esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structure and Mathematic Model
### Dynamic Model of IBPEH  
When the composite beam is in the first-order bending vibration mode, the piezoelectric energy harvester obeys the law of energy conservation, and the IBPEH model can be equivalently transformed into a lumped parameter model. Figure 4 is the lumped parameter model diagram of the IBPEH system, where \(M_{eq}\) represents the equivalent mass of the system, \(C_{eq}\) represents the equivalent damping of the system, \(R\) represents the PVDF external resistance, and \(F_{m}\) represents the magnetic force received by the magnet A.  
According to the equivalent model of lumped parameters of the piezoelectric energy harvester, based on [PERSON]'s second law and [PERSON]'s law, the nonlinear motion control equation of IBPEH system is established:  
\[M_{eq}\ddot{(t)}+cy\dot{(t)}-\dot{\sigma}v(t)+F_{r}+F_{m}=-\gamma z\ddot{(t)} \tag{10}\]  
\[\dot{\sigma}y(t)+C_{p}v(t)+\frac{v(t)}{R}=0 \tag{11}\]  
where \(v(t)\) is the voltage across the load resistor \(R\), and \(y(t)\) is the displacement of the end of the composite beam at time \(t\). \(z\ddot{(t)}=Acos(2\pi ft)\), where \(z(t)\) is the base vibration displacement, \(A\) is the excitation amplitude, and \(f\) is the excitation frequency. The parameters of the lumped parameter model of the piezoelectric energy harvester are  
\[\dot{\sigma}=\frac{\beta_{\theta}e_{31}b\big{(}h_{p}+h_{s}\big{)}}{L} \tag{12}\]  
\[C_{p}=\frac{\epsilon_{33}^{*}bL}{h_{p}} \tag{13}\]  
In the formula, \(A_{s}=b\big{(}h_{s}+2h_{p}\big{)}\), where \(\theta\) represents the equivalent electromechanical coupling coefficient of the system. \(e_{31}\) and \(\epsilon_{33}^{s}\) represent the piezoelectric constant and clamping permittivity of the piezoelectric material PVDF, respectively. \(\beta_{\theta}\) represents the correlation coefficient, and \(h_{p}\) represents the thickness of PVDF.
# Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System
## 5 Results of Experiments and Discussion
### Discussion
#### 5.4.2 Potential Model Improvements  
In this study, we utilized a convolutional encoder-decoder network. While simple and efficient, this structure lacks temporal modeling capabilities. More advanced image-generation models, such as Variational Autoencoders (VAEs) and diffusion models, can produce high-quality outputs but often require large datasets and computational resources.  
As our dataset expands, we plan to evaluate the application of such models to our drawing sequence task. In addition, integrating sequential models such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) networks into our architecture could allow the model to better preserve spatial continuity and semantic progression across drawing steps.  
Moreover, we compared our approach with PredRNN, a recurrent baseline model, and observed its superiority in long-term consistency and perceptual quality (lower LPIPS, higher SSIM). This suggests that incorporating explicit temporal memory can be critical for maintaining detail in deeper stages of sequential generation. However, this comes at the cost of greater model complexity.
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 4 Performance Evaluation
### Application and Discussion  
#### 4.4.1 Application  
The translation method proposed in this paper for vehicle body-related messages based on labeled bits can be applied to almost all commercially available vehicles. The method described in this paper only needs to collect CAN traffic and send test messages through the OBD-II interface. All vehicles must be equipped with the OBD-II interface [30] as a common automotive standard before they are launched [31; 32; 33; 34], because our method can use the OBD-II interface for data collection and sending for any vehicle model. In addition, OBD-II connection devices are now readily available in the market, ranging in price from tens [35] to hundreds of dollars [36], all of which can be easily used to send and receive CAN messages. Therefore, the method proposed in this paper can be easily applied to most car models.
# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 3 Experiment and Results
### Experimental Setup and Evaluation Metrics
#### 3.1.2 Evaluation Protocols  
To assess the efficacy of iris image super-resolution, we computed the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM), which quantify the fidelity and structural resemblance between the reconstructed and the original HR images [33,34].  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{L^{2}}{\text{MSE}}\right) \tag{10}\]  
\[\text{SSIM}(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{ 2}+\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{11}\]  
where \(L\) is the maximum possible pixel value of the image, and MSE represents the mean squared error between the reconstructed image and the reference image. \(\mu_{x}\) and \(\mu_{y}\) are the local means, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances, \(\sigma_{xy}\) is the covariance of images \(x\) and \(y\), and \(C_{1}\) and \(C_{2}\) are small constants for stability.  
To evaluate the practical effectiveness of the super-resolved images in recognition applications, we adopted MobileNetV3 [35] as the recognition model, chosen for its efficiency and good performance on classification tasks, and used the standard classification metrics of Precision, Recall and F1-Score to quantify the performance of the iris-recognition system.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{12}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{13}\]  
\[\text{F1-Score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{14}\]  
where \(TP\), \(FP\), and \(FN\) denote true positives, false positives, and false negatives, respectively.
# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 3 Materials and Methods
### Performance Metrics  
In this contribution a total of seven metrics are selected to assess the performance of our proposed model namely, Accuracy, Recall Score, Precision Score, Balanced Accuracy, F1 Score, Brier Score Loss, and ROC AUC Score; Each metric is described briefly in the following:  
**Accuracy (Acc)**: The accuracy is defined as the ratio of correctly predicted to total number of instances; it is calculated using the following formula:  
\[Acc=\frac{T_{+}+T_{-}}{T_{+}+T_{-}+F_{+}+F_{-}} \tag{1}\]  
where \(T_{+}\) and \(T_{-}\) are the number of correctly predicted positives and negatives, respectively, while \(F_{+}\) and \(F_{-}\) are the number of falsely predicted positives and negatives, respectively.  
**Recall Score (RS)**: Recall is defined as the ratio of correctly predicted positive instances to the total number of positive instances; it is calculated using the following formula:  
\[RS=\frac{T_{+}}{T_{+}+F_{-}} \tag{2}\]  
Figure 1: Architecture of the proposed Bagged Ensemble Learning Model.  
**Precision Score (PS)**: Precision is defined as the ratio of correctly predicted positive instances to the total number of predicted positive instances; it is calculated using the following formula:  
\[PS=\frac{T_{+}}{T_{+}+F_{+}} \tag{3}\]  
**Balanced Accuracy (BAcc)**: Balanced accuracy is defined as the average of recall and precision; it is calculated using the following formula:  
\[BAcc=\frac{RS+PS}{2} \tag{4}\]  
**F1-score (FI)**: F1-score is defined as the harmonic mean of precision and recall; it is calculated using the following formula:  
\[F1=\frac{2\times PS\times RS}{PS+RS} \tag{5}\]  
**Brier Score Loss (BSL)**: Brier score loss is a proper scoring rule which quantifies the accuracy of probabilistic predictions; it is calculated using the following formula:  
\[BSL=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2} \tag{6}\]  
where \(\hat{y}_{i}\) is the predicted probability of the \(i\)-th instance being positive and \(y_{i}\) is its actual class label.  
**ROC AUC Score (AUC)**: ROC AUC score is the area under the ROC curve; it quantifies the ability of the model to distinguish between positive and negative classes.
## 5 Experimental Results
### Results and Analyses

Table 4: Performance comparison between the ant search algorithm and our tabu search algorithm on the IEEE 39-bus system. Figure 16: The pictorial result of instance _ieee118-3-b_ generated by the algorithm with \(L=7\). Figure 17: The pictorial result of instance _ieee118-3-b_ generated by the algorithm with \(L=15\). [PERSON] _et al._[26] and [PERSON] _et al._[18] split the IEEE 118-bus system into three islands using the fast method and the OBDD (Ordered Binary Decision Diagram) based method. We present the performance comparison for the IEEE 118-bus system in Table 5. The fast method and our tabu search algorithm obtained the same power imbalance, namely 119.3 MW. The fast method consumed 0.012 s on a machine with a 2.2-GHz CPU, while our algorithm used 0.004 s (500 iterations) on a machine with a 3.3-GHz CPU. Our computation time is equivalent to about 0.006 s on a 2.2-GHz CPU machine. Therefore, our algorithm is better than the fast method, since it obtained the same solution with less computation time. The power imbalance obtained by our algorithm is greater than that obtained by the OBDD-based method. However, the OBDD-based method found different splitting strategies in five runs, and each run required much more time than our algorithm. [PERSON] _et al._[18] also claimed that their method is not applicable to large power systems. Before running the OBDD-based method, the power system needs to be simplified to the one with only 100 nodes. The time consumed by the OBDD-based method is 5 s, and the program was ran on a machine with a 1.86-GHz CPU. However, the computation time of our algorithm is only 0.004 s (500 iterations) on a machine with a 3.3-GHz CPU. Therefore, our algorithm requires less computational time, and the power imbalance can also be controlled under less than 3% of the total generation power.
## 3 Stroke Pattern Extraction
### Ranklet Transformation  
Detecting acute stroke by visual observation using NCCT is relatively insensitive. In this study, a Ranklet transformation was proposed to enhance the subtle contrast in the underlying NCCT patterns. Ranklet transformation changed the original pixel values into relative contrast coefficients [21]. Variations in gray-scale distributions were thus enhanced. Extracting textural features from the transformed patterns exhibited more-meaningful information for diagnosing stroke.  
The relative contrast is orientation selective in Ranklet transformations, such as the Haar function used in the wavelet transform [22] and contrast orientations including horizontal, vertical, and diagonal. The three orientations present spatial adjacency between two sides of a block in a local region, and the difference between two sides reveal local fluctuations. Under a resolution value, an image is divided into multiple blocks as local regions. 16 is the single resolution size used in the experiment for the MRI images \(512\times 512\). Different size parameters determine different spatial features. According to the  
Figure 1: Image processing used to divide the left and right sides of the brain for symmetric comparisons. (**a**) The original non-contrast computed tomographic (NCCT) image, (**b**) the extracted brain area, (**c**) the left side, and (**d**) the right side.  
three orientations, the two sides of each block are defined as \(X\) and \(X^{\prime}\) as shown in Figure 2. Then, the number of pixel pairs (\(P_{H}\), \(P_{L}\)) in each block is determined; that is, the relative rank of pixels of \(P_{H}\) in subset \(X\) is higher than that of \(P_{L}\) in the other subset \(X^{\prime}\). If there are \(C\) pixels in a block, then \(C/2\times C/2\) = \(C^{2}/4\) comparisons are calculated. The resulting number is normalized to between \(-1\) and \(1\). The Ranklet transformation coefficient, \(R_{O}\), is formulated as  
\[R_{0}=\frac{\sum_{p\in X^{\prime}_{O}}\pi(p)-\frac{C}{4}\Big{(}\frac{C}{2}+1 \Big{)}}{\frac{C^{2}}{8}}-1,\ O=H,\ V,D. \tag{1}\]  
In subset \(X^{\prime}_{O}\), pixel ranks, \(\pi(p)\), are summed. If more pixels in \(X^{\prime}_{O}\) are higher than those in \(X_{O}\), \(R_{O}\) is close to \(1\). Otherwise, it is close to \(-1\). If the pattern in a block has no strong variations, the coefficient is close to \(0\). After replacing the original pixel values by Ranklet coefficients, the contrast correlation of a local pattern is presented as shown in Figure 3.  
Figure 3: _Cont._  
Figure 2: Illustration of comparisons of differences in three orientations used in the Ranklet transformation, including horizontal, vertical, and diagonal patterns.
## 4 Scenarios
### Extract Features with CNNs
#### 4.2.2 Extract Features with Retrained CNNs
Finally, the smaller ResNet18 network requires the shortest time, while its classification accuracy results are close to those of ResNet50 and Inception_v3. Overall, the choice of the network should be made among one of the ResNet architectures, with ResNet50 being the middle ground. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multicolumn{5}{c}{**Total Time (s)**} \\ \cline{2-5}  & **ResNet18** & **ResNet50** & **ResNet101** & **Inception\_v3** & **EfficientNet-B0** \\ \hline KDEF & 2953 & 6642 & 8802 & 6764 & 19,808 \\ JAFFE & 131 & 327 & 683 & 638 & 1031 \\ RaFD & 3604 & 10,030 & 22,347 & 13,014 & 32,897 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Total time needed (in seconds) for retraining with 80% of the files and extracting results in 20% of the files for all databases. Figure 5: Classification accuracy after retraining each CNN on the corresponding database. The dashed lines depict the previous (without CNNs retraining) highest results for each database. After the retraining of the networks, the maximum classification accuracy has been:  
* For the KDEF database, the classification accuracy reached 94.59% with ResNet101 (an improvement of 13.4%). ResNet50 and Inception_v3 also marked a significant improvement in the classification rate, by 12.1% and 12.3%, respectively, in less time;
* For the JAFFE database, the retraining of the networks did not lead to higher results. The classification rate reached the previous level of 92.86% but with the increased time required for the retraining process;
* For the RaFD database, classification accuracy was increased by 3.17%, reaching 98.88%, with ResNet101 being the highest (in terms of classification accuracy) of all networks.
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 2 Mathematical Modeling for the CMfg Scheduling Problem
### Constraints for Real-Time Scheduling  
Constraints for real-time scheduling problems in the CMfg environment include task precedence, resources occupation, logistics duration, and so on.  
Specifically, Constraints (10) and (11) indicate the logical limitation of allocation, which means that for type-matched MRs, any MT can only be assigned to one of them for processing, and each of the MT can only be processed once. As formulated in Equation (12), the processing time of any MT needs to be guaranteed, that is, the time span between the start time \(b_{j}^{(c)}\) and the finish time \(e_{j}^{(c)}\) of processing is at least equal to the required processing time \(p_{j}^{(c)}\).  
During the processing of MT, Equation(13) means the available capacity of MR needs to be no less than the capacity required by the MT, and Equation (14) indicates the sum of occupied capacity by active MTs does not exceed the maximum resource capacity at time \(t\). Take \(\mathbb{R}_{2}^{(0)}\), for example, as shown in Figure 4, the capacity upper bound is \(\overline{V}_{2}^{(0)}=5\), and the rectangle in black indicates the resource occupation of the provider's internal planning task. At time \(t=16\), the maximum available capacity of \(\mathbb{R}_{2}^{(0)}\) becomes \(V_{2}^{(0)}(16)=\overline{V}_{2}^{(0)}-1=4\), so it is reasonable to manufacture \(\mathbb{T}_{4}^{(3)}\) (\(v_{4}^{(3)}=3,b_{4}^{(3)}=12,e_{4}^{(3)}=18\)) and \(\mathbb{T}_{2}^{(1)}(v_{2}^{(1)}=1,b_{2}^{(1)}=14,e_{2}^{(1)}=19\)) at this time slot since the sum of occupied capacity does not exceed (\(v_{2}^{(1)}+v_{4}^{(3)}=4\leq V_{2}^{(0)}(16)\)); hence, we get the active MT set as \(\mathcal{M}_{2}^{(0)}(16)=\{(2,1),(4,3)\}\).  
Figure 4: Capacity occupation of \(\mathbb{R}_{2}^{(0)}\), checking at \(t=16\).  
Figure 5 depicts the precedence constraints in MP that contain Equations (15) and (16), which, respectively, mean the ready time of any MT is no earlier than the complete time of its predecessors and the start time of any MT is no earlier than its ready time plus its logistics duration.  
Where the auxiliary variable \(l_{j}^{(c)}\) is defined as Equation (22).  
\[l_{j}^{(c)}=\sum_{i\in\mathcal{I}_{k}}x_{i,j}^{(c)}L_{c,i}^{(k)}\quad k=\kappa_ {j}^{(c)};j\in\mathcal{J}_{c};c\in\mathcal{C} \tag{22}\]
## 4 Computational Study
### Experimental Results  
The initial strategy to estimate the best period is to train and evaluate the regression algorithms individually, then apply a stacking approach to combine those algorithms into an ML ensemble, as shown in Figure 2. The final strategy entails training the Long Short-Term Memory (LSTM) algorithm and then aggregating it into an ML and DL ensemble, also shown in Figure 2. Table 2 represents the results of the implemented approaches. MAE, the R\({}^{2}\) score, and other error metrics commonly used for evaluating and reporting regression model performance, such as Mean Squared Error and Root Mean Squared Error [48], are used to evaluate the regression models' prediction accuracy.
# Novel Fault Injection Attack without Artificial Trigger
###### Abstract  
Theoretical process of fault injection attacks is defined as a process of recovering a secret key assuming that an attacker can inject faults into a specific targeted operation. Therefore, an artificial triggering is required to execute such an attack. However, when conducting analysis on real devices, artificial triggering needs to rely on a powerful assumption, such as manipulation of internal codes. In this paper, we propose a novel fault injection system using Input/Output (I/O) signals of target devices as a trigger for relaxing an attacker assumption. This system does not require an implementation of artificial triggering as input signals are used as a trigger in transmission of plaintexts for fault injection attacks. As a result, the attacker can perform fault injection attacks concerning the entire encryption process. To decide the fault injection time based on the trigger, the proposed system applies simple power analysis (SPA), employing electromagnetic emission of target devices. Considering that the fault injection time identified by SPA can be relatively vague compared with that obtained using a system based on an artificial triggering, we address this problem by proposing a process to recover the secret key without knowing the byte index of an injected fault.  
fault injection attack; AES DFA; electromagnetic fault injection; trigger setup; attacker's assumption +
Footnote †: journal: Applied Sciences
## 4 Surrogate-Based Optimization (SBO)
### Kriging Model  
Recently, the KM has extensively applied approximate complex computational models. The KM is a half parametric interpolation method, which contains the global model and local deviation [52], shown as follows:  
\[Y(x)=\beta+Z(x) \tag{14}\]  
where \(y(x)\) is the unknown objective function of \(x\), and \(\beta\) is the constant mean since the ordinary KM is used in this study. \(Z(x)\) is the local deviation, which is the realization of a stationary Gaussian process with mean zero.  
\[E[Z(x)]=0 \tag{15}\]  
\(Z(x)\) can be determined with non-zero covariance matrix of  
\[\mathrm{COV}\Big{[}Z\left(x^{i}\right),\ Z\left(x^{j}\right)\ \Big{]}=\sigma^{2}\mathbf{F}\Big{[}F\left(x^{i},x^{j} \right)\Big{]}, \tag{16}\]  
In Equation (16), the superscript \(i=j=1,\dots,\ n_{s}\). Here, \(n_{s}\) represents the number of sample points. \(\sigma^{2}\) is the process variance of the spatial correlation function scalar, and \(\mathbf{F}\) is the \(n_{s}\times n_{s}\) correlation matrix. \(F\left(x^{i},x^{j}\right)\) is the spatial correlation function between any two sample points \(x^{i}\) and \(x^{j}\). The correlation function is the Gaussian correlation function, which can be defined as Equation (17):  
\[F\Big{(}\ x^{i},x^{j}\Big{)}=\exp\left(-{\sum_{k=1}^{n}\theta_{k} \Big{|}x^{i}_{k}-x^{j}_{k}\Big{|}^{2}}\right) \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & \(\boldsymbol{\phi}\) & \(\boldsymbol{Re}\) & \(\boldsymbol{Hd}\) \\ \hline
1 & 0.9 & 600 & 1.5 \\
2 & 0.9 & 800 & 2.0 \\
3 & 0.9 & 1000 & 2.5 \\
4 & 0.9 & 1200 & 3.0 \\
5 & 1.0 & 600 & 2.0 \\
6 & 1.0 & 800 & 1.5 \\
7 & 1.0 & 1000 & 3.  
where \(n\) is the number of variables, and \(\theta_{k}\) is the unknown correlation parameter used to fit the model, which can be obtained by the maximum likelihood estimation (MLE) theory, as shown in Equation (18):  
\[\theta_{k}^{max}>0\left\{\frac{-\left[n_{s}\ ln\left(\partial^{2}\right)+ln( \mathbf{F})\right]}{2}\right\} \tag{18}\]  
The predicted value of the KM for the unknown \(x\) point is shown as follows:  
\[\hat{y}(x)=\hat{\beta}+f(x)^{T}\mathbf{F}^{-1}\big{(}y-1\hat{\beta}\big{)} \tag{19}\]  
where \(f(x)^{T}\) is the correlation vector between known sample points and unknown \(x\), as shown in Equation (20). \(y\) is the vector of \(n_{s}\times 1\), including the sample values of the response, and \(1\) is the unit vector of \(n_{s}\times 1\).  
\[f(x)=\left[F\Big{(}x,x^{1}\Big{)},F\Big{(}x,x^{2}\Big{)},\ldots,\ F(x,x^{n_{s} })\right]^{T} \tag{20}\]  
\(\hat{\beta}\) and \(\partial^{2}\) can be calculated as follows:  
\[\hat{\beta}=\frac{1^{T}\mathbf{F}^{-1}y}{1^{T}\mathbf{F}^{-1}1} \tag{21}\]  
\[\hat{\sigma}^{2}=\frac{\big{(}y-1\hat{\beta}\big{)}^{T}\mathbf{F}^{-1}\big{(}y -1\hat{\beta}\big{)}}{n_{s}} \tag{22}\]  
The mean square error (MSE) of the KM prediction at \(x\) is calculated by Equation (23), which represents the uncertainty of the prediction value.  
\[\hat{s}^{2}=\partial^{2}\left[1-f^{T}\mathbf{F}^{-1}f+\frac{1-1^{T}\mathbf{F} ^{-1}f}{1^{T}\mathbf{F}^{-1}1}\right] \tag{23}\]