorithms can be categorized according to the main challenges they address, such as resource-constrained environments, security and privacy concerns, economic incentives, and resource optimization. Broadly, these can be  
Figure 7: Schematic of the existing challenges of AI-driven blockchain.  
classified into four categories: AI-driven IoT/Edge computing consensus mechanisms, AI-powered security and privacy protection consensus, AI-driven economic incentives and resource optimization consensus, and AI-driven intelligent computing and deep learning consensus.
## 8 Experimental Evaluation
### M-String Operations  
The first group of benchmarks focuses on the use of resources by abstraction. Benchmarks compare the effectiveness of abstract domain operations with the automatically abstracted implementation of standard library functions from PDCLib, a public-domain libc implementation, using only essential abstract operations: lift, update and access. The results depicted in Table 2 were measured with parametrized M-String inputs of two kinds (l is a parametric length of the input):  
* _Word_\(w\) is a string of the form: \(w=c_{1}^{i_{1}}\cdot c_{2}^{i_{2}}\cdot\ldots\cdot c_{1}^{i_{l}}\) where \(\sum_{k=1}^{l}i_{k}\leq l\) and \(c_{x}\) is an arbitrary character from domain \(\overline{\mathbf{C}}\).
* _Sequence_\(w\) is a string of the form \(w=c^{i}\), where \(i\leq l\) and \(c\) is a character from domain \(\overline{\mathbf{C}}\).  
For each standard library function and input type, we created an isolated benchmark in two variants: one using an abstract semantics of M-String operations (see Table 2) and the other variant (Table 3) only with an automatic abstraction of essential aggregate operations.  
The first notable difference between automatically abstracted implementations of library functions and M-String operations is that the analysis of the former timeouts for input strings longer than 64 characters. The main cause of the lifted implementation's inefficiency is that it has to iterate over all characters, while M-String operations leverage iteration over larger segments. This difference also causes a blow-up of the model checker's state space for the lifted implementations while the state space size does not change for M-String operations. The reason for this is the fact that the number of segments does not change with the length of the input. Therefore M-String operations always perform the same computation independently of the M-String length.
## 2 Introduction of Speech Feature Representation and Acoustic Model Configuration
### Mel-Frequency Cepstral Coefficients and Mel-Frequency Filter-Bank Features
_Logarithmic operation:_ The human auditory system tends to adjust the strength of the received sound to protect the ears. When the input sound has a high amplitude, it will be suppressed by the human ears. To portray such a function, a logarithmic operation is applied to the mel-scaled spectrum \(S[l]\) as follows: \[S^{l}[l]=\log(S[l]),l=1,2,\ldots,L\] (8) In particular, the features \(S^{(l)}[l]\) obtained in Equation (8) are called logarithmic mel-scale filter-bank spectra, which are often referred to as FBANK features. * _Discrete Cosine Transform (DCT):_ The FBANK features can be converted to the cepstral domain via the discrete cosine transform (DCT). As mentioned earlier, the DCT has the ability to produce nearly uncorrelated features, and here the resulting outputs are MFCC features. The DCT operation to obtain the MFCC, \(y[p]\) from FBANK \(S^{(l)}[l]\), is as follows: \[y[p]=\frac{1}{L}\sum_{l=1}^{L}S^{(l)}[l]\cos(\frac{\pi p(l-0.5)}{L}),p=0,1, \ldots,J-1<L\] (9) Specech sample Pre-emphasis
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 2 Materials and Methods
### Characterization of the Materials Used
#### 2.1.2 Portland Cement (PC)  
The soil was stabilized using a composite Portland cement (CEM II/B-M(S-LL) 42.5 R), in accordance with EN 197-1 [50], selected for its rapid strength development and proven efficacy in geotechnical applications. The hydration process of this cement forms rigid matrices that bind soil particles and enhance the mechanical properties of treated soils [51]. The cement comprises 65-79% clinker, 6-20% granulated blast furnace slag (S) and 6-20% limestone (LL) by mass [50]. Its chemical composition is characterized by 56.2% CaO, 19.8% SiO\({}_{2}\), and 7.1% Al\({}_{2}\)O\({}_{3}\), among other oxides [52]. These ensure that the PC has a minimum compressive strength of 42.5 MPa at 28 days, qualifying it as a high-early-strength cement (denoted by \"R\"). The inclusion of slag improves sulfate resistance and long-term durability by refining pore structures and mitigating deleterious expansive reactions [53; 54], while limestone enhances particle packing and fresh-state workability [55]. In soil stabilization, these attributes contribute to increased load-bearing capacity, reduced plasticity and enhanced resistance to environmental degradation. However, successful application necessitates careful evaluation of site-specific variables, including soil mineralogy, organic content and curing regimes, to ensure compatibility and long-term performance [56]. Table 1 gives the detailed chemical composition of the PC.
## 3 Mathematical Model for Optimal Trajectory Planning
### Constraint Function Design
#### 3.3.2 Restricted Area Constraint (RAC)  
For some specific areas (e.g., government-sensitive regions), monitoring drones cannot be allowed to enter due to the regulations. A legal path should be carefully designed to avoid those forbidden areas. For simplicity, we assume that those forbidden areas are rectangles. Therefore, the forbidden area constraint can be formulated as:  
\[RAC=0,\;RAC=\sum_{i=1}^{N_{w}}RAC_{i} \tag{20}\]  
where,  
\[RAC_{i}=\left\{\begin{array}{cc}P,&if\,waypoint\,in\,range(x_{j},y_{j})\\ 0,&otherwise\end{array}\right.\]  
where,  
\[range(x_{j},y_{j})\;=\;\left\{l_{x}\leq x_{j}\leq u_{x}\right\}\;\cap\;\left\{ l_{y}\leq y_{j}\leq u_{y}\right\}\]  
where \(N_{w}\) is the number of waypoints, \(l_{x},l_{y}\) are the lower bounds of \(x\) and \(y\), \(u_{x}\), \(u_{y}\) is the upper bounds of \(x\) and \(y\).
# Generating Datasets for Real-Time Scheduling on 5G New Radio
## 5 Formulation of the SMT Problem
### Improvement
Based on SMT(\(n\)), if an uncovered packet with \(m_{i}=1\) can be placed under other packets, then SMT(\(n+1\)) is satisfiable. (2) SMT(\(n\)) is satisfiable, and SMT(\(n+1\)) is not satisfiable. Based on SMT(\(n\)), if there is no uncovered packet with \(m_{i}=1\), then SMT(\(n+1\)) must not be satisfiable. (3) SMT(\(n\)) is satisfiable, and SMT(\(n-1\)) is satisfiable. After SMT(\(n\)) is satisfied, if available resources are sufficient to make a covered packet with \(m_{i}=1\) uncovered, then SMT(\(n-1\)) is satisfiable. (4) SMT(\(n\)) is satisfiable, and SMT(\(n-1\)) is not satisfiable. After SMT(\(n\)) is satisfied, if there are no resources available, and no packet can be covered anymore, then SMT(\(n-1\)) cannot be satisfiable. By combining Theorem 1 (sufficient condition for unschedulability), Theorem 2 (lower bound of \(A\)), Algorithm 1 (upper bound of \(A\)), Equation (11) (upper bound of \(A\)), and Theorem 3 (non-monotonicity), the 5G NR scheduling problem can be solved quickly by Z3. The time complexities are as follows: checking Theorems 1 and 2 are \(O(M|V|)=O(n^{2})\) and \(O(|V|)=O(n)\), respectively; searching the value of \(A\) is \(O(\sum\limits_{\forall m\in[1,M]}(w^{m}\times|V^{m}|))=O(n)\); Algorithm 1 is \(O(n^{4})\); Equation (11) is \(O(n)\). Therefore, excluding Z3, the time complexity of our proposed method is \(O(n^{4})\).
## 3 Experimental Results and Analysis
### Strength Characteristic Analysis
The residual strength (the residual resistance to external load after the deformation and failure of the sample depends on the friction between the blocks and particles) is significantly improved due to the increase in interparticle friction caused by high confining pressure. The triaxial compressive strength test results of water-sensitive shale with different water contents under confining pressures of 60 MPa, 70 MPa, and 80 MPa show (see Table 3) that the percentages of residual strength and peak strength of water-sensitive shale with the same water content increase with an increase in confining pressure, except for the small increase under dry conditions. Under confining pressures ranging from 60 MPa to 80 MPa, the increase rate was 2.36%, and the rate was large under the water-containing state. The increase rate was increased under conditions of 2%, 4%, 6%, and 8%, to 10.32%, 12.13%, 14.18%, and 21.1%, under confining pressures ranging from 60 MPa to 80 MPa. The greater the water content, the smaller the void, and the greater the effective stress produced by confining pressure transformation. The softening coefficient of shale under high confining pressure increases with the increase in pressure. However, due to the interactions of other factors in the compound state of high confining pressure, from 60 MPa to 70 MPa, the softening coefficient did not increase by 1.95%. From 70 MPa to 80 MPa confining pressure, the increase in softening coefficient was 0.47%. It can be seen that the increase in softening coefficient decreases with the increase in confining pressure. Figure 4: Fitting curves of strength under different confining pressures and water contents.
## 5 Discussion and Analysis
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## 3 Results and Discussion
### CH/CNC and CH/CD Edible Films
#### 3.1.1 Thickness and Physicochemical Properties
[34]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Proportion of**} & \multirow{2}{*}{**Ph**} & \multicolumn{2}{c}{**Surface Tension**} & \multirow{2}{*}{**\(\mathbf{\eta}\) (**mg\(\cdot\)s, 256 s\({}^{-1}\)**)**} & \multirow{2}{*}{**k (Pa\(\cdot\)s)**} & \multirow{2}{*}{**n**} \\  & **CH/CNC or CH/CD** & & & & & & \\ \hline CH & 100/0 & 4.582 \(\pm\) 0.026 \({}^{\rm\,\AA}\) & 52.017 \(\pm\) 0.775 \({}^{\rm\,\AA}\) & 0.948 \(\pm\) 0.003 \({}^{\rm\,\AA}\) & 167.34 \(\pm\) 1.41 \({}^{\rm\,\AA}\) & 0.8366 \(\pm\) 0.0572 \({}^{\rm\,\AA}\) & 0.7122 \(\pm\) 0.0153 \({}^{\rm\,\AA}\) \\  & 75/25 & 4.311 \(\pm\) 0.059 \({}^{\rm\,\AA}\) & 50.042 \(\pm\) 0.480 \({}^{\rm\,\AA}\) & 0.957 \(\pm\) 0.008 \({}^{\rm\,\AA}\) & 90.67 \(\pm\) 1.18 \({}^{\rm\,\AA}\) & 0.4599 \(\pm\) 0.0444 \({}^{\rm\,\AA}\) & 0.7096 \(\pm\) 0.0157 \({}^{\rm\,\AA}\) \\ CH/CNC & 50/50 & 3.556 \(\pm\) 0.072 \({}^{\rm\,\AA}\) & 50.093 \(\pm\) 1.124 \({}^{\rm\,\AA}\) & 0.948 \(\pm\) 0.005 \({}^{\rm\,\AA}\) & 67.33 \(\pm\) 0.71 \({}^{\rm\,\AA}\) & 0.377 \(\pm\) 0.0432 \({}^{\rm\,\AA}\) & 0.6868 \(\pm\) 0.0233 \({}^{\rm\,\AA}\) \\  & 25/and [PERSON] et al. [35], where the basic film forming material was gelatin (another widely used basic material), lower moisture values were observed in the present study, which is obviously considered beneficial. Finally, the thickness of the final formed edible films was not significantly changed by the addition of either CNC or CD to the CH, regardless of the level of addition, with all films presenting a thickness between 0.03 mm and 0.04 mm. The above results show that both CNC and CD, provide composite films with improved physicochemical properties. With the only exception in the case of humidity, CD proved slightly more advantageous than CNC.
## 3 Results and Discussion  
### Determination of Degree of Chloromethylation (DCM)  
DCM of PEK-C, defined as the average number chloromethyl groups per repeat unit of PEK-C, was used to evaluate the immobilization amount of catalyst. The Step CAPEK-C (2) from PEK-C (1) in Figure 1, chloromethylation of PEK-C plays an important role in the subsequent ILs immobilization reaction. That is to say, the number of chloromethyl groups attached to the polymer could affect the immobilization probability of ILs and further the catalytic hydrolysis efficiency. In this study, the conditions of chloromethylation reaction are very harsh and not easily controllable. When lower PEK-C/CMOE molar ratio (1:4) and higher temperature (50 \({}^{\circ}\)C) were adopted, cross-linking occurred and a polymer gel generated. This is not conducive to subsequent immobilization reaction of ILs. First, the repeat-unit molecular weight of the CAPEK-C (2) based on its mass change relative to PEK-C was calculated, and then determined DCM from the above obtained molecular weight. The details for the calculation have been given in the experimental section. Table 1 lists the results between the DCM and reaction time. From the experimental results we can see that the DCM can be increased along with the reaction time. When dichloroethane was used as the solvent and anhydrous zinc chloride as the catalyst, the intermediate products (PEK-C-Cl) with different chloromethylated degree were prepared by controlling the amount of chloromethylation reagent within different reaction time, and the maximum DCM can reached 1.46 for 20 h. Meanwhile, when concentrated sulfuric acid was used as the solvent and catalyst, the DCM was obviously increased and can reached 3.51 at 30 \({}^{\circ}\)C for 20 h (shown in Table 1).  
However, concentrated sulfuric acid as a strong proton acid would cause equipment corrosion in a certain degree and it needs strict reaction conditions, such as PEK-C/CMOE molar ratio, in the chloromethylation process. On the other hand, when volatile dichloroethane was used as solvent, it will not only easily pollute the air, but relatively high cost of production, because of the high solvent price, long reaction time and elevated temperature. Thus, all things considered, the reaction media with concentrated sulfuric acid as catalyst was proposed. In addition, in order to obtain the optimal DCM, the catalytic performances of catalysts with different DCM were systematically investigated and the experimental results were analyzed in the followed results and discussion section.
## 4 Results and Discussion
### Optimal or Enhanced Model Results  
To ensure the robustness, reliability, and stability of the proposed enhanced or optimal FND model, we conducted three independent training runs on each of the three benchmark datasets. FakeNewsNet, ISOT, and LIAR. This multi-run evaluation strategy mitigates the influence of randomness and helps confirm the consistency of the model's performance across varying initializations and data shuffling.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Model** & **Dataset** & **Test Accuracy** & **Test F1** & **Parameters** \\ \hline BERT & FakeNewsNet & 0.4750 & 0.3059 & 4,518,658 \\ BERT & ISOT & 0.9997 & 0.9997 & 4,518,658 \\ BERT & LIAR & 0.5866 & 0.5878 & 4,518,658 \\ RoBERTa & FakeNewsNet & 0.4750 & 0.4740 & 4,518,658 \\ RoBERTa & ISOT & 0.9990 & 0.9990 & 4,518,658 \\ RoBERTa & LIAR & 0.5924 & 0.5826 & 4,518,658 \\ DeBERTa & FakeNewsNet & 0.5250 & 0.4326 & 4,568,066 \\ DeBERTa & ISOT & 0.9997 & 0.9997 & 4,568,066 \\ DeBERTa & LIAR & 0.5757 & 0.5764 & 4,568,066 \\ DistilBERT & FakeNewsNet & 0.5250 & 0.3615 & 4,502,146 \\ DistilBERT & ISOT & 0.9990 & 0.9990 & 4,502,146 \\ DistilBERT & LIAR & 0.5830 & 0.5772 & 4,502,146 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Baseline Performance of Transformer Models Across Datasets.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Avg Accuracy** & **Std Accuracy** & **Avg F1** & **Std F1** \\ \hline BERT & 0.6871 & 0.2257 & 0.6311 & 0.2849 \\ RoBERTa & 0.6888 & 0.2245 & 0.6852 & 0.2263 \\ DeBERTa & 0.7001 & 0.2128 & 0.6696 & 0.2407 \\ DistilBERT & 0.7023 & 0.2111 & 0.6459 & 0.2648 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Average Performance Metrics for Each Transformer Model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Component Removed** & **Avg Impact** & **Std Dev** & **Experiments** \\ \hline no\_attention & 0.1671 & 0.2176 & 9 \\ no\_layer\_normalization & 0.0352 & 0.0582 & 12 \\ no\_feedforward & 0.0224 & 0.0530 & 12 \\ no\_dropout & 0.0109 & 0.0436 & 12 \\ no\_positional\_encoding & 0.0103 & 0.0499 & 9 \\ complex\_classifier & 0.0082 & 0.0119 & 3 \\ no\_residual\_connections & 0.0078 & 0.0162 & 12 \\ no\_cls\_token & 0.0026 & 0.0030 & 3 \\ no\_gelu\_activation & 0.0014 & 0.0036 & 3 \\ no\_disentangled\_attention & 0.0006 & 0.0200 & 3 \\ no\_enhanced\_mask\_decoder & \(-\)0.0003 & 0.0211 & 3 \\ no\_relative\_position & \(-\)0.0075 & 0.0106 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Component Importance Across All Transformer Models.
## 4 Result and Performance Analysis
By contrast, ILPSO Scenario 3 exhibits a lower user data rate for a high channel gain user than ILPSO Scenarios 1 and 2 to ensure that ILPSO Scenario 3 can guarantee a higher data rate for low channel gain users. In ILPSO Scenarios 1 and 2, the user data rate gap between the high and low channel gain users is considerable due to violation of the constraints. ILPSO Scenario 2 reduces the gap between high and low channel gain users more than ILPSO Scenario 1, which has no user data rate constraint. Therefore, ILPSO Scenario 3 achieves a better balance between the high and low channel gain users whilst the low channel gain users still receive a sufficient data rate. Figure 2: System throughput vs. the number of users. Figure 5 illustrates the convergence of the proposed algorithm for the three scenarios. ILPSO Scenario 3 violates the constraints in the first 100 iterations. This scenario converges after 200 iterations and at lower throughput than ILPSO Scenarios 1 and 2. Moreover, ILPSO Scenarios 1 and 2 start with higher throughput and converge after 50 iterations with higher achieved throughput. The ILPSO algorithm stops once convergence is achieved, and higher throughput is reached during the last iteration. Hence, the algorithm stops moving through the maximum number of iterations because no changes occur in the convergence. ILPSO converges after at least 200 iterations for Scenario 3 and 50 iterations for Scenarios 1 and 2. ILPSO ends the process when the change per iteration is less than the tolerance \(1e^{-6}\) for the scenario that finishs with a lower number of iterations. ILPSO runs the risk of  
Figure 4: Achieved user throughput for high and low channel gain users. Figure 3: Achieved Jain’s fairness vs. the number of users. producing a sparse solution during earlier iterations. The set of constraints is important such that increasing the swarm size is unnecessary when capturing the global optimum.