esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Phase 3  
In the age of digital technology, devices are commonly interconnected, which facilitates data exchange and simplifies communication for users. This connection introduces various security risks to user and business data [60]. Among these risks, malware attacks, particularly ransomware, pose significant threats by encrypting user data, and often a ransom is demanded for decryption [11; 73]. However, paying the ransom does not guarantee data recovery [74]. Detecting ransomware early is crucial to mitigating its impact. Prior studies [10; 75; 16] emphasize the importance of early detection using machine learning to identify ransomware before encryption.  
Deep learning has emerged as a powerful tool in malware detection, which improves the traditional signature-based methods. Studies highlight the advantages of deep learning  
\begin{table}
\begin{tabular}{l|l|c} \hline
**Type** & **Features** & **Rank** \\ \hline  & CryptEncrypt & 1 \\
**Crypto APIs** & CryptGenKey & 3 \\  & CryptDestroyKey & 6 \\  & BCryptGenRandom & 9 \\ \hline  & CreateFile & 2 \\
**File access APIs** & FindFirstFileEXA & 5 \\  & FindNextFileA & 8 \\  & DeleteFile & 10 \\ \hline
**Network APIs** & WinHttpConnect & 4 \\  & WinHttpOpenRequest & 7 \\ \hline \end{tabular}
\end{table}
Table 6: The 10 most prevalent API calls utilized by ransomware, categorized by functionality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Technique**} & **Per-Call Execution** & **Per-Call Execution** & **Train Time (min)** \\  & **Time (s)** & **Time (min)** & **Train Time (min)** \\ \hline IMIS & 0.01 s & 3.5 m & 19 m \\ RCGU & 0.054 s & 10.8 m & 33 m \\ EMRMRR & 0.063 s & 12.6 m & 37 m \\ MIFS & 0.03 s & 6.0 m & 28 m \\ JMI & 0.07 s & 4.0 m & 24 m \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparative performance evaluation of the proposed IMIS and related techniques.  
in IoT security [76], Android malware defense [77], and dynamic malware analysis [78; 79]. The adaptability of deep learning allows for quick analysis of attack patterns and improves detection and prevention [80; 81; 82; 83]. However, overfitting remains a challenge, and early stopping mechanisms can help mitigate it by pausing training before performance goes down [84].  
Existing early stopping techniques primarily rely on validation accuracy [85; 86; 87], but static criteria may lead to underfitting or missed opportunities for better generalization [88]. Dynamic stopping approaches address these limitations by adapting to the training differences [89]. However, current methods often fail to integrate uncertainty and variability, which can lead to the most suitable stopping decisions. Due to the lack of ransomware datasets, our framework contains regularization techniques, data augmentation, and active learning to enhance model performance.
# Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings
## 5 Conclusions  
From the above discussion, the following conclusions can be drawn:  
1. The vibration bars are high for all rotating speeds, and vibration data are scattered for each damaged bearing for both of the loading conditions--without installing a bearing loader and with installing a bearing loader--due to the fact that the damaged bearing produces high vibrations, as it has high damage energy.
2. The kurtosis values are also high for each damaged bearing and all rotating speeds.
3. The vibration data for an outer race damaged bearing are more scattered as compared to the intact bearing, since the width and height of the bars for an outer race damaged bearing are greater.
4. For some data, without installing the bearing loader, the bars are not prominent for a damaged bearing due to the fact that vibrations generated at a smaller load have lower amplitudes.
5. Due to the defect, damaged bearings produce greater vibrations, resulting in high bars in histograms and showing that the damage energy can be a good indicator to detect faults in roller bearings.
6. As the kurtosis values give a better idea of vibration energy at damage, thus, the histograms and kurtosis values revealed that the energy at damage-based approach is an efficient way to detect damages in the rolling bearing.
7. The comparison of all kurtosis values for all types of damages, without installing a bearing loader and with installing a bearing loader, revealed that the kurtosis values for all types of damages are high as compared to intact bearings, and the results regarding the condition of without installing a bearing loader provide a better understanding of the concept of the vibration energy at damage-based statistical approach.
8. These results are based on the simulator; the real bearing fault detection could be more difficult due to the noise coming from different devices. However, this novel methodology can be helpful in detecting damages at early stages.  
Conceptualization, N.A.; data curation, N.A.; formal analysis, N.A. and A.K.; funding acquisition, X.Y.; investigation, X.Y.; methodology, N.A. and A.K.; project administration, X.Y. and A.K.; resources, X.Y.; software, N.A., A.K. and J.J.; supervision, X.Y. and A.K.; validation, N.A.; writing--original draft, N.A.; writing--review and editing, N.A., X.Y., A.K. and J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51105316) and the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2018)M5107).  
Not applicable.  
It is declared that the authors have all the raw data in excel files, and data will be provided upon request.  
The authors declare no conflict of interest.
## 2 Background
### Sparse Storage Schemes
#### 2.1.2 Compressed Sparse Row (CSR)  
The compressed sparse row (CSR) format like the COO format stores the column indices and nonzero values in arrays named _col_ and _val_ explicitly. However, a third array of row pointers, _ptr_, is used to store the row pointers. For an \(m\times n\) matrix, _ptr_ has length \(m+1\) and stores the offset into the \(i\)th row in \(ptr[i]\). The last entry in \(ptr\), which would otherwise correspond to the \((m+1)\)-st row, stores nnz, the number of nonzeros in the matrix. Figure 3 illustrates the CSR representation of an example matrix. SpMV for CSR based matrix can be performed using two techniques, CSR scalar and CSR vector [58]. In CSR scalar, one thread is assigned per row for SpMV operation. Each thread will compute the products and sum up the products for each row. However, due to workload imbalance and non-coalesced memory access, CSR scalar has poor performance. CSR vector is an improvement to CSR scalar, in which a warp (32 threads) is assigned to a row to perform SpMV. However, variance in the number of non-zero elements per row results in idle threads, which causes load imbalance, which leads to poor performance.
# Analysis, Evaluation and Simulation of Railway
## Materials and Methods
### Energy Storage Technologies  
To characterize energy storage (ESS) technologies, it is usual to determine the energy to weight ratio (or the energy to volume ratio), that is also called energy density, but in traction applications is also significant the power density of the storage system (or the power to weight ratio) because locomotives demand high power peaks when the train accelerates.  
Large energy density ratios mean that the ESS is capable of supplying power demand during long periods, while large power density ratios translate in the ability to supply high levels of power pulses in a short time.  
Figure 6 presents a classification of different storage technologies depending on both parameters: Energy and power density.  
It is necessary to consider the trend rather than the absolute levels, as there could be discrepancies between different published data, because of the interdependency of both parameters and the publishing date, since these systems are currently being developed, and consequently, figures of performance could have improved.  
Although there are several energy storage technologies, the most common ESS employed in railways applications are supercapacitors, flywheels and batteries. Batteries have a high energy density, but a low power density (a problem from the point of view of energy needs when the train accelerates), while supercapacitors have a high power density, but a low energy density (Figure 6). Flywheels have higher power density than batteries and higher energy density than supercapacitors, but cost is an important drawback. Other important factors to take into account are shown in Table 6.  
In order to choose what technology is the most appropriate for each railway application, it is necessary to consider not only their characteristics, but also the place where the energy storage system  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Storage** & **Specific** & **Specific** & **Energy** & **Price** & **Self-Discharge** & **Lifespan** \\
**System** & **Energy** & **Power** & **Density** & **(f/kWh)** & **(\%/day)** & **(Cycles)** \\ \hline Li-ion & 100–250 & 230–340 & 200–620 & 200–1800 & 0.1 & \(4\times 10^{3}\)–\(4\times 10^{4}\) \\ Batteries & 0.1–15 & 0.1–10 & 10–25 & 300–4000 & 2 & \(>\)5\(\times 10^{5}\) \\ Supercapacitors & 5–130 & 400–1600 & 20–80 & 1000–3500 & 20 & \(10^{5}\)–\(10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Characteristics of ESS technologies most applied in transportation [(63)].  
Figure 6: Classification of Energy Storage Systems (ESS) according to data available in references [(61; 62)].  
is going to be installed. There are two possibilities for placing ESS, on board or wayside (normally in electrical substations) as stationary systems.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Ad Hoc On-Demand Distance Vector (AODV)  
An ad hoc on-demand distance vector (AODV) is a routing algorithm with a traditional routing table based on the concept of the distance vector algorithm [50]. The node is deleted from the table if the network is not completed within a certain amount of time. All data and communications from neighbors are carried via the supplier and intermediary nodes. For connectivity, a control message is forwarded, and a path is identified by delivering a route request (RREQ) message.
## 3 Numerical Model Validation  
This section presents the results of a comparison between the numerical model used in this study and the experimental data from [PERSON] and [PERSON] (2020) [3] to validate the applied computational model. Figure 1 shows the dimensions of the 3D rectangular tank used in the experiment. The partially filled tank undergoes sinusoidal surge motion as described by Equation (1), induced by a shaker, and the free surface displacement is measured by a wave gauge positioned 0.02 m away from the wall.  
When the water height is 0.15 m, the lowest natural sloshing frequency is 7.085 rad/s. For the validation test, the excitation frequency is set to \(\omega=\omega_{0}\), and the excitation amplitude is A = 0.005 m.  
Figure 2 shows the schematic diagram of the finite element model used for the validation test. The numerical simulations were performed using the CEL module in the commercial software ABAQUS 2024. The tank is modeled using Lagrangian shell elements, with its deformation neglected. The water is modeled using Eulerian solid elements, while the regions outside the water are assigned to the void material option. For water simulation, the Us-Up EOS is applied. The EOS parameters implemented for the water are provided in Table 1.  
Figure 1: Validation test model.  
Figure 2: Schematic diagram of the validation test model.  
A mesh sensitivity study was conducted along with the validation test. The size of the Eulerian elements significantly affects the overall computation time, so simulations were performed with element sizes ranging from 3 mm to 7 mm to determine the optimized element size. Figure 3 shows snapshots of the free surface profile for each element size.  
Figure 4 presents the results of the validation test. The simulation was run for approximately 6 s, with excitation applied at the calculated lowest natural sloshing frequency, enabling the observation of a clear resonant phenomenon. The results indicate that as the element size decreased, the simulation trend became more consistent with the experimental data, with convergence observed for element sizes smaller than 4 mm. To ensure higher accuracy, an element size of 3 mm was ultimately selected for this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Density (\(\rho_{\text{w}}\))** & **Dynamic Viscosity** & **Speed of Sound (co)** & \\
**[kg/m\({}^{3}\)]** & **(n) [N-s/m\({}^{2}\)]** & **[m/s]** & **\(\Gamma_{0}\)** & **s** \\ \hline
1000 & 0.001 & 1450 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main particular and loading condition of the target structure.  
Figure 3: Snapshots of free surface profile during sloshing according to different element size.
## 4 Experiments and Results
### Proposed Model Construction
#### 4.4.1 Analysis of Model Architectures  
We explored the many-to-many LSTM model in three deferment architectures: Stack bi-directional LSTM (SBLSTM), feedforward followed by bi-directional LSTMs (FF-BLSTM), and bi-directional LSTM followed by feedforward (BLSTM-FF), as shown in Figure 4. The structure of the SBLSTM as presented in prevalent work was used [12; 56]. It employed only the BLSTM as the hidden layer. The FF-BLSTM and BLSTM are hybrids of the feedforward and BLSTM layers, respectively. The FF-BLSTM has lower hidden layers with a feedforward structure that is cascaded with the upper hidden layers through a BLSTM adopted from work on text-to-speech systems [11]. The BLSTM-FF uses the lower layer as a BLSTM and the upper layer as a feedforward structure. All models used linear activation in the output layer. We trained the models with the learning rate of 0.0001, a mini-batch size of 64. The best model for each architecture can be summarized as follows:* SBLSTM: Three hidden layers of BLSTMs, each comprising a forward and a backward layer with 64 units;
* FF-BLSTM: Two feedforward layers with 256 units per layer, where the top layer consisted of three BLSTM layers, each of which in turn consisted of 128 forward and backward units;
* BLSTM-FF: Two BLSTM layers, each consisting of 64 forward and backward units, where the top layer consisted of two feedforward layers with 512 units per layer.  
The RMSE and CORR of the best model in each network architecture are shown in Table 4. Note that the voiced/unvoiced flags were obtained from the baseline RNN-based speech synthesizer, the details of implementation of which are described in Section 4.5. As shown in Table 4, the number of model parameters of the models are quite different because we varied the number of hidden layers and units and selected the best performance for each network architecture. Table 4 shows that the SBLSTM architecture delivered the poorest performance, while the FF-BLSTM and BLSTM-FF architectures performed similarly. This indicates that using hybrid feedforward and LSTM layers could improve model performance. We selected the FF-BLSTM for the next evaluation.
## 4 Description of the Proposed Topology Construction
### Design Issues of the Proposed Logical Topology
#### 4.5.4 Inserting Additional Nodes into the Network  
Additional nodes may be inserted into the network at any time. Before a node is inserted, the BS records and stores its unique ID and will insert the node into a nearby chain with the least number of nodes. This helps to minimise the chance of a chain monopolising a certain bandwidth if it contains a greater number of nodes than other chains that are communicating. The node will then organise itself within its chain.
_Applied_  
_sciences_  
_Article_  
**Providing a Visual Understanding of Holography**  
**Through Phase Space Representations**  
**[PERSON] 1,2,*(), [PERSON] 3() and [PERSON] 1,2()**  
1 Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels B-1050, Belgium; [EMAIL_ADDRESS]  
2 imec, Department of Digital & User Centric Solutions (DUCS), B-3001 Leuven, Belgium  
3 Department of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 28 May 2020; Accepted: 6 July 2020; Published: 10 July 2020  
**Featured Application: Visually interpretable phase space representations can aid the development of new methodologies in digital holography in general and can be evaluated for any digital hologram. Concrete applications presented in the paper are: coarse scene depth estimation, scene analysis, and digital hologram (processing) artifact analysis.**  
**Abstract:** Digital holograms are a prime example for signals, which are best understood in phase space--the joint space of spatial coordinates and spatial frequencies. Many characteristics, as well as optical operations can be visualized therein with so called phase space representations (PSRs). However, literature relies often only on symbolic PSRs or on, in practice, visually insufficient PSRs like the Wigner-Ville representation. In this tutorial-style paper, we will showcase the S-method, which is both a PSR that can be calculated directly from any given signal, and that allows for a clear visual interpretation. We will highlight the power of space-frequency analysis in digital holography, explain why this specific PSR is recommended, discuss a broad range of basic operations, and briefly overview several interesting practical questions in digital holography.  
**Dataset:** www.erc-interfere.eu  
**Dataset License:** CC-BY-NC-SA  
**Keywords:** digital holography; phase space representation; time-frequency representation; Wigner-Ville; spectrogram; S-method
###### Abstract  
Based on the three-parameter approximate JCAL analytical model (hereinafter referred to as the three-parameter model), this study conducted an in-depth analysis of the effects of porosity, median pore size, and pore size standard deviation on the acoustic performance of porous materials and developed a composite porous material composed of glass fibers and zeolite particles. Experimental results indicate that the pore size distribution significantly affects the acoustic performance of fibrous porous sound-absorbing materials. Specifically, smaller pores lead to better sound absorption at mid-low frequencies, with the optimal sound absorption performance observed when the median pore size is between 60 and 80 \(\upmu\)m. Increasing the material density and decreasing the fiber diameter help reduce the internal pore size, thereby improving the material's sound absorption performance. Additionally, the appropriate addition of zeolite can further optimize the internal pore size and effective sound-absorbing interface, thus enhancing the material's sound absorption performance. When the material density is 120 kg/m\({}^{3}\) and the zeolite substitution rate is around 10%, the material exhibits the best acoustic performance, with a noise reduction coefficient (NRC) reaching 0.65, which is a 10.17% increase compared to the material without zeolite. Comparing the simulation data from the three-parameter model with the actual measurement data shows that the model has excellent predictive performance for the sound absorption coefficient (SAC) of single-fiber porous materials (with an error of approximately 5%). However, for composite porous materials, due to the complex changes in interfaces, there is a certain prediction error (with the maximum error reaching 12.81%), indicating that the model needs further optimization and correction when applied to composite materials.  
composite materials; sound absorption; pore size distribution; fiber diameter; inorganic particles +
Footnote †: journal: Applied sciences
## 2 Mathematical Model Descriptions
### The Lagrange Expression
Substituting Equations (11)-(17) in Equations (1) and (2), in terms of the system model in Figure 1 the kinetic energy \(G_{r1}\) of the primary shaft is given as:  
\[ \begin{array}{l}G_{r1}=\frac{1}{2}\mu_{\rm{M}}\hat{\theta}_{1}^{2}+\frac{1} {2}\mu_{\rm{D}}\left(\hat{\theta}_{1}+\dot{\psi}_{1}\right)^{2}+m_{1u}e_{1}^{2 }\dot{\theta}_{1}\dot{\psi}_{1}+\frac{1}{2}m_{1u}e_{1}^{2}\dot{\psi}_{1}^{2}+ \frac{1}{2}m_{1u}e_{1}^{2}\dot{\theta}_{1}^{2}(1+\dot{\psi}_{1}^{2})\\ +\frac{1}{2}(M_{1}+m_{1u})\left[\hat{X}_{1}^{2}+\dot{Y}_{1}^{2}\right]+m_{1u} \dot{Y}_{1}\dot{\psi}_{1}(e_{1x1}\cos\theta_{1}-e_{1y1}\sin\theta_{1})\\ -m_{1u}\dot{X}_{1}\dot{\theta}_{1}\left[\left(e_{1x1}-\dot{\psi}_{1}e_{1y1} \right)\sin\theta_{1}+(e_{1x1}\dot{\psi}_{1}+e_{1y1})\cos\theta_{1}\right]\\ +m_{1u}\dot{Y}_{1}\dot{\theta}_{1}\left[\left(e_{1x1}-\dot{\psi}_{1}e_{1y1} \right)\cos\theta_{1}-(e_{1x1}\dot{\psi}_{1}+e_{1y1})\sin\theta_{1}\right]-m_ {1u}\dot{X}_{1}\dot{\psi}_{1}(e_{1x1}\sin\theta_{1}+e_{1y1}\cos\theta_{1})\\ \end{array}  \tag{18}\]  
The kinetic energy \(G_{r2}\) of the secondary shaft is constituted as:  
\[ \begin{array}{l}G_{r2}=\frac{1}{2}I_{D2}\left(\hat{\theta}_{2}+\dot{\psi}_{ 2}\right)^{2}+\frac{1}{2}m_{2u}e_{2}^{2}(1+\dot{\psi}_{2}^{2})+\frac{1}{2}m_{ 2u}e_{2}^{2}\dot{\psi}_{2}^{2}+m_{2u}e_{2}^{2}\dot{\theta}_{2}\dot{\psi}_{2}\\ -m_{2u}\dot{X}_{2}\dot{\theta}_{2}\left[\left(e_{2x2}\dot{\psi}_{2}+e_{2y2} \right)\cos\theta_{2}+\left(e_{2x2}-\dot{\psi}_{2}e_{2y2}\right)\sin\theta_{2 }\right]+\\ +m_{2u}\dot{Y}_{2}\dot{\theta}_{2}\left[\left(e_{2x2}-\dot{\psi}_{2}e_{2y2} \right)\cos\theta_{2}-\left(e_{2x2}\dot{\psi}_{2}+e_{2y2}\right)\sin\theta_{2 }\right]+\frac{1}{2}(M_{2}+m_{2u})\left[\dot{X}_{2}^{2}+\dot{Y}_{2}^{2}\right]\\ -m_{2u}\dot{X}_{2}\dot{\psi}_{2}\left(e_{2x2}\sin\theta_{2}+e_{2y2}\cos\theta _{2}\right)+m_{2u}\dot{Y}_{2}\dot{\psi}_{2}\left(e_{2x2}\cos\theta_{2}-e_{2y2 }\sin\theta_{2}\right)\\ \end{array}  \tag{19}\]  
The kinetic energy of the system comprises the kinetic energy of the primary shaft \(G_{r1}\) and secondary shaft \(G_{r2}\):  
\[G_{Total}=G_{r1}+G_{r2} \tag{20}\]