esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Ransomware Detection with Deep Learning Models
## 3 PARSEC Dataset
### Dataset Features  
From the collected API calls of the PARSEC dataset, we generated several datasets that differ in the number of API calls taken from each process. We selected N initial API calls of processes to enable our models to detect malicious processes upon their startup; here, N is a parameter. The aim of our approach is the early detection of ransomware processes. If a process executes fewer API calls than required for the dataset, we performed  
Figure 2: Ransomware verification pipeline.  
data augmentation using oversampling. Specifically, we replicated sequences of API calls at random; this method guarantees datasets' consistency.  
We selected a number of API calls between 500 and 5000 to evaluate the potential for early ransomware detection based on limited API calls. It also helped us understand the implications of dataset size on the efficiency of our models. Note that the dataset size primarily affects the duration of training. Larger volumes of data extend the training time but may result in models that are better at generalizing across different ransomware behaviors. Conversely, smaller datasets reduce the training time but might limit the model's comprehensiveness in learning varied ransomware patterns. This balance is crucial for developing practical, deployable models that can be updated and retrained as new ransomware threats emerge. The naming convention for dataset variations is PARSEC-N, where N is the number of initial API calls included for each process. Therefore, we have PARSEC-N datasets for \(\mathrm{N}=500,1000,2000,3000,4000,5000\).  
The API features we recorded include process operation, process result, process duration, and process detail features (a full list of these features appears in Appendix A.2). We denote these feature lists as Ops, Res, Dur, and Det, meaning operations, results, duration, and detail features. In the basic setup, we started with operation features and only extended the list by adding the result features, and then we added the API execution times and detail features. By starting with basic features and incrementally adding complexity, we isolated the impact of each feature type on the models' performance. We denote as FLIST the list of features used in the dataset; it accepts the values Ops (process operation features), OpsRes (process operation and result features), OpsResDur (process operation, result, and duration features), and OpsResDurDet (process operation, result, duration, and detail features).
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 6 Results  
In this section, we evaluate the performance of MR, FZF, MMSE, and PZF in comparison to their corresponding DMD variants in the context of CF-MMIMO systems. We investigate how the distribution of antennas and the number of pilot sequences impact system performance, utilizing the closed-form SE expression with LSFD across various combining schemes. Our simulation setup mirrors that of a previous study [44], where \(L\) APs and \(K\) UEs are uniformly distributed within a circular simulation area with a radius of 1 km. The maximum transmit power for each UE is capped at \(p_{\max}=100\) mW, with a channel bandwidth of \(B=20\) MHz and a speed \(v_{min}\leq v\leq v_{max}\). Each AP is equipped with \(N\) antennas. Pilot sequences, \(\tau_{p}\), fewer than \(K\), are randomly allocated to the UEs. Following the approach outlined in [43], the simulations use Montecarlo, with 256 scenarios,and each scenario is used 100 times. Based on the 3 GPP model [45,46], the likelihood of a Line-of-Sight (LoS) path is influenced by the distance. The probability of having a LoS connection for the channel between UE \((l,i)\) and \(BSj\) is  
\[Pr(LoS)=\begin{cases}\frac{300-d_{li}^{i}}{300},&0<d_{li}^{j}<300\text{ m}\\ 0,&d_{li}^{j}>300\text{ m}\end{cases} \tag{104}\]  
If the LoS path exists, then the corresponding large-scale fading coefficient is modeled (in dB) as  
\[\beta_{li}^{j}=-30.18-26\log_{10}(d_{li}^{j})+F_{li}^{j}, \tag{105}\]  
We apply the heuristic UL power control policy from [3], where the transmit power UE \(k\) in the cell \(j\) is  
\[p_{jk}=\begin{cases}p_{max}^{ul},&\Delta>\frac{\beta_{jk}}{\beta_{j,min}^{j}} \\ p_{max}^{ul}\Delta\frac{\beta_{j,min}^{j}}{\beta_{jk}^{j}},&\Delta\leq\frac{ \beta_{jk}^{j}}{\beta_{j,min}^{j}}.\end{cases} \tag{106}\]  
Various simulations are performed, taking into account the Doppler effect. Here, \(T_{s}\) denotes the sampling time, while \(f_{D,k}=\left(\frac{\tau_{b}\tau_{f}}{c}\right)\) represents the Doppler shift for a UE moving at velocity \(v_{k}\), where \(f_{c}\) and \(c\) are the carrier frequency and the speed of light, respectively [19]. Our primary focus is on addressing the issue of channel aging and mitigating its effects through time-delay correlation. In all scenarios, the maximum normalized Doppler shift \(f_{D}T_{s}\) is utilized [23].
## 4 Engine Test Results and Discussions  
### Fuel Effects on Engine Performance  
At full load conditions, the brake torque (\(T_{tq}\)) and brake specific fuel consumption (BSFC, \(b_{e}\)) are shown in Figure 5 when the engine runs on the 9 fuels. Decalin-blended fuels give the best power performance, and heptane gives the best BSFC. Tetralin-blended fuel has the highest volumetric energy but the worst BSFC, though its power performance is still high.  
Figure 4: Schematic diagram of the experimental setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Engine Load** & **25\%** & **50\%** & **75\%** & **100\%** \\ \hline Torque (N-m) @1700 rpm & 476 & 952 & 1428 & 1904 \\ Torque (N-m) @1800 rpm & 448 & 896 & 1344 & 1790 \\ Torque (N-m) @1900 rpm & 428 & 856 & 1284 & 1712 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Engine conditions.  
Except for the minor effect of fuel viscosity, the cyclic fuel injection quantity by volume can be considered the same at full load conditions. The volumetric LHV from high to low are tetralin, decalin, diesel, dodecane and heptane, so the addition of tetralin and decalin will increase engine output torque. As shown in Figure 5, when burning TE50 and DE50 at 1900 r/min, the engine torques increase by 33 N\(\cdot\)m and 41 N\(\cdot\)m, respectively. Additionally, the lower the engine speed, the more the torque.  
At the most interesting speed (rated speed) condition of 1900 r/min, the effect of the addition of hydrocarbons on BSFC is shown in Figure 6. The same trend exists in that the addition of straight-chain hydrocarbons, including heptane and dodecane, improve the fuel economy, while tetralin, representing aromatics, increases the engine's fuel consumption.  
As is shown in Figure 7, BSFC can be decreased by adding heptane and dodecane. Since the AI50 of n-heptane and n-dodecane is lower, the combustion phase is advanced, which, in turn, causes the reduction of BSFC. The lower viscosity and boiling temperature can also promote spray atomization, mixing with air and thus accelerating the combustion process [8; 21]. The addition of tetralin is the worst, as the combustion analyses show that the peak cylinder pressures are lower, while the peak heat release rates are high. This indicates that the decrease in CN delays the combustion process, which can be proved by the curves of the calculated cumulative heat release in Figures 8-11 in the next section.  
Figure 5: The power and economy performance at 100% load.  
Figure 6: Effective specific fuel consumption at 1900 r/min.
## 4 Results and Discussion  
### System Recommendation Effects  
Using a controlled experimental method, this study compares the proposed model with benchmark models, such as BERT, across different models, and quantitatively evaluates the recommendation effectiveness through precision and recall metrics in the TOP-N recommendation scenario. The precision and recall results for each model are shown in Figures 2 and 3.  
The comparative analysis of the data confirms that the model has the best performance in precision and recall. In the precision measurement chart, the precision of the model in this paper in the TOP-N tasks is above 90%. From the overall curve, when the number of samples increases, the recommendation accuracy of the model in this paper increases slightly, indicating that the path recommended by the model in this paper can effectively meet the learning needs of students. In contrast, the recommendation accuracy of BERT and RoBERTa is around 85%, and the increase in accuracy is almost unchanged under low and high sample numbers. When the number of samples is low, the recall rates of several modelsin the experiment are around 0.65. When the number of recommended students increases, the recall rate improvement of the model in this paper is more intuitive. GPT-4 considers the deep meaning of feature data when generating feature vectors and can extract features that have a more obvious impact on learning effects, highlighting the key directions of recommendations and increasing the accuracy of recommendations. In general, the system model in this paper performs well in both precision and recall, has strong comprehensive capabilities, and can provide students with efficient learning support.
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 1 Introduction
An analytical CEP expression in IEEE 802.15.4-based networks operating in the presence of AWGN, interference and multipath fading has been derived in [9]. In this paper, the performance of an IEEE 802.15.4 network operating under the impact of [PERSON] and Nakagami-\(m\) fading, in the presence of AWGN and interference, are considered. In order to reduce the impact of fading on the signal quality degradation, SC and MRC diversity techniques for reducing the impact of fading in the network were used. To the best of the authors' knowledge, there are no similar analyses or results presented in the available literature. The main contributions of this paper are:  
* Analytical expressions for the BER of the received signal in the IEEE 802.15.4 network operating under the impact of SC and MRC diversity, in the presence of Rayleigh and Nakagami-\(m\) fading, as well as AWGN and interference. * Numerical results are presented in the form of graphs, and the results are confirmed by Monte Carlo simulation. A comparative analysis of the SC and MRC combiner impact on the received signal quality is given. * Derived expressions are the basis for obtaining other system performance such as CEP, symbol error probability (SEP) and packet error probability (PEP). The organization of this paper is as follows: Section 2 presents the system model under consideration. Section 3 gives the BER calculation procedure for the case in which SC combiners are used to reduce the impact of Nakagami-\(m\) and Rayleigh fading on the system performance in the presence of AWGN and interference. Section 4 gives the BER calculation procedure for the case in which MRC combiners are used to reduce the impact of fading. Numerical results for different scenarios and parameters are presented in Section 5. The conclusion is in Section 6.
# Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method
## 2 Materials and Methods
### Mixed Collocation Form of the Monodomain Equation
#### 2.1.2 Boundary Conditions Imposition  
For the monodomain model, the domain \(\Omega\) is assumed to be isolated in the sense that no current can flow in or out of the boundary \(\partial\Omega\). To model electrical isolation, we enforce the Neumann boundary conditions (BCs) in mixed collocation using the penalty method described in [32]. From Equations (1) and (6), the Neumann BC imposition on the \(\gamma_{bc}\) nodes of the Neumann boundary \(\partial\Omega\) at a given time \(t\), where \(\gamma_{bc}\subset\{1,\cdots,N\}\), can be written in matrix form as follows:  
\[N_{bc}\mathbf{q}_{bc}=\mathbf{0}, \tag{14}\]  
where \(\mathbf{q}_{bc}\) is the vector collecting the transmembrane potential fluxes at \(\gamma_{bc}\) nodes and \(N_{bc}\) is the matrix containing the normal vectors given by  
\[N_{bc}=\begin{bmatrix}\mathbf{n}_{1}&&0\\ &\ddots&\\ 0&&\mathbf{n}_{\gamma_{bc}}\end{bmatrix} \tag{15}\]  
The Neumann BCs are enforced at the \(\gamma_{bc}\) nodes by multiplying Equation (14) with the penalty factor \(\alpha\mathbf{N}_{bc}^{T}\) and adding it to Equation (11) to obtain  
\[\mathbf{q}_{bc}+\alpha N_{bc}^{T}N_{bc}\mathbf{q}_{bc}=\mathbf{K}_{a}^{bc}\mathbf{V}^{bc}. \tag{16}\]  
By rearranging terms, Equation (16) can be written as  
\[\mathbf{q}_{bc}=\{\mathbf{I}+\alpha N_{bc}^{T}N_{bc}\}^{-1}\{\mathbf{K}_{a}^{bc}\mathbf{V}^{bc} \}=\mathbf{Q}^{-1}\{\mathbf{K}_{a}^{bc}\mathbf{V}^{bc}\}, \tag{17}\]where \(\mathbf{I}\) is the identity matrix and \(\mathbf{Q}=\mathbf{I}+\alpha\mathbf{N}_{bc}^{T}\mathbf{N}_{bc}\). Combining Equations (13) and (17), the matrix form of the monodomain model's diffusion term is given by  
\[\mathbf{M}\mathbf{V}+\mathbf{K}^{\prime}\mathbf{V}=\mathbf{0}, \tag{18}\]  
where \(\mathbf{K}^{\prime}=\left[\mathbf{K}_{\mathbf{s}}^{bc}\mathbf{Q}^{-1}\mathbf{K}_{\mathbf{a}}^{bc}\ \ \,\ \ \mathbf{K}_{\mathbf{s}}^{in}\mathbf{K}_{\mathbf{a}}^{in}\right]\). Here, the superscripts \(bc\) and \(in\) connote the row entries of the matrices \(\mathbf{K}_{\mathbf{s}}\) and \(\mathbf{K}_{\mathbf{a}}\) that correspond to the \(\gamma_{bc}\) nodes on \(\partial\Omega\) and the \(\gamma_{in}\) nodes in \(\Omega\), respectively, such that \(\gamma_{bc}\cup\gamma_{in}=\{1,\cdots,N\}\). The value of the penalty factor \(\alpha\) should be sufficiently large to ensure accurate imposition of the boundary condition. Instability issues may arise if \(\alpha\) is too large. In this study, we used \(\alpha=10^{6}\) as it was found to be the optimal value in [28].
## 5 Challenges and Future Directions
### Weather, Communication, and Path Planning  
The recent research explores solutions to improve UAV path planning and navigation in harsh weather conditions. Wind, rain, fog, and other extreme weather conditions can impair drone sensors, reduce visibility, and decrease endurance [209; 210]. To address these challenges, some studies propose risk assessment and path planning algorithms that avoid severe weather regions [211]. Other works apply numerical analyses using the Euler method to predict drone trajectories under wind loads [212]. Tilting drones based on the wind direction can minimize camera deviations when tracking ground objects. Since fog severely limits visibility, it also poses problems for camera-based obstacle avoidance [213]. Overall, the advances in drone design and path planning algorithms show promise for enabling successful navigations, despite precipitation, wind, fog, and other adverse weather.  
Collision avoidance in constantly changing environments has also posed challenges for UAVs' path planning [214]. Since complete environment information is unavailable before drone takeoff, unknown stationary or moving obstacles may hinder planned flight paths. One research area involves developing sensors, such as LiDAR and ultrasonic, to detect obstacles during flight [215]. Other research focuses on real-time obstacle avoidance by incorporating sensor data. For instance, [216] proposed using a Gaussian process occupancy map to predict the collision probabilities along a path using collected sensor data. The algorithm then selected the potential waypoints for the path generation. More recently, learning-based algorithms were suggested for navigation in dynamic environments [217; 218]. By learning from past drone actions, these techniques can generate paths without prior knowledge of the surrounding environment. Coordinating multiple drones without inter-drone collisions in unknown environments also emerged as a challenge. Lastly, integrating UAVs with AI and IoT technologies presents both opportunities and challenges. While AI can enhance the capabilities of UAVs in data processing and analysis, it also introduces challenges related to data privacy, security, and real-time decision making. Integrating UAVs with IoT systems requires robust communication networks and protocols to ensure seamless data transmission and integration [219]. Overcoming these technical challenges is essential for maximizing the potential of UAVs in climate change research in cities.
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 4 Experimental Results and Discussion
### Dataset Preparation  
This article used the RealSenseD435I depth camera to capture and search online to create a total of 5213 original photos of small target fruits, including apples, bananas, and oranges. Each photo contained a random number of experimental fruits.  
The rotating boxes were annotated with the LabelImg tool, resulting in a custom dataset formatted similarly to COCO. This dataset was divided into training, validation, and testing subsets following a 7:2:1 ratio, respectively. To facilitate a comparative analysis, both the YOLOv8 standard model and the enhanced model proposed in this paper were trained using these specified training, validation, and test sets.
# Multi-Objective Optimization of Cascade Blade Profile Based on Reinforcement Learning
## 5 DDPG-Based Optimization for Blade Profile
### Application of DDPG and Optimization
The environment of the DDPG network in this paper is the ANN-based surrogate model. With the given state \(s_{t}\) and action \(a_{t}\) (the profile geometry and geometric change), the next step state \(s_{t+1}\) (updated geometry) can be obtained. The aerodynamic parameters according to the state and action can be predicted with the surrogate model, to calculate the reward function \(r_{t}\). The reward function represents our actual evaluation of an action. It is used to adjust the critic network to output value function closer to reality. Since a blade profile is expected with lower total pressure loss coefficient, larger laminar flow area, and as small as possible change in outlet flow angle, the reward function can be expressed as:  
\[ \begin{split} r&=(\varpi^{0}-\varpi)*w_{0}-\max(0, \ (\widetilde{A}_{SS}^{0}*1.15-\widetilde{A}_{SS}))*w_{1}\\ &\quad-\max(0,\ (\widetilde{A}_{PS}^{0}*1.15-\widetilde{A}_{PS}))*w_{2}- \sum_{i}\phi_{i}*w_{3},\end{split}  \tag{17}\]  
where \(\phi=\max(0,\ \beta_{2}-\beta_{2}^{+})+\max(0,\ \beta_{2}^{-}-\beta_{2})\), and \(\beta_{2}^{+}\) and \(\beta_{2}^{-}\) are boundaries to constrain \(\beta_{2}\) of different \(\beta_{1}\). \(\varpi^{0}\), \(\widetilde{A}_{SS}^{0}\), and \(\widetilde{A}_{PS}^{0}\) represent the initial profile's \(\overline{\omega}\), \(\widetilde{A}_{SS}\), and \(\widetilde{A}_{PS}\). According to the PCA, the weights in Equation (17) are: \(w_{0}=100,w_{1}=3,w_{2}=1.5,w_{3}=1/3\). The target of the optimization is to achieve a blade profile with reduced total pressure loss and improved laminar flow area. During the training process of the DDPG network, huge numbers of states (profiles) interact with the environment, many of which have good performance. Each time the reward value of one state is calculated, the aerodynamic parameters of the blade profile of the state are predicted by the surrogate models. The state (the geometric parameters) of the blade profile with the smaller objective function is stored in the training process of the DDPG network to optimize the blade profile. Compared with the initial blade profile, the optimized blade profile should have as small total pressure loss coefficient as possible, enough large laminar flow area, and enough small change in outlet flow angle, so the objective function of the optimization can be expressed as:  
\[ \begin{split} loss&=\overline{\omega}*w_{0}+\max(0,\ (\widetilde{A}_{SS}^{0}*1.15-\widetilde{A}_{SS}))*w_{1}\\ &\quad+\max(0,\ (\widetilde{A}_{PS}^{0}*1.15-\widetilde{A}_{PS}))*w_{2},\end{split}  \tag{18}\]  
with a constraint on \(\beta_{2}\). The weights are: \(w_{0}=1,w_{1}=0.15,w_{2}=0.075\). With the optimization policy from DDPG, the optimizer can approach the good-performance profile (high reward state) with not many iterations of geometric modification (action).
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 7 Conclusions
### Application of Developed SDN Architecture as a Case Study  
An application of the optimized design is investigated in a hypothesized scenario whereby a prosumer and smart microgrid are in Cape Town. The typical SDN architecture evaluated the data path availability with the controller located in Johannesburg. The optimized SDN architecture design was applied by decentralizing the SDN controller, adding a redundant network connection to each SDN node and changing the network layout to a mesh topology configuration with automated failover. As a result, the data path availability improved by 0.27%, which resulted in a data path downtime reduction of 117 min per month.
# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 4 Experimental Result
### Proposed Model Architecture and Space Complexity  
We calculated the complexity of this CNN model by analyzing both the parameter count and the number of floating-point operations (FLOPs) as shown in Table 19. The parameter count is determined by summing the weights and biases in each layer, including Conv1D, Dense, and batch normalization layers. The FLOPs are computed based on the number of multiplications and additions required by the convolutional and Dense layers to process the input data. By aggregating these metrics, we assessed the model's computational and memory requirements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Benign & 1 & 1 & 1 & 20078 \\ Bot & 1 & 1 & 1 & 19922 \\ \hline \hline \end{tabular}
\end{table}
Table 17: The evaluation performance of scenario 7 in the proposed model per category.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Results** \\ \hline Accuracy & 1 \\ Precision & 1 \\ Recall & 1 \\ F1-Score & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Overall classification results of scenario 7 in the proposed model.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Layer** & **Output Shape** & **Parameters** \\ \hline Conv1D & 76 \(\times\) 64 & 448 \\ Batch normalization & 76 \(\times\) 64 & 256 \\ MaxPooling1D & 38 \(\times\) 64 & 0 \\ Conv1D & 38 \(\times\) 64 & 24,640 \\ Batch normalization & 38 \(\times\) 64 & 256 \\ MaxPooling1D & 19 \(\times\) 64 & 0 \\ Conv1D & 19 \(\times\) 64 & 24,640 \\ Batch normalization & 19 \(\times\) 64 & 256 \\ MaxPooling1D & 10 \(\times\) 64 & 0 \\ Flatten & 1 \(\times\) 640 & 0 \\ Dense & 1 \(\times\) 64 & 41,024 \\ Dropout & 1 \(\times\) 64 & 0 \\ Dense & 1 \(\times\) 64 & 4160 \\ Dense & 1 \(\times\) 2 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Proposed model architecture and space complexity.  
The CNN model has a moderate complexity with 95.810 parameters and 559.507 FLOPs, striking a balance between computational efficiency and learning capacity. Its architecture includes three Conv1D layers, each followed by batch normalization and max pooling, reducing the spatial dimensions while preserving essential features. The model's parameter count is concentrated in the Conv1D and Dense layers, with a total memory footprint of 374.26 KB, making it suitable for deployment on devices with limited resources. This design ensures effective training and inference for sequential data tasks while maintaining manageable computational and memory requirements.