esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Learning Framework for Enhancing Bridge Damage Prediction
## 3 Results and Discussion
### Evaluation Metrics  
Because our models are binary classification, we utilized performance metrics that experiment with binary classification models, such as accuracy, precision, recall (also called sensitivity), f1-score, ROC-AUC score, and error rate. All these metrics are taken from the confusion matrix, which shows the classifier's performance. Four elements comprise the confusion matrix: false positive (FP), false negative (FN), true positive (TP), and true negative (TN). Let us define:  
* \(y=\) actual labels;
* \(\hat{y}=\) predicted labels;
* \(N=\) total number of samples;
* False positive (FP): Incorrectly predicted positives (actual negatives);  
\[FP=\sum_{i=1}^{N}(y_{i}=0\text{ \emph{and} }\hat{y_{i}}=1) \tag{3}\]
* False negative (FN): Incorrectly predicted negatives (actual positives);  
\[FN=\sum_{i=1}^{N}(y_{i}=1\text{ \emph{and} }\hat{y_{i}}=0) \tag{4}\]
* True Positive (TP): Correctly predicted positives;  
\[TP=\sum_{i=1}^{N}(y_{i}=1\text{ \emph{and} }\hat{y_{i}}=1) \tag{5}\]* True Negative (TN): Correctly predicted negatives. \[TN=\sum_{i=1}^{N}(y_{i}=0\text{ and }\hat{y_{i}}=0)\] (6)
* The formula for evaluation metrics is as follows [29; 30]: \[\text{Accuracy}=\frac{(\text{TP}+\text{NT})}{(\text{TP}+\text{NT}+\text{FN}+ \text{FP})}\] (7)  
\[\text{Precision}=\frac{\text{TP}}{(\text{TP}+\text{FP})} \tag{8}\]  
\[\text{Recall}=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{9}\]  
\[\text{F1}-\text{score}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{10}\]  
\[\text{Error Rate}=\frac{(\text{FP}+\text{FN})}{(\text{TP}+\text{TN}+\text{ FP}+\text{FN})} \tag{11}\]  
\[\text{ROC-}AUC=\frac{SP+SE}{2} \tag{12}\]  
\[SP=\frac{TN}{TN+FP} \tag{13}\]  
where SP is the true negative rate (TNR), also called specificity, and SE is the true positive rate (TPR), also called sensitivity or recall [31].
# HAE: A Hybrid Cryptographic Algorithm for Blockchain Medical Scenario Applications
## 2 Relevant Knowledge
### Blockchain Technology  
In 2008, [PERSON] introduced a groundbreaking electronic transaction system that operates independently of a trusted third party, marking the inception of blockchain technology [14]. A blockchain is a decentralized global ledger database, fortified by cryptographic methods, ensuring its attributes of decentralization, immutability, data integrity, and transparency. This avant-garde technology amalgamates peer-to-peer (P2P) networking [15], asymmetric encryption [16], and consensus mechanisms [17]. Utilizing an encrypted blockchain architecture, it stores data and verifies their authenticity and precision. Its applications span various sectors, including financial transactions, healthcare, digital currencies, and more. Figure 2 delineates the hierarchical structure of a blockchain.  
In the blockchain architecture, the data layer employs hash functions and asymmetric encryption. This layer consists of data blocks and chained structures that house transac  
Figure 2: Hierarchy of blockchain.  
tional data, while Merkle trees archive state data. Integrating these methodologies ensures that the blockchain remains structured and resistant to tampering. At the network layer, the predominant protocol is P2P. Upon generating a new block, the primary node disseminates it to the subsidiary nodes. These nodes, upon receipt, proceed to validate the block's authenticity. Central to the blockchain's hierarchy is the consensus layer, which addresses the challenge of data consistency within distributed systems. Notable consensus algorithms encompass practical byzantine fault tolerance (PBFT) [18, 19, 20], delegated proof of stake (DPoS) [21], proof of stake (PoS) [22], and proof of work (PoW) [23]. The incentive layer primarily rewards nodes involved in computational and verification tasks. The intelligent contract anchors the contract layer. Within the blockchain ecosystem, all participants concur on predefined rules to deploy these smart contracts. When a transaction aligning with these conditions arises, the smart contract's code and algorithms are autonomously activated. The application layer, on the other hand, facilitates blockchain integration into everyday scenarios, predominantly spanning programmable currency, finance, and society. As blockchain gains traction, its potential to revolutionize sectors like healthcare becomes increasingly evident. For instance, [PERSON] et al. undertook a comprehensive review and analysis of the interoperability requisites for blockchain-supported EHR within the healthcare domain [24]. They introduced an interoperable framework for blockchain-based EHR [25] that aligns with the stipulations set forth by multiple national and international EHR standards, such as HIPAA and HL7. This framework guarantees the confidentiality, privacy, and integrity of medical records.  
Furthermore, based on accessibility and deployment strategies, blockchains can be categorized into public, private, and consortium chains. Table 2 elucidates the distinctions among these three types.
## 4 Methodology
### Machine Learning Models  
The ML approaches employed in our experiments are RF, GBT, SVM, and DNN; the hyperparameters adopted are shown in Table 2. We optimise the models using random hyperparameter search [48], using conditions at the upper and lower limits of the parameters used for the datasets generation. The models are not optimised for individual datasets. We keep the model's hyperparameters constant as it might be a factor that affects feature importance accuracy. We therefore limit our objectives to investigating how data characteristics affect interpretability methods and how the appropriate fusion of different feature importance methods produces less biased estimates. Furthermore, we try to minimise overfitting in the deep learning model by adopting dropout [49] and L2 kernel regularisation [50]. The number of epochs for the training of deep learning model is not fixed as early stopping is used. If the loss remains constant for 10 epochs, the training is automatically stopped.  
The feature importance methods employed by each ML models are listed in Table 3. For SHAP, we employ weighted k-means to summarise the data before estimating the values of SHAP. Each cluster is weighted by the number of points they represent. Using k-means to summarise the data has the advantage of lowering computational cost but slightly decreasing the accuracy of SHAP values. However, we compare the SHAP values from data with and without k-means for several datasets and found the SHAP values to be almost identical.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Models** & **Hyperparameters** & **Values** \\ \hline \multirow{4}{*}{Random Forest} & Number of trees & 700 \\  & Maximum depth of trees & 7 levels \\  & Minimum samples before split & 2 \\  & Maximum features & \(\sqrt{p}\) \\  & Bootstrap & True \\ \hline \multirow{4}{*}{Gradient Boosted Trees} & Number of trees & 700 \\  & Learning rate & 0.1 \\  & Maximum depth of trees & 7 levels \\  & Loss function & Least square \\  & Maximum features & \(\sqrt{p}\) \\  & Splitting criterion & Friedman MSE \\ \hline \multirow{4}{*}{Support Vector Regressor} & Kernel & Linear \\  & Regularisation parameter & 2048 \\  & Gamma & \(1\times 10^{-7}\) \\  & Epsilon & 0.5 \\ \hline \multirow{4}{*}{Deep Neural Network} & Number of layers & 8 \\  & Number of nodes for each layer & 64, 64, 32, 16, 8, 6, 4, 1 \\ \cline{1-1}  & Activation function for each layer & ReLU, except for output is linear \\ \cline{1-1}  & Loss function & MSE \\ \cline{1-1}  & Optimiser & Rectified Adam with LookAhead \\ \cline{1-1}  & Learning rate & 0.001 \\ \cline{1-1}  & Kernel regulariser & L2 (0.001) \\ \cline{1-1}  & Dropout & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter values for Random Forest, Gradient Boosted Trees, Support Vector Regression, and Deep Neural Network for all experiments.  
We train and evaluate the ML models using Intel(R) Xeon(R) CPU @ 2.20 GHz and the deep learning model is trained on Dell 16 GB NVIDIA Tesla T4.
## 3 Results and Discussion
### Weak \(B_{0}\) Field Limit Case  
The simulations for the SIDE and IDE models and for weaker \(B_{0}\) fields were prematurely stopped due to excessively high values of exponents in the expression for the kernel function for the arbitrary viscous torque (history acceleration torque). Therefore, in the weakest \(B_{0}\) fields, we only focused on the models without the history acceleration torque. In this weak \(B_{0}\) field limit case, Figure 1f,g, the stochastic Brownian torque effect rises and starts to dominate. In contrast, even the viscous history acceleration torque (part of the arbitrary viscous torque) is numerically unreachable; its effect is minimal and can therefore be neglected.  
Figure 2: Time evolution of each kind of torque for the strong \(B_{0}\) field limit generated with the SIDE model. For comparison, see the time evolution of \(\varphi(t)\) and \(\omega(t)\) in Figure 1a.  
The manifestation of stochastic behavior for the time evolution of angle \(\varphi(t)\) is only visible for the weakest magnetic flux density fields. Even for the time evolution of the angular velocity \(\omega(t)\), it occurs for the medium magnitudes of the magnetic field. The stochasticity of \(\varphi(t)\) will also be visible for the same values of \(B_{0}\) as for the stochastic behavior of \(\omega(t)\) in the case of omitting the inertial torque from the models (low Reynolds number limit, not shown here). This discrepancy only disappears in the case of times significantly longer than the inertial time \(\tau_{\text{inertial}}\equiv I_{\text{p}}/\gamma=0.03\,\upmu\text{s}\), when both the time evolution of the angle for models with the inertial torque and without it are jagged because the microscopic details are not resolvable. Otherwise, the time evolution of the angle for the model with the inertial torque is smoothed [42].  
The zero mean and uncorrelation of stochastic Brownian impulses to the magnetic particle are clearly visible in the averaged time evolution of \(\varphi(t)\) for an ensemble of \(N=1000\) samples of rotational magnetic particle alignment, as shown in Figure 3a for the SDE model, meaning that the evolution replicates the time dependence of \(\varphi(t)\) of the single ODE model. Furthermore, its region of variance, defined with lines of mean \(\pm\) std, does not change with the size of the finite timestep \(h\) (see Figure 3b).  
The stochastic Brownian torque term contribution to the overall dynamics of magnetic particle alignment increases with the weakening external magnetic flux density field \(B_{0}\), as discussed. This contribution will be even more visible for an increased thermodynamic temperature of the ambient fluid, while the stochastic Brownian torque depends on the square root of the thermodynamic temperature, Equation (7).
## 1 Introduction  
The development of electrically insulating coatings is extremely important for the lithium/ vanadium (Li/V) blanket of the fusion reactor [1; 2]. The coatings have to possess sufficiently high resistance and stability in liquid Li in order to mitigate the magnethydrodynamic (MHD) pressure drop to an acceptable level [3]. However, only a few materials constitute possible solutions, according to previous research work [3; 4; 5; 6], because the liquid lithium acting as a strong reducing agent can dissolve most materials, especially at elevated temperatures [6].  
A number of studies have shown that aluminum nitride (AlN) is a promising candidate that has high electrical resistivity and good compatibility with liquid Li as well as excellent resistance to irradiation [7; 8; 9]. In addition, AlN coatings can be potentially realized by in situ formation in liquid Li [10], which favor coating fabrication on surfaces with complex geometries. For monolayer AlN coatings, through-thickness defects are easy to develop since their thickness is usually limited to only a few tens of microns [11]. The presence of the through-thickness defects may degrade the integrity and insulation properties of the coating [11; 12]. The reason for this is that the V-alloy substrate absorbs nitrogen from liquid Li to form vanadium nitride (VN), which breaks the dissolution equilibrium [11]. To solve this stability issue, the development of multilayer coatings is thought to be an effective approach [13; 14; 15] and the \"V-alloy/AlN/metal\" (e.g., metal = Cr, Nb [13]) multilayer coating has been investigated. This bilayer coating with an integrated top metal layer can provide enough resistance. However, the integrity and corrosion issue of the metal layer in Li could be problematic because of thechemical interactions between the metal layer and the impurities (e.g., N, C) in lithium [16; 17]. In order to overcome the weakness, the \"V-alloy/Ti/AlN\" bilayer coating is proposed by the authors for the first time. This coating with a Ti interlayer is aimed at enhancing the stability of AlN coating in Li and depends on the formation of a titanium nitride (TiN) reaction layer at the Ti/AlN interface using an appropriate thermal treatment [18; 19; 20]. The TiN layer, which is chemically stable in lithium [4; 6], is expected to function as a protective layer to prevent the further invasion of lithium into the V-alloy substrate despite the possible production of any through-thickness defects in the top AlN layer.  
The Ti interlayer plays an important role in combing the V-alloy and AlN coating, which is why it is of great scientific and practical value to study the interface characteristics of V-alloy/Ti interface and Ti/AlN interface for fabricating the target V-alloy/Ti/AlN coating. However, the research on the interface characteristics of V-alloy/Ti joint has not been reported so far. In this work, the V-4 Cr-4 Ti/Ti diffusion bonded joint was produced by hot forging and the microstructure and mechanical properties of the joint were characterized.
## 3 Results and Discussion
### Phenolic Compounds
Notably, the control sample of Istarska Bjelica variety exhibited an exceptionally high TPC of 773 mg/kg, approximately 2.4 times higher than that of Levantinka (328 mg/kg) and 1.8 times higher than that of Oblica (429 mg/kg). In Istarska Bjelica oil, ligstroside aglycones and oleocanthal were the predominant phenolic compounds. Conversely, Levantinka oil contained the highest concentrations of olecein and oleocanthal, whereas Oblica oil was characterized by a predominance of oleuproein and ligstroside aglycones. The TPCs recorded for Istarska Bjelica slightly exceeded values reported previously [(35; 70; 71)] (414-685 mg/kg), while the TPCs for Levantinka and Oblica were within the range reported for these varieties (212-694 mg/kg and 50-791 mg/kg, respectively) [(35; 63; 72)]. Probably due to the different activity of endogenous enzymes determined by the genetic characteristics of the variety [(13; 28; 29)], the influence of FTT (temperatures of 25 \({}^{\circ}\)C and above) was significant only for the VOOs of the Oblica variety. Heating led to increased concentrations of olecein and oleocanthal, accompanied by a decrease in tyrosol, ligstroside aglycones and \(p\)-coumaric acid. These findings are consistent with previous reports indicating that elevated malaxation temperatures can enhance the concentrations of olecein and oleocanthal in VOOs produced both at laboratory scale [(71; 73; 74)] and industrial plant [(13)]. Several studies [(75; 48; 76)] have attributed such increases to the thermal activation of endogenous enzymes that facilitate the formation of phenolic compounds during malaxation or promote their release by disrupting cellular structures. However, the potential adverse effects of elevated olive paste temperatures, as we noticed on Oblica ligstroside aglycones, \(p\)-coumaric acid and partially tyrosol, must also be considered. Numerous studies have reported phenolic degradation via enzymatic oxidation mediated by polyphenol oxidase (PPO) and peroxidase (POD) produced both on laboratory scale [(77; 78)] and industrial plant [(79)]. In addition to heating, cooling the olive paste after crushing has also been shown to have a positive effect on the concentration and profile of phenolic compounds [(28; 29; 30)], but this was not the case in our study. Ultimately, the phenolic composition of VOO reflects a complex interplay among enzymatic activity, thermal stability of key enzymes (e.g., \(\beta\)-glucosidase, esterase, PPO, POD), and the partitioning behavior of phenolics between the oil and aqueous phases. All these processes are strongly influenced by the temperature of the olive paste during processing [(13; 14; 26; 29; 30; 31; 80)].
# On Computational Hardness of Multidimensional Subtraction Games +
## 2 Concepts and Results  
### Impartial Games  
Positions and moves of an _impartial_ game form a directed acyclic graph (DAG). It implies that positions in a play cannot be repeated. Such DAG may be infinite, but the set of positions reachable (by one or several moves) from any given position is finite. A choice of an initial position specifies an instance of a game. Two players move alternately. One who must move but is out of moves loses.  
Positions of an impartial game are divided in N-_positions_ and P-_positions_ (from words Next and Previous). A player who moves at an N-position has a winning strategy. A player who moves at a P-position has not a winning strategy (therefore the opponent has a winning strategy). In graph theory, the set of P-positions of a DAG is called its _kernel_; it can be found in time linear in the size of the DAG [24]. It gives an efficient algorithm solving an impartial game represented by the list of positions and moves. However, many games admit succinct description. The size of DAG may be exponential in size of the succinct description. It makes the kernel construction algorithm exponential.  
The Sprague-Grundy (SG) function refines the concept of N- and P-positions. For a set \(S\) of nonnegative integers the _minimum excluded value_ of \(S\) is defined as the smallest nonnegative integer that is not in \(S\) and denoted by \(\operatorname{mex}(S)\). In particular, \(\operatorname{mex}(\varnothing)=0\). The SG value of a position \(x\) is defined recursively as  
\[SG(x)=\operatorname{mex}\{SG(y):(x,y)\in E(G)\}, \tag{1}\]  
where \(G\) is the corresponding DAG. A position of SG value \(t\) is called a \(t\)-position. Then, P-positions are exactly \(0\)-positions, while N-positions have positive SG values.  
Taking in mind the relation with the Sprague-Grundy function, we assign to P- and N-positions the (Boolean) values \(0\) and \(1\), respectively. The basic relation between values of positions is  
\[p(v)=[p(v_{1}),\dots,p(v_{k})]=-\bigwedge_{i=1}^{k}p(v_{i})=\bigvee_{i=1}^{k} \
eg p(v_{i}), \tag{2}\]  
where all the possible moves from the position \(v\) are to the positions \(v_{1}\),..., \(v_{k}\). If \(v\) is a sink then \(p(v)=0\). We will use notation \([\dots]\) introduced in [19] for Boolean functions in Equation (2).
## 3 Results and Discussion
### Batch Studies
#### 3.2.4 Time Effect on the Adsorption of MB and IBF  
The MB and IBF sorption rate with AC-MOP was assessed between 1 and 80 min, as shown in Figure 8. Figure 8 displayed a similar trend, showing the adsorption capacities of AC-MOP increased as contact time increased. Phase 1 was ascribed to a faster adsorption rate occurring between 1 and 25 min in the initial stages. This was attributed to the readily available functional groups such as -CO -OH, -CO, -NH\({}_{2}\) and -CH=CH and pores on the AC-MOP surface, which allowed for greater adsorption. A similar trend was observed by [PERSON] et al. 2020 [41]. Stage 2 involves a slower increase due to the saturation of the active sites as time passes. AC-MOP reached equilibrium after 40 min.  
Two kinetic models were evaluated using PFO and PSO to determine the adsorption mechanisms of MB and IBF with AC-MOP. The results in Table 5 show that the pseudo  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **MB** & **IBF** \\ \hline Langmuir Q\({}_{0}\) (mg/g) & 74.1538 & 78.1524 \\ \hline b (L/mol) & 0.3094 & 0.5997 \\ \hline R\({}^{2}\) & 0.9987 & 0.9980 \\ \hline Freundlich N & 1.6514 & 1.5827 \\ \hline K\({}_{\text{f}}\) & 0.2577 & 0.2369 \\ \hline R\({}^{2}\) & 0.7870 & 0.8973 \\ \hline Exp q\({}_{\text{e}}\) (mg/g) & 73.7411 & 77.2327 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Isotherms for the uptake of MB and IBF.  
Figure 7: Concentration effect for AC-MOP at 25 \({}^{\circ}\)C and at pH 5.5, 0.4 g dosage and agitation at 200 rpm for 2 h.  
second-order projected capacity figures are more similar to experimental capacity data than the pseudo-first-order. The regression coefficient for the pseudo-second-order for both pollutants is close to 1 compared to the pseudo-first-order. According to the regression coefficient data, the pseudo-second-order better fits the mechanism than the pseudo-first-order. The results of the k\({}_{2}\) parameter indicated a higher adsorption rate for MB than IBF. This agrees with the results obtained in the time effect. The PSO model proposes that the sorption of MB dye and IBF with AC-MOP possibly occurred through electron transfer [42].
## 4 Experiment and Results
### ECA-ATCNet on MI-EEG Classification
#### 4.1.6 SIT-Conversion on ECA-ATCNet  
MI-EEG signals have been widely applied in many brain-machine interface (BCI) applications. In this paper, the Efficient Channel Attention Temporal Convolutional Network (ECA-ATCNet) is proposed, which demonstrates advanced performance in EEG-based motor imagery classification tasks. Considering the portability of wearable brain-machine interface devices, we study the ANN-to-SNN conversion to convert ATCNet to SNNs, reducing the model's power consumption while ensuring the decoding performance of MI-EEG signals.  
The proposed SIT-conversion is used to perform ANN-to-SNN conversion of the self-attention in ECA-ATCNet, as shown in Table 4. The accuracy of the ECA-ATCNet model by SIT-conversion is significantly higher than that of the directly trained HR-SNN [49] on both MI-EEG datasets. On the BCI-2a dataset, the accuracy of the ECA-ATCNet model by SIT-conversion is 9.68% higher than that of LeNet, and the model's energy consumption is reduced by 53.52%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Method** & **SNN Accuracy** & **Conversion Loss** & **Time Steps** & **Energy Reduction** \\ \hline \multicolumn{6}{c}{PhysioNet MI-EEG} \\ HR-SNN [49] & Direct Training & 67.24\% & / & / & / \\ ECA-ATCNet & ANN-to-SNN & **71.88\%**\({}^{1}\) & \(-\)0.73\% & 12 & \(\mathbf{-52.84\%}\)\({}^{1}\) \\ \hline \multicolumn{6}{c}{BCI-2a} \\ HR-SNN [49] & Direct Training & 77.58 & / & / & / \\ LeNet [65] & ANN-to-SNN & 77.61\% & \(-\)0.23\% & 100 & \(-\)0.29\% \\ ECA-ATCNet & ANN-to-SNN & **87.29\%**\({}^{1}\) & \(-\)0.60\% & 12 & \(\mathbf{-53.52\%}\)\({}^{1}\) \\ \hline \hline \end{tabular} \({}^{1}\) Bold indicates the best results.  
\end{table}
Table 4: Comparison of the ECA-ATCNet model by SIT-conversion with SOTA models.  
Due to the limited research on converting motor imagery classification models to SNNs through ANN-to-SNN conversion, in Section 4.2, we will provide a detailed comparative analysis of its performance in image classification tasks to validate the advancement and robustness of SIT-conversion.