esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset
## 3 Methodology
### Android Malware Detection Based on Deep Learning
In Algorithm 3, we describe the general process of our classifier generation phases namely, data processing (lines 1-3), model building (line 4), and model fitting (line 5) as well as using the model for prediction (lines 6-13). For the complexity discussion, we focus on the prediction phase because once the model is trained, it can be reused as much as there is a need. The time and space complexity of the model's prediction phase is \(O(p\times n_{l1}+...+n_{li-1}\times n_{li}+...+n_{ln-1}\times o)\), where \(p\) is the number of features, \(n_{li}\) is the number of neurons at layer \(i\) in a neural network, and \(o\) is the number of outputs. Therefore, the complexity is asymptotically quadratic, \(O(\max(n_{li-1}\times n_{li}))\), in the size of the network layers. The architecture and parameters of our DL model were determined experimentally and tuned for best performance; they are described in Table 2 and Figure 6.  
```
Input:\(X:apps\_features,Y:labels\)\(\triangleright\) label is benign or malware \(Building\_params_{list}=(units,activation\_function,input\_dim,dpoint)\)\(fitting\_params_{list}=(X_{train},Y_{train},batch\_size,epochs)\)\(dpoint:decision\_point\)\(\triangleright\) 0.5 by default Output:\(predicted\_app\)\(\triangleright\) benign or malware app
1\(X_{train},X_{test},Y_{train},Y_{test}\)\(\triangleright\) Splitting the dataset
2\(X_{train}=sc.fit_{transform}(X_{train})\)\(\triangleright\)Feature Scaling using StandardScaler (sc)
3\(X_{test}=sc.transform}(X_{test})\)\(Model=build\_ANN\_model\_architecture(Building\_params)\)\(\triangleright\)ANN model Building
4\(Model\gets ANN\_model\_fit(fitting\_params)\)\(\triangleright\)ANN model fitting
5\(y_{pred}=Model.predict(X_{test})\)
6 for\(app.pred\in y_{pred}\)do
7 if\((app.pred>dpoint)\)then
8\(app\gets Malware\);
9
10 else
11\(app\gets Benign\);
12
13 end if
14
15 end for
```  
**Algorithm 3**Deep Learning based Model. Figure 5: The architecture of DL layers using Sequential neural network.
## Materials and Methods  
### Fabrication of Solid Samples  
Optimization Procedure for the SLM Samples  
A set of 26 cubic samples (Figure 2) with a side length of 5 mm was fabricated in one batch on a Realizer SLM50 machine. Samples with energy density delivered to a consolidated material progressively increasing and then decreasing from 60-120 J/mm\({}^{3}\) were prepared. The laser pattern used for sample fabrication was alternating (red arrows in Figure 3) with a 90\({}^{\circ}\) rotation for every new layer. Samples were prepared using a constant scanning speed of 0.5 m/s (Samples I-XII: exposure time of 40 \(\upmu\)s and point distance of 20 \(\upmu\)m) and 0.3 m/s (Samples XIII-XXVI: exposure time of 60 \(\upmu\)s and point distance of 20 \(\upmu\)m). Laser power was varied between 50 and 120 W, and the hatch distance was varied between 35 and 95 \(\upmu\)m. Parameters used for samples' fabrication are summarized in the Supplementary Material. Based on the density results measured by the Archimedes method, the parameters from the sample XI were chosen for the fabrication of the cubic sample with an edge length of 15 mm (summarized in Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Realizer SLM50** & **Arcam EBM S12** \\ \hline Beam power (W) & 110 & 50–3500 \({}^{*}\) \\ Layer thickness (\(\upmu\)m) & 50 & 50 \\ Scan speed (m/s) & 0.5 & 0.5 \\ Atmosphere & 0.4–0.6 vol. O\({}_{2}\) & Vacuum 10\({}^{-4}\)–10\({}^{-5}\) mbar \\ \hline \end{tabular} \({}^{*}\) For EBM, this parameter is continuously variable during the process.  
\end{table}
Table 2: SLM and EBM (Electron Beam Melting) process parameters used for samples with a 15-mm side length.  
Figure 2: Set of 26 cubic samples prepared for process parameters optimization on a Realizer SLM50 machine. Samples’ numbering and energy densities (J/mm\({}^{3}\)) delivered to the material (left side). Samples’ top surfaces after fabrication (right side).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Powder Supplier**} & \multicolumn{8}{c}{**Percentage by Weight**} \\ \cline{3-10}  & & **Al** & **V** & **O** & **C** & **N** & **H** & **Other Max.** & **Ti** \\ \hline SLM & AP\&C & 5.50–6.75 & 3.50–4.50 & 0.12–0.15 & 0.02 & 0.02 & 0.005 & 0.65 & bal. \\ EBM & ARCAM & 6.00 & 4.00 & 0.15 & 0.03 & 0.01 & 0.003 & 0.10 & bal. \\ \hline \end{tabular} \({}^{*}\) According to the manufacturer’s data.  
\end{table}
Table 3: Chemical composition \({}^{*}\) of stock Ti-6 Al-4V Grade 5 powders.
## 3 Results and Discussion
### Inlet Mass Flow  
The studied efficiencies of the PVT panels under the parametric analysis that vary the mass flow of the working fluid in the hybrid panels are graphically represented in Figure 9. The reduction of the mass inlet flow under nominal flow supposes an overheating of the PV cells and a reduction in the electrical efficiency of the panels. Electrical efficiency is 15.2% and 15.7%, with a working mass flow of 1 and 5 g/s, respectively. However, this overheating positively affects the TEG modules' efficiency because of the indifference of temperatures between cold and hot sides of them. In particular, the overall electrical efficiency of a PVT + TEG panel is 1.1% higher compared to a PVT panel with a circulating flow of 1 g/s. Despite the overheating produced in the interior of the hybrid panel with the reduction of the mass flow, thermal efficiency does not show an improvement.  
Figure 8: Thermal and electrical efficiency of the studied hybrid panels with the ambient temperature variation.  
In global efficiency terms, an optimal working condition exists when the mass flow has a value of 18 L/h. This parameter is lower than the nominal flow given by the manufacturer. In this situation, the global efficiency of the conventional PVT panel is 81.9%, and the global efficiency of PVT+TEG panel is 82.2%.
## 8 AI Applications in Smart Grids
### Load Forecasting in Smart Grids  
Artificial intelligence dramatically improves load forecasting in smart grids. The system uses advanced machine learning algorithms to examine past electricity usage data, weather trends, economic factors, and consumer habits to forecast future electricity demand. The precision of these forecasts is essential for maximizing the efficiency of electricity generation and distribution. AI aids in effectively allocating resources, reducing wastage, and preparing for situations of high demand. In addition, AI-driven forecasting tools such as neural networks excel at adjusting to evolving consumption patterns and seasonal fluctuations. Adaptability ensures that the forecasts remain dependable and precise, assisting in smart grids' strategic planning and operational effectiveness [128].  
Figure 7: AI applications for smart grids [155].
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.13 Message Loss Feedback based  
Congestion Control Scheme using Message Loss Feedback: A rate-based congestion control scheme was proposed in [29] to tackle the problem of congestion detection in default CoAP. For default CoAP, at least 2 out of 16 messages must be transmitted as CON messages for detection of congestion. To overcome this problem and detect the congestion in case of unreliable transmission as well, the proposed method defines a 1-bit field in the message header as 'CS field'. The field value is either 0 or 1 depending on the type of transmitted message as either CON or NON respectively. For every transmission, the packet records the CS field in a 'CS_list_S' which is a list that holds the values of CS field for sender. On reception of a message, the receiver node updates the same list on receiver side called 'CS_list_R'. The receiver list is then sent back to the sender with ACK in case of CON message. However, for the case of NON message loss, the number of packet loss is mentioned in the receiver side list i.e., CS_list_R and sends it to the sender in the next ACK message. The sender node compares the value in its list upon reception of ACK and subtracts it to find the packet loss number. This packet loss number is used to find the current transmission rate which in turn calculates the congestion value. In this way the proposed method detects congestion in network.  
For congestion control, the method varies the transmission rate instead of congestion window size. It uses a token bucket mechanism for congestion control with the assumption that bucket size is not limited. When there is a data packet to be sent, it checks the remaining token size and transmits only if the token size is bigger than data size, otherwise it waits until a token with bigger size is created. The proposed method varies the packet transmission rate according to network congestion by adjusting the token generation rate. Although the approach performs better in terms of transmission success rate and throughput; however, it does not consider the scenario when number of CON and NON messages will be equal. In such case the loss rate of transmission cannot be found for CON messages.
## 7 Summary  
In this paper, a holistic approach is taken to isolate flywheel vibrations by considering the flywheel mounting plate and flywheel isolator as a system. Through finite element simulations and a 2-stage simplified model, it is demonstrated that achieving ideal flywheel vibration isolation is challenging when relying solely on the flywheel isolator due to the influence of the structural plate's elasticity. To address this issue, the elastic boundary is applied for the flywheel-mounted structural plate instead of a common rigidly fixed boundary. The stiffness and damping of the plate boundary are optimized using a simplified 3-stage model, and the optimized results are verified through finite element simulations of the plate-isolator system. The simulation results indicate that the influence of high-order modes of the plate is effectively suppressed, and the overall force transmissibility of the plate-isolator system is comparable to that of a 1-DOF isolator. Finally, the idea of an elastic plate boundary is further verified by the finite element simulation of a simple 1.5 m\({}^{3}\) cube satellite. The flywheels are mounted on the bottom plate of the satellite, and the kinetic responses of the satellite structure are predicted when the bottom plate is elastically or rigidly connected to the satellite's side plates. The dynamic responses of the cube satellite confirm the effectiveness of the elastic boundary of the flywheel-mounted plate in suppressing the vibration of the satellite's structural plates away from the flywheel.  
**Author Contributions:** Conceptualization, X.Z.; Methodology, X.K.; Software, H.L.; Validation, H.L.; Formal analysis, X.K.; Investigation, X.K. and H.L.; Data curation, X.X.; Writing--original draft, X.K. and X.S.; Writing--review & editing, X.Z. and X.S.; Supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Participants
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **All (_n_ = 101)** & **Girls (_n_ = 46)** & **Boys (_n_ = 55)** \\ \hline Variable & mean (95\% CI) \(\pm\) SD & mean (95\% CI) \(\pm\) SD & mean (95\% CI) \(\pm\) SD \\ \hline Age (years) & 13.96 (13.76–14.17) \(\pm\) 1.02 & 13.93 (13.62–14.25) \(\pm\) 1.05 & 13.99 (13.72–14.26) \(\pm\) 1.00 \\ \hline OffT (s) & 60.34 (58.81–61.86) \(\pm\) 7.73 & 59.15 (57.33–60.98) \(\pm\) 6.14 & 61.32 (58.95–63.70) \(\pm\) 8.77 \\ \hline OnT (s) & 72.81 (70.09–75.53) \(\pm\) 13.78 & 72.51 (68.31–76.70) \(\pm\) 14.13 & 73.06 (69.38–76.73) \(\pm\) 13.61 \\ \hline Off + OnT (s) & 133.14 (129.11–137.18) \(\pm\) 20.44 & 131.66 (125.92–137.40) \(\pm\) 19.34 & 134.38 (128.59–140.17) \(\pm\) 21.42 \\ \hline On \(-\) OffT (s) & 12.47 (10.69–14.25) \(\pm\) 9.01 & 13.35 (10.38–16.33) \(\pm\) 10.03 & 11.73 (9.54–13.92) \(\pm\) 8.09 \\ \hline BH (cm) & 166.72 (164.76–168.67) \(\pm\) 8.90 & 164.18 (161.78–166.57) \(\pm\) 7.29 & 168.91 (165.98–171.84) \(\pm\) 9.64 \\ \hline SBH (cm) & 86.42 (85.43–87.40) \(\pm\) 4.49 & 85.98 (84.66–87.30) \(\pm\) 4.01 & 86.80 (85.32–88.28) \(\pm\) 4.88 \\ \hline BM (kg) & 59.65 (56.22–63.08) \(\pm\) 13.95 & 55.24 (51.34–59.14) \(\pm\) 10.45 & 63.32 (58.08–68.57) \(\pm\) 15.50 \\ \hline Bfat (\%) & 21.39 (20.03–22.74) \(\pm\) 5.50 & 24.46 (22.81–26.11) \(\pm\) 4.43 & 18.83 (17.13–20.52) \(\pm\) 5.01 \\ \hline S10 (s) & 2.07 (2.04–2.10) \(\pm\) 0.15 & 2.11 (2.06–2.16) \(\pm\) 0.15 & 2.03 (1.99–2.08) \(\pm\) 0.15 \\ \hline S20 (s) & 3.67 (3.59–3.74) \(\pm\) 0.34 & 3.75 (3.65–3.86) \(\pm\) 0.32 & 3.59 (3.48–3.70) \(\pm\) 0.35 \\ \hline
20Y BP (s) & 4.98 (4.79–5.17) \(\pm\) 0.85 & 5.14 (4.88–5.40) \(\pm\) 0.79 & 4.84 (4.57–5.12) \(\pm\) 0.89 \\ \hline TCODS (s) & 2.74 (2.65–2.83) \(\pm\) 0.40 & 2.81 (2.69–2.93) \(\pm\) 0.35 & 2.68 (2.55–2.81) \(\pm\) 0.43 \\ \hline TRAG min (s) & 3.47 (3.37–3.58) \(\pm\) 0.47 & 3.59 (3.45–3.72) \(\pm\) 0.42 & 3.38 (3.23–3.53) \(\pm\) 0.49 \\ \hline SJ (cm) & 25.66 (24.38–26.95) \(\pm\) 5.85 & 23.91 (22.19–25.62) \(\pm\) 5.22 & 27.18 (25.36–29.00) \(\pm\) 5.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Participants’ characteristics.
## 3 Results and Discussion
### Size Optimization of AuNPs  
The size of the AuNPs was carefully selected. Four sizes of AuNPs with various diameters were synthesized and characterized using TEM, as shown in Figure 3a-d. The colors of the AuNPs with different diameters were visibly different, and the variation in color is a result of the different localized surface plasmon resonance (LSPR) of the AuNPs. As shown in Figure 3e, the LSPR bands were observed at wavelengths of 537.2, 536.4, 529.0, and 518.2 nm, corresponding to AuNPs with diameters of 50.2, 42.1, 29.3, and 17.4 nm, respectively. The excitation source used was a 532 nm green laser. It was speculated that the AuNPs, with a diameter of 42.1 nm and a LSPR band at 532.0 nm, exhibit the most suitable coupling resonance with the laser, thereby providing better and stronger SERS spectra. The UV-Vis results depicted in Figure 3f demonstrate a significant reduction in the intensity of the LSPR peak of ZIF-8@AuNPs, whereas the peak position remained  
Figure 1: SEM images of ZIF-8 (**a**), ZIF-8@AuNPs (**b**), XPS spectrum of ZIF-8@AuNPs (**c**), and EDS spectrum of ZIF-8@AuNPs (**d**).  
Figure 2: \(N_{2}\) sorption isotherms at 77 K of ZIF-8 and ZIF-8@Au. (**a**) The pore size distribution map of ZIF-8 and ZIF-8@Au (**b**).  
unchanged. This finding was attributed to the limited penetration of green light. Moreover, as shown in Figure S2, despite the larger amount of ZIF-8 compared to AuNPs, it exhibited fewer chemical binding sites with ZIF-8. This finding was consistent with the previous observation, in which ZIF-8 experienced less adsorption-site loss.
## 3 Materials and Methods
### K-Means  
The K-means algorithm is a method capable of automatically clustering similar data examples together using a training set \(\{x^{(1)},...,x^{(m)}\}\), where \(x^{(i)}\in\mathcal{R}^{n}\)[2; 28]. The algorithm is an iterative procedure that starts by randomly initializing the centroids, and then proceeds to loop consisting of two parts. The first part is the example-centroid assignment procedure, which means that every example gets assigned to the closest centroid using a certain distance metric. The second part is recomputing the centroids, which is done by calculating the mean value of all examples assigned to currently evaluated centroid and shifting the centroid to the computed mean value. This procedure is applied to every centroid, which ends the last step of the loop [2; 28]. The algorithm can be represented by the pseudocode shown on the Listing 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Work** & **Platform Used** & **Price** \\ \hline
[25] & STM32H743 VI & \$15.91 \\
[26] & Sipeed Maixduino & \$34.90 \\
[27] & STM32H74 VI & \$15.91 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Platform cost comparison.  
```
centroids = initialize_random(); for (int i = 0; i < iterations; i++) { example_assignments = assign_closest_centroid(); centroids = compute_means(); }
```  
**Listing 1**K-means algorithm.  
The K-means algorithm will always converge to a certain set of centroid values, but the solution may not be ideal, because it depends on the initial centroid values. Therefore, the K-means algorithm should be run multiple times with different random initializations. After running the algorithm several times, the best centroid set can be chosen from the results [2; 29]. The typical K-means algorithm implementation uses Euclidean distance as the distance metric in the example-centroid assignment procedure.  
The K-means algorithm was chosen for its overall lightness as it requires small amounts of both energy and memory to execute. The drawbacks of the algorithm were mitigated by careful design and testing of the preprocessing filters. Other methods that were considered have not been used due to the computational complexity and memory constraints. Notable methods other than the used K-means with \(O(n^{2})\) computational complexity are Support Vector Machines (SVM) with a computational complexity of \(O(n^{3})\), and Neural Networks with a computational complexity of \(O(n^{4})\). The algorithm we chose supported by carefully designed filters gave the best tradeoff between complexity (both computational complexity and memory complexity) and the given results. This is very important due to the strict TinyML limitations regarding the power consumption and available device resources. This approach gave us satisfactory results, and the other methods can be evaluated in future work. For more details, please refer to Sections 3.2.2 and 3.3.
## 2 Methodology
### Fusion Processing  
The point cloud dataset collected by the depth camera is large. Even after pre-processing, it is still dense, and if directly used for alignment, it will consume more time and memory, making it difficult to meet real-time requirements, so it is necessary to further segment the scene point cloud. After locating the position of the target object through the deep learning module, the coordinates of the center point of the detection rectangle box are converted to 3D coordinates, and then the coordinates are used to perform a KD-Tree search so as to achieve the positioning and segmentation of the target point cloud.
# Digital-Twin-Based Monitoring System for Slab Production Process
## 5 Conclusions  
In addressing the shortcomings in intelligence and production management during the slab production process, this paper establishes a monitoring system tailored for slab production based on digital twin (DT) technology. The defect identification module enables rapid and precise recognition and localization of defects in slabs, including their position and category. The production management module collects, processes, and displays data related to production output and defect predictions. The effectiveness of the system is thoroughly validated. Building upon these foundations, the contributions of this paper are outlined as follows:  
1. A comprehensive slab production process monitoring system: Achieves monitoring of the slab production process and encompasses various functionalities, such as defect identification, production output management, and defect prediction.
2. A faster and more accurate defect identification network: Compared to other state-of-the-art models, the precision of the network proposed in this paper is higher.
3. A defect prediction model: Integrates environmental information to predict the probability of defect occurrence in slabs. Users can adjust relevant settings based on this probability to reduce the likelihood of defects, thereby enhancing the production of high-quality steel and minimizing raw material waste.  
Through the establishment of the aforementioned monitoring system, the normal operation of the slab production process monitoring system has been realized. Currently, the framework system has been implemented, achieving real-time mapping from physical space to virtual space. However, there are limitations in more advanced applications, such as big data. In the future, improvements and upgrades will be made to the system to achieve a more intelligent and precise system.  
Methodology, software, validation, writing--original draft preparation, writing--review and editing, T.F.; writing--review and editing, P.L., C.S. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data available upon request due to privacy and ethical restrictions. The data presented in this study are available upon request from the first author. The data are not publicly available due to privacy and ethical restrictions.  
The authors declare no conflicts of interest.
# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
###### Abstract  
The large-scale integration of renewable energy and the serious safety issues faced by coal-fired units during peak regulation pose significant challenges to the reliable operation of power systems. Traditional probabilistic production simulation (PPS) methods fail to account for the fluctuations in load demand and the time-series variability of renewable energy, and they do not sufficiently consider the lifespan degradation of coal-fired units during actual peak regulation operations, which results in an inaccurate reflection of system reliability. This paper proposes an improved PPS method. First, this method rigorously considers the time-series fluctuations of load demand and renewable energy. Secondly, a multi-state model is established for coal-fired units, incorporating lifespan degradation and failure rates under different output conditions, which are dynamically updated based on load demand and operational status. An equivalent multi-state model for wind power output is also developed for different time periods. Finally, the Universal Generating Function (UGF) algorithm is used for PPS, enabling the calculation of system reliability indices, as well as dynamic costs such as unit start-up and shutdown, to assess the system's capability to accommodate wind power. The impact of different peak regulation capabilities of units on system reliability is also studied. This paper presents the theoretical foundation, algorithm implementation, and related case studies, verifying the rationality and effectiveness of the proposed method in addressing the above-mentioned issues.  
probabilistic production simulation; Universal Generating Function (UGF); reliability assessment; multi-state model +
Footnote †: journal: _Algorithms_