esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
###### Abstract  
Monitoring Indicators of Compromise (IOC) leads to malware detection for identifying malicious activity. Malicious activities potentially lead to a system breach or data compromise. Various tools and anti-malware products exist for the detection of malware and cyberattacks utilizing IOCs, but all have several shortcomings. For instance, anti-malware systems make use of malware signatures, requiring a database containing such signatures to be constantly updated. Additionally, this technique does not work for zero-day attacks or variants of existing malware. In the quest to fight zero-day attacks, the research paradigm shifted from primitive methods to classical machine learning-based methods. Primitive methods are limited in catering to anti-analysis techniques against zero-day attacks. Hence, the direction of research moved towards methods utilizing classic machine learning, however, machine learning methods also come with certain limitations. They may include but not limited to the latency/lag introduced by feature-engineering phase on the entire training dataset as opposed to the real-time analysis requirement. Likewise, additional layers of data engineering to cater to the increasing volume of data introduces further delays. It led to the use of deep learning-based methods for malware detection. With the speedy occurrence of zero-day malware, researchers chose to experiment with few shot learning so that reliable solutions can be produced for malware detection with even a small amount of data at hand for training. In this paper, we surveyed several possible strategies to support the real-time detection of malware and propose a hierarchical model to discover security events or threats in real-time. A key focus in this survey is on the use of Deep Learning-based methods. Deep Learning based methods dominate this research area by providing automatic feature engineering, the capability of dealing with large datasets, enabling the mining of features from limited data samples, and supporting one-shot learning. We compare Deep Learning-based approaches with conventional machine learning based approaches and primitive (statistical analysis based) methods commonly reported in the literature.  
malware; machine learning; Deep Learning; few shot learning; Cyber Attacks +
Footnote †: journal: Advances in Neural Information Processing Systems
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 1 Introduction: Addressing Challenges in Fire Safety Engineering with Surrogate Modelling
### Research Scope and Objectives  
Despite the growing recognition of PISM in FSE, a comprehensive and systematic synthesis of its diverse applications, methodological approaches to physics integration, and inherent challenges is currently lacking. To address this gap and guide future research and implementation, this systematic review aims to answer the following research questions:  
1. What are the reported applications of PISM across various domains within FSE?
2. What distinct strategies for integrating fundamental physical principles into machine learning models (i.e., feature engineering techniques, loss-constrained techniques, architecture-constrained techniques, and offline-constrained techniques) are employed in PISM studies within FSE?
3. What are the current challenges and limitations associated with the development and application of PISM in FSE?
4. Based on the synthesis of existing literature, what best practices can be identified, and what stepwise framework can be proposed for the systematic creation of PISMs in FSE?  
By systematically addressing these questions, this work seeks to enhance both computational efficiency and physical fidelity in predictive models for FSE, ultimately advancing the field.  
Figure 4: Different strategies for implementing physics into models: loss-constrained (LCT), architecture-constrained (ACT), offline-constrained (OCT) and feature engineering (FET) (adapted from [50]).
## 3 Results and Discussion
### Impacts of Filtering
#### 3.1.1 Relative Velocity  
Overall, filtering reduced relative velocity data spread. Still, filter 1 increased relative velocity and data spread for trucks 2 and 3. In breaks, trucks were parked behind one another. Thus, removal of data from break periods serves to increase data spread, since these metrics differed more in driving periods. Excluding filter 1 (_Driving_ and _Following_), filter 2 (relative velocity \(\pm\) 30 km/h) affects the measured relative velocity the most, by  
Figure 6: Number of measurements after each filtering step has been applied.  
Figure 7: Histogram of measured distances as a function of filtering steps.  
removing data points corresponding to oncoming vehicles. This filter also reduced the average relative velocity to approximately \(-0.5\) km/h for both trucks. Thus, while average relative velocities approach zero (as they should in car-following situations), they remain slightly negative. The negative value is due to most detected objects (with the exception of the preceding trucks) heading toward the sensors (as opposed to receding from them). The curated datasets for both trucks had slightly more instances of negative than positive relative velocities. The impact of filter 2 is greater for truck 3 than for truck 2, despite both having approximately equal sizes of datasets. It removes 20% of measurements from truck 3 and only 12% from truck 2, versus the number of measurements remaining after filter 1. In fact, the dataset of truck 3 enters filter 2 with a larger average relative velocity, presumably due to platoon instability and weight differences in which perturbations caused harsh braking for truck 3, which would naturally tend to occur at short distances, which filter 2 ended up removing (cf. Figure 7). Subsequent filtering steps slightly reduce the variability of relative velocity measurements, suggesting that erroneous detections are gradually removed.
# Application of Deep Learning to Construct Breast Cancer Diagnosis Model
## 2 Materials and Methods  
This study applied the use of machine learning methods in artificial intelligence to train and construct machine learning models for breast cancer diagnosis using data related to patients' breast cancer risk factors and mammograms. In the first stage, ANN and SVM were applied to the breast cancer dataset, and their performances were compared to determine the relevant breast cancer risk factors. In the second stage, an image recognition model was setup using pretrained AlexNet, ResNet101, Inception_V3 fed with preprocessed mammograms. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithms to diagnose benign or malignant tumors, after which their accuracies were evaluated. Since the accuracy of breast cancer survivability is essential, the stepwise approach has significantly boosted the classification accuracy level. Furthermore, soft-voting was applied to average prediction results obtained from the pretrained networks.
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### CCS-Model Predictive Control of PMSM
#### 3.3.3 Robust Control Strategy  
In recent years, to improve the ability of generalized model predictive control and explicit model predictive control strategies to suppress unmodeled disturbances, scholars have proposed the following improved model predictive controls based on them: repetitive model predictive control (RMPC) [168] and iterative learning model predictive control (ILMPC) [169]. In reference [170], according to the discrete Fourier theory, the periodic disturbance is expressed as an internal model composed of multiple frequency signals in series, and the internal model is embedded in the prediction model so that the model prediction controller can suppress the disturbance corresponding to the elimination of the internal model of the signal generator. Reference [171] makes full use of the characteristics of periodic disturbance to directly embed the mathematical model of the repetitive controller into model predictive control. This kind of strategy does not need the frequency information of periodic disturbance, but only needs to determine the extreme range of the disturbance,  
Figure 11: High-bandwidth model prediction control block diagram.  
and then determine the predictive control law that can still meet the preset value function under the worst disturbance by solving the min-max optimization problem.  
The unbiased model predictive controller (OFMPC) [172] is a type of controller that first expands the disturbance into the state variables of the prediction model, then designs a Romberg observer or a Kalman observer to observe the expanded system state variables (including the original state variables and disturbance variables), using the observation results to achieve the purpose of eliminating disturbance [173]. However, due to the influence of the dynamic performance of the observer, this type of model predictive controller can only eliminate the constant or slowly varying type of disturbance.
## 1 Introduction
To the best of our knowledge, this study uniquely employs the simultaneous application of periodic and complete rescheduling while evaluating performance through innovative metrics for stability [5], robustness [6], and nervousness [7]. The comprehensive literature review, particularly the in-depth analysis of dispatching rules, is anticipated to serve as a valuable resource for researchers. Moreover, the unique problem structure of the production line addressed in this study is expected to provide significant insights and foster innovative perspectives within the field. In scheduling identical parallel machines, disruptions, stochastic processing times, and job rework introduce significant inefficiencies. Existing approaches often rely on optimization-based methods that are computationally impractical for large-scale, dynamic environments. Additionally, periodic and complete rescheduling strategies are rarely combined, and total weighted tardiness (TWT) and number of tardy jobs (NTJ) are not commonly used as the primary performance objectives in predictive-reactive rescheduling studies. This paper aims to bridge these gaps by introducing a heuristic-driven predictive-reactive rescheduling framework that integrates 13 dispatching rules and evaluates performance using innovative stability, robustness, and nervousness metrics. In the subsequent sections of this paper, we delineate the theoretical underpinnings of our predictive-reactive rescheduling framework, describe the implementation details of our methodology using Python code, and present empirical findings and case studies to demonstrate the efficacy and practical applicability of our approach. Through this comprehensive analysis, we aim to provide valuable insights and practical guidelines for the development and implementation of predictive-reactive rescheduling strategies in identical parallel-machine environments. This paper is organized as follows. Section 2 provides a detailed literature review, emphasizing the novelty of the MIU approach. Section 3 defines the problem formally and presents the underlying assumptions. Section 4 explains the MIU approach, detailing its methodology and implementation. Section 5 presents computational experiments and results, comparing MIU with traditional methods. Finally, Section 6 concludes with findings and future research directions.
## Appendix A  
**Proof of Theorem 1.** From (9) and (13), the OP of FU can be rewritten as  
\[P_{FU} =1-\Pr\left(\left|\dot{g}_{SR}\right|^{2}>\left(\theta\Big{(}1- \epsilon^{2}\Big{)}\Omega_{SR}\theta_{FU}\rho_{PB}+\frac{\theta_{FU}}{\left| g_{PS}\right|^{2}}\right)\right)\] \[\times\Pr\left(\left|\dot{g}_{FU}\right|^{2}>\left(\theta\Big{(} 1-\epsilon^{2}\Big{)}\Omega_{FU}\theta_{FU}\rho_{PB}+\frac{\theta_{FU}}{ \left|g_{PR}\right|^{2}}\right)\right) \tag{15}\]  
where \(\theta_{FU}=\frac{\gamma_{FU}}{\theta\epsilon^{2}\rho_{PB}(x_{F}-\gamma_{FU }\omega_{N})}\). Next, \(P_{FU}\) can be calculated as  
\[P_{FU} =1-\int\limits_{0}^{\infty}f_{\left|g_{PS}\right|^{2}}(x)\int \limits_{0}^{\infty}f_{\left|g_{PR}\right|^{2}}(y)\] \[\times F_{\left|\dot{g}_{SR}\right|^{2}}\left(\theta_{FU}\theta \Big{(}1-\epsilon^{2}\Big{)}\Omega_{SR}\rho_{PB}+\frac{\theta_{FU}}{x}\right)dz\] \[\times F_{\left|\dot{g}_{FU}\right|^{2}}\left(\theta_{FU}\theta \Big{(}1-\epsilon^{2}\Big{)}\Omega_{FU}\rho_{PB}+\frac{\theta_{FU}}{y}\right)dy \tag{16}\]  
With (1), (2) and Eq. (3.471.4) from [52], we obtain  
\[P_{FU} =1-\frac{e^{-2\theta_{FU}\theta\big{(}1-\epsilon^{2}\big{)}\rho _{PB}}}{\Omega_{PS}\Omega_{PR}}\int\limits_{0}^{\infty}e^{-\frac{\pi}{\Omega_ {PS}}}e^{-\frac{\theta_{FU}}{\Omega_{SR}}}dz\int\limits_{0}^{\infty}e^{-\frac {\pi}{\Omega_{PR}}}e^{-\frac{\theta_{FU}}{\Omega_{PU}}}dy\] \[=1-\frac{4e^{-2\theta_{FU}\theta\big{(}1-\epsilon^{2}\big{)}\rho _{PB}}\theta_{FU}}{\sqrt{\Omega_{SR}\Omega_{PS}\Omega_{FI}\Omega_{PR}}}K_{1} \left(2\sqrt{\frac{\theta_{FU}}{\Omega_{SR}\Omega_{PS}}}\right)K_{1}\left(2 \sqrt{\frac{\theta_{FU}}{\Omega_{FU}\Omega_{PR}}}\right) \tag{17}\]  
where \(K_{p}\) denotes the \(v\)-order modified Bessel function of the second kind. The proof is completed.
# Solanesol Biosynthesis in Plants
## 4 Key Enzymes in Solanesol Biosynthesis
### Isopentenyl Diphosphate Isomerase (Ipi)  
IPI catalyzes the conversion of IPP into DMAPP, using Mg\({}^{2+}\) as a cofactor (Figure 2) [3,6]. _IPI_ genes have been identified in _N. tabacum_[38], _A. thaliana_[39,40], _S. Lyopersicum_[41], and _Catharanthus roseus_[42]. In _N. tabacum_, two _IPI_ genes have been found (i.e., _IPI1_ and _IPI2_), which are expressed in the chloroplasts and cytoplasm, respectively [38]. High salinity and irradiation promoted the expression of _IPI_, while high salinity with low temperature increased the expression of _IPI2_, and treatment with 100 \(\upmu\)mol/L exogenous abscisic acid increased the expression of both genes [38]. Two _IPI_ genes have been identified in _A. thaliana_: _AtIPI1_ and _AtIPI2_, which are expressed in the plastids and mitochondria, respectively [39]. _AtIPI1_ or _AtIPI2_ single mutants appear to be normal, while double mutant plants display dwarfing and male sterility, and sterol and ubiquinone contents that are decreased by over 50% as compared to the wild type [40]. _Escherichia coli_ expressing the yeast _Saccharomyces cerevisiae IPI_ genes produces a variety of terpenoids [43]. Introduction of _IPI_ genes into single-cell green algae dose-dependently increased their carotenoid content [44]. Co-expression of potato _IPI_ and _SPS_ genes in _N. benthamiana_ significantly increased the solanesol content [1]. Hence, _IPI_ is a key enzymatic gene in the biosynthesis of terpenoids, and its overexpression or inhibition induces changes in the contents of downstream metabolites.
# Directly Synthesized Graphene-Based Photonics and Optoelectronics Devices
## 3 Application of Directly Grown Graphene  
### Ultrafast Fiber Laser  
The excellent optical properties of graphene, such as its broadband saturable absorption, ultra-wide spectral range because of the straight scattering of Dirac electrons, unique energy band gap, and ultrafast recovery time, makes it suitable for ultrafast pulse generation and a powerful element for fiber lasers [110]. Several studies have proven that graphene can be an excellent SA for both mode-locking and Q-switching pulse generation in infrared wavelengths [111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121]. However, directly synthesized graphene contouring the surfaces of the 3D-structured optical devices is highly anticipated to maximize the nonlinear interaction with guided light. Here, we have discussed two processes, in situ and ACS, that enable growing graphene directly on the optical devices so that no further transferring is required. Accordingly, by providing maximum surface coverage and eliminating the interstices that frequently have been found for the suspended graphene cases, maximum light-matter interaction can be ensured [109].  
A SA device, made of in situ grown graphene on the fiber end facet, as shown in Figure 7a, was employed to generate soliton pulses. The central wavelength and full width at half maximum (FWHM) of the generated ultrafast pulses were found to be \(\lambda\) = 1571.96 nm and 0.75 nm, respectively, as shown in Figure 7b. The pulse repetition rate was determined to be _frep_ = 15.06 MHz with a pulse duration of 2.6 ns for sech\({}^{2}\)-profile as shown in Figure 7c. Later, as presented in Figure 7d, the in situ grown graphene on the polished surface of a D-shaped fiber device was employed in the same cavity to achieve femtosecond pulses. In this case, the generated pulse was centered at 1559 nm with a FWHM of 3.44 nm (see Figure 7e), where the pulse repetition rate was determined to be 8.93 MHz (see inset of Figure 7e); the generated pulse was found in the fs scale with a measured duration of 910 fs (see Figure 7f). The time-bandwidth product (TBP) value of 0.386, indicating the output pulses were slightly chirped, was calculated from the pulses. These results indicate the stability of the soliton pulses generated by in situ graphene.  
Thereafter, another work reveals generating ultrafast pulses by a metal-free, transfer-free, directly synthesized conformal graphene synthesized by the ACS method [109].  
mboxfigfig:applsci-118015-f007g schematically illustrates how the ACS-grown graphene can maximize the nonlinear interaction, compared to the case of graphene transferred by conventional methods. In this study, the central wavelength of the pulses from an ACS grown graphene-enabled SA device reported to be \(\lambda\) = 1555.82 nm with an FWHM of 3.75 nm. The solitonic operation was found until 16.15 mW of intracavity power, beyond which the pulses were found to be stretched (see Figure 7h). The pulse duration in the temporal domain and TBP was 770 fs (see Figure 7i) and 0.35, respectively.
## 4 Result and Discussion
### Analytical Measurement and Visualization after Preprocessing
#### 4.1.3 Logistic Regression Algorithm  
According to the statistics, the logistic regression algorithm can be a reliable statistical version, in which its essential type that runs on the logistic functionality to simulate a binary determining factor; many complex extensions exist. It is an application for binomial Regression. In this experiment, we applied the logistic regression algorithm for classification on the dataset for reviews and found different information on different parameters concerning TF and TF/IDF. We calculated the accuracy of classification of each category application and reported precision, recall and F1 scores. Figure 5 illustrates a visualization bar chart for the logistic regression algorithm in which series1 indicates the accuracy of the logistic regression algorithm, series2 indicates the precision, series3 indicates the recall, and series4 indicates the F1 score measurement.  
Figure 4: (**a**) Bar-chart visualization of the TF random forest algorithm after preprocessing; (**b**) bar-chart visualization of the TF/IDF random forest algorithm after preprocessing.
## 3 Proposed Methodology for Out-of-Stock Prediction Model Using BCHO-TCN LightGBM
### Working of TCN LightGBM in Out-of-Stock Prediction
#### 3.5.2 Concatenation of Both Models  
The input value \(T\) is first processed by the TCN, after which it goes to LightGBM. Now, the output of the TCN is denoted as \(T_{TCN}\). The TCN manipulates spatial input using causal convolutions and dilation to capture temporal patterns using an efficient approximation. It prevents the release of data at the next time step and can handle variable-length inputs. The output \(T_{TCN}\) encodes the learned characteristics of the input sequence and then feeds the LightGBM model for the subsequent prediction. Let us call the function that learns for the LightGBM model \(l_{GBM}(T_{TCN})\), which is used to predict the target variable based on the information the TCN has extracted. Mathematically, the process can be represented as follows:  
\[T_{TCN}=l_{TCN}(T)\;\text{\#TCN} \tag{15}\]  
\[PO=l_{GBM}(T_{TCN})\;\text{\#LGBM} \tag{16}\]  
So, the final output is  
\[PO=l_{GBM}(l_{TCN}(T)) \tag{17}\]  
This procedure involves the TCN extracting informative temporal attributes from the input sequence. These are further integrated into the LightGBM model, which then makes the predictions. This combination uses the positive features of both models to optimize the model's forecasting abilities. The architecture of the proposed LightGBM-enabled deep temporal convolutional neural network is shown in Figure 2. Here, the input layer is represented in blue circles, the red circles correspond to hidden layers, and the output layer is indicated as yellow circles in the TCN. The configuration of the neural network layers is included in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Dimensions** & **Trainable Weights** & **Output Details** \\ \hline input\_1 (Input Layer) & [(None, 25, 1, 1)] & 0 & [] \\ \hline conv2d (Conv2D) & (None, 25, 1, 16) & 160 & [i’nput\_1[0][0]’] \\ \hline activation (Activation) & (None, 25, 1, 16) & 0 & [c’onv2d [0][0]’] \\ \hline batch normalization (Batch Normalization) & (None, 25, 1, 16) & 64 & [a’ctivation [0][0]’] \\ \hline max\_pooling2d (MaxPooling2D) & (None, 25, 1, 16) & 0 & [b’atch\_normalization [0][0]’] \\ \hline dropout (Dropout) & (None, 25, 1, 16) & 0 & [m’ax\_pooling2d [0][0]’] \\ \hline conv2d\_1 (Conv2D) & (None, 25, 1, 32) & 4640 & [d’dropout [0][0]’] \\ \hline activation\_1 (Activation) & (None, 25, 1, 32) & 0 & [c’onv2d\_1[0][0]’] \\ \hline batch\_normalization\_1 (Batch Normalization) & (None, 25, 1, 32) & 128 & [a’ctivation\_1[0][0]’] \\ \hline conv2d\_2 (Conv2D) & (None, 25, 1, 64) & 18,496 & [b’atch\_normalization\_1[0][0]’] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of the neural network layers.  
Figure 2: Architecture of the proposed TCN LightGBM.