esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The increasing sophistication and frequency of cyber threats necessitate the development of advanced techniques for detecting and mitigating attacks. This paper introduces a novel cybersecurity-focused Multi-Layer Perceptron (MLP) trainer that utilizes evolutionary computation methods, specifically tailored to improve the training process of neural networks in the cybersecurity domain. The proposed trainer dynamically optimizes the MLP's weights and biases, enhancing its accuracy and robustness in defending against various attack vectors. To evaluate its effectiveness, the trainer was tested on five widely recognized security-related datasets: NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, and CSE-CIC-IDS2018. Its performance was compared with several state-of-the-art optimization algorithms, including Cybersecurity Chimp, CPO, ROA, WOA, MFO, WSO, SHIO, ZOA, DOA, and HHO. The results demonstrated that the proposed trainer consistently outperformed the other algorithms, achieving the lowest Mean Square Error (MSE) and highest classification accuracy across all datasets. Notably, the trainer reached a classification rate of 99.5% on the Bot-IoT dataset and 98.8% on the CSE-CIC-IDS2018 dataset, underscoring its effectiveness in detecting and classifying diverse cyber threats.  
c cybersecurity; neural networks; attack detection; metaheuristic Article
# Evaluation of Eco-Efficiency of Two Alternative Agricultural Biogas Plants
## Materials and Methods  
### Study Subject  
Two agricultural biogas plants operating in Poland were selected as the subjects of this study; they were designated as \"Biogas Plant A\" and \"Biogas Plant B\" for the purposes of this analysis. The subjects were selected so that they differ in terms of the installed capacity, type of feedstock and method of its transport, heat utilisation method, and handling of digestate (Table 1). They also reflect the installed capacity of installations operating in Poland.  
\begin{table}
\begin{tabular}{c c c} \hline
**Discriminant** & **Biogas Plant A** & **Biogas Plant B** \\ \hline Feedstock quantity [Mg/year] & 36,517 & 27,905 \\ \hline \multirow{3}{*}{Feedstock} & \multirow{3}{*}{Livestock manure (40.6\%) Maize silage (3.4\%) Rye silage (7.8\%) Maize silage (59.4\%) Distiller (38.8\%) Livestock manure (50\%)} \\  & & \\ \hline Feedstock transport & Wheeleded & Transmission (liquid) and wheeled (solid) \\ \hline Capacity installed & 1.0 MW & 0.526 MW \\ \hline Performance time [h/year] & 8100 & 8000 \\ \hline Quantity of biogas generated [m\({}^{3}\)/year] & 4,169,760 & 2,195,639 \\ \hline Quantity of electricity generated [MWh/year] & 7861 & 4193 \\ \hline Quantity of heat generated [MWh/year] & 7769 & 5132 \\ \hline Quantity of heat used [MWh/year] & 1470 & 4393 \\ \hline Total efficiency (electricity and heat) [\%] & 51 & 88 \\ \hline Distiller quantity [Mg/year] & 35,515 & 26,025 \\ \hline Distiller application & No separation, fertilising of cropland &
\begin{tabular}{c} Mechanical separation, fertilising of \\ cropland and cow litter (dry fraction) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: The most important parameters of the tested agricultural biogas plants.
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 4 Discussion  
At present, many semantic segmentation algorithms for automatic extraction of buildings from remote sensing images have emerged, but most of these algorithms spend a lot of effort on extraction accuracy, and there is little research on extraction speed. This work designs FRA-Net from the two aspects of balancing extraction accuracy and extraction speed. First, in order to reduce the gradient disappearance phenomenon in the training process of the model, the depth residual learning method is used to extract the building features in the remote sensing image in the coding stage. In order to reduce a large number of parameters, the depth separable convolution is introduced to improve the efficiency, and the separable residual module is designed to complete the downsampling work. In addition, in order to obtain the multi-scale information of building remote sensing images, a feature pyramid pooling module is designed to focus on context and extract multi-layer features more effectively. In the decoding stage, on the one hand, the separable residual module is used to gradually recover the spatial information to generate the final building segmentation result. On the other hand, the idea of an attention mechanism is introduced, and a multi-feature attention module is designed to recover the context information learned in the coding stage from multiple scales in an aggregated way.  
It can be seen from the experimental results that the accuracy of the method proposed in this paper can reach the premise of the latest method and can improve the operation efficiency of the model. Although FRA-Net has achieved better segmentation results than other semantic segmentation models, there are still many missed and false detections. On the one hand, due to the lack of training sets for special buildings, the segmentation effect can be improved by increasing the number of training sets. On the other hand, the semantic segmentation algorithm can be integrated with the target detection task to further optimize the network model.  
Figure 9: The local prediction map of each model in the Massachusetts building dataset. In the figure, green represents true-positive (predicted as a building, actually a building), blue represents false-positive (predicted as a building, actually a non-building), white represents true-negative (predicted as a non-building, actually a non-building), and red represents false-negative (predicted as a non-building, actually a building). (**a–c**) are the experimental results of various methods on images from the Massachusetts building dataset.
# Industrial Cyber-Physical System Evolution Detection and Alert Generation
## 5 CALENDAR Evaluation: Scalability Test
### Discussion
#### 5.1.2 F2: Percentage of Changes  
This factor evaluates whether the percentage change affects the execution time, i.e., if with the same Data Model (e.g., 1000 devices), changing 20% (e.g., 200) or 100% (e.g., 1000) of devices influences the time required (execution time) for detecting changes. The results are shown in Figure 7.  
Considering the different changes that can occur, we observe that  
* **Inserting:** The higher the percentage of change, the higher the execution time. In the smallest Data Model (50 devices), the execution time has an increase of 41.89%. With a bigger Data Model (e.g., 10000), the difference is 46.49%. Considering all the results (see Table 6), more or less of the difference between the minimum and maximum execution time when the percentage of change changes is between 40% and 60%. Therefore, as it is reflected in Figure 7, the growth of time is linear to the percentage of change.
* **Removing:** The lower the percentage of change made, the longer the execution time. In addition, the time decreases linearly. The execution time required for removing 20% in a 50 device Data Model is 14.462 ms, but in 80%, it is 10.06 ms. A difference of 52.86% exists, reaching 143.98% in the case of 1000 devices.
* **Modifying:** Increasing the percentage of change does not have a negative impact on time. The major percentage of change occurs with 1000 Data Model sizes, i.e., 0.013%. That is why we can concluded that the trend is constant.
* **Random:** This scenario is similar to modifying devices, i.e., increasing the percentage of change does not have a negative impact on the time, making the trend rather constant. Moroever, this is a case that depends a lot on the changes that have taken place.  
Therefore, in a common scenario where we do not control over what is going on, we can assume that the percentage of change does not have a negative impact on the execution time needed. The  
Figure 7: The increase in the percentage of changes in different ICPS Data Model sizes.  
times for adding and removing are compensated for each other, leaving a rather stable time when making random changes.
### Research Contribution  
The key contributions of the proposed research are as follows:  
* By combining the real-time, functional insights provided by EEG with the detailed structural information from MRI, a hybrid model can offer a more accurate diagnosis. This integrative approach leverages the strengths of both methods, potentially leading to earlier and more reliable identification of PD, especially in cases where symptoms are ambiguous or in their early stages.
* The proposed multimodal technique allows a more holistic view of the brain's functioning and structure. This comprehensive understanding is crucial in tracking the progression of PD, enabling clinicians to observe changes over time in both brain activity and physical structure. This can be instrumental in tailoring treatment plans and monitoring the effectiveness of therapeutic interventions.
* PD manifests differently in each individual. A hybrid diagnostic approach can provide a more personalized assessment of the disease's impact on a specific patient. This individualized understanding is key to developing personalized treatment strategies, ensuring that each patient receives care that is tailored to their unique presentation of the disease.
* Through rigorous experimental evaluations and comparative analyses with established benchmark methods, the proposed method has demonstrated superior performance, achieving an impressive accuracy rate of 97% for the PD-DS-I and PD-DS-II datasets.  
The structure of this paper is as follows: Section 2 reviews past research on [PERSON]'s detection. Section 3 outlines the materials and methods used in our study. Section 4 presents the experimental outcomes and provides a comparative evaluation of our model against pre-existing ones. Section 5 concludes by summarizing our work and findings.
## 4 Analysis and Discussion
### DOP Comparison between Monocrystalline Silicon and Polycrystalline Silicon Cells  
To further compare the polarization characteristics of monocrystalline and polycrystalline panel, two photovoltaic cells with size of 52 mm * 52 mm were selected and measured, fixed on the same plane, as shown in Figure 18, wherein the monocrystalline silicon photovoltaic cell is above and the other is below.  
Considering that the rated parameters of voltage, current, power, and others are different for monocrystalline and polycrystalline silicon photovoltaic cells, the same type of DC power supplies were used to apply a preset bias voltage at the same time. In the experiment, the bias voltage was adjusted and monitored by a SWIR camera so that the outgoing intensity from the two cells stayed almost equal, as shown in Figure 19.  
Figure 20 shows DOP images of two monocrystalline silicon and polycrystalline silicon cells, and the curves of DOP value with view angle are shown in Figure 21.  
Figure 19: Images I of solar cells (from top to bottom, the monocrystalline and polycrystalline silicon photovoltaic cell).  
Figure 18: Photovoltaic cells observed (from top to bottom, the monocrystalline and polycrystalline silicon photovoltaic cell).  
It can be seen from Figure 21 that the degree of polarization of monocrystalline and polycrystalline silicon cells increases with the view angle. Under the same luminous  
Figure 21: Curves of DOP value with view angle.  
Figure 20: Images DOP of monocrystalline silicon and polycrystalline silicon cells.  
intensity, the degree of polarization value of polycrystalline silicon cell is slightly greater than that of the monocrystalline one. This may be because there are more crystal defects in polycrystalline silicon solar cells, where high density dislocations significantly polarize the EL sub-band gap and affect the anisotropic charge distribution in the recombination process. It should be noted that due to the difference between the structure of the cell and the panel, the polarization degree of the cell is lower than that of the panel, and the data fluctuates, resulting in a DOP value lower than the theoretical value at 20\({}^{\circ}\) and 40\({}^{\circ}\).
## 2 Methodology  
For single-frequency GNSS data processing, ionosphere delay is one of the most challenging issues. In this study, the group and phase ionospheric correction (GRAPHIC)model is used to eliminate the first-order ionospheric effects [29]. The GPS/BDS-combined single-frequency KOD model is first introduced, and then an improved data-processing strategy, including the adaption of the weighting model and the joint detection algorithm of receiver clock jump and cycle slip based on single-frequency observations, is described.
## 4 Empirical Study
### Overall Comparisons for the Compared CNNs with Transfer Learning  
In the above experiments, the best settings for data augmentation, batch size, and resizing were determined. In this subsection, based on these settings, a comparative study is detailed, with evaluations of all models using transfer learning. Table 8 shows the effectiveness of the compared CNNs with transfer learning for different metrics, which can be explained from numerous viewpoints. First, the ranking of the five Nets was DenseNet, InceptionNet, EfficientNet, ResNet, and VGG, with average accuracies of 0.889, 0.889, 0.861, 0.852, and 0.806, respectively. Second, the top three individual models were DN201, DN169, and RN101, in accordance with their accuracies. Third, overall, the best performances for each metric consistently occurred in two networks, namely DN201 and RN101. From this aspect, DN201 and RN101 were the two most reliable models. Fourth, from an average point of view, the top three individual models were DN201, RN101, and INv3, for which the averages of all metric results were 0.886, 0.882, and 0.879, respectively. This can be regarded as an echo of the third point that DN201 and RN101 had a balanced performance for all metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Batch Size** & **8** & **16** & **24** & **32** \\ \hline VGG16 & 0.792 & 0.819 & 0.722 & 0.806 \\ VGG19 & 0.792 & 0.819 & 0.792 & 0.764 \\ RN50 & 0.764 & 0.792 & 0.806 & 0.792 \\ RN101 & 0.694 & 0.750 & 0.764 & 0.889 \\ RN152 & 0.500 & 0.681 & 0.681 & 0.792 \\ DN121 & 0.806 & 0.833 & 0.889 & 0.847 \\ DN169 & 0.722 & 0.847 & 0.833 & 0.889 \\ DN201 & 0.764 & 0.875 & 0.875 & 0.903 \\ INv3 & 0.861 & 0.833 & 0.847 & 0.847 \\ ENB0 & 0.847 & 0.847 & 0.833 & 0.806 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracies of compared CNNs for different batch sizes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Resize** & **112 \(\times\) 112** & **140 \(\times\) 140** & **168 \(\times\) 168** & **196 \(\times\) 196** \\ \hline VGG16 & 0.819 & 0.778 & 0.778 & 0.806 \\ VGG19 & 0.708 & 0.806 & 0.847 & 0.764 \\ RN50 & 0.861 & 0.806 & 0.792 & 0.792 \\ RN101 & 0.861 & 0.764 & 0.778 & 0.889 \\ RN152 & 0.778 & 0.833 & 0.764 & 0.792 \\ DN121 & 0.861 & 0.833 & 0.903 & 0.847 \\ DN169 & 0.833 & 0.861 & 0.833 & 0.889 \\ DN201 & 0.847 & 0.806 & 0.875 & 0.903 \\ INv3 & 0.875 & 0.847 & 0.875 & 0.847 \\ ENB0 & 0.819 & 0.833 & 0.819 & 0.806 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracies of compared CNNs for different image resizes.
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 2 Original Survey
### Photogrammetric Survey  
From the available sketch of the original photogrammetric survey, we were able to read that [PERSON] and his team captured images in stereo-pairs. The capturing scenario is apparent from the sketch (see Figure 2). Three stereo-pairs (A1-B1, A2-B2, A3-B3) were captured in one line and one stereo-pair (A5-B5) was shifted by 1 m to the line. One stereo-pair (A4-B4) was captured from an unknown further distance (according to the sketch, approximately 16 m). Stereo-pair A1_15-B1_15 (missing in Figure 2) was captured at exactly the same place as stereo-pair A1-B1 but the camera was rotated by 15\({}^{\circ}\) upwards. The distances between the images of the same stereo-pair were measured and were reported in an available document. There were markers M1-M6 on the south wall, signalized by crosses carved to the stones of the south wall of the abbey. The distances between those markers were measured most likely using a long tape. The result of the photogrammetric survey was a drawing of stones of the wall.
## 3 Cross-Layer Adaptive Feedback Scheduling  
To enable wireless control in dynamic environments, the feedback scheduling technology is adopted. It has been shown that feedback scheduling is quite promising in managing uncertainties in resource availability [28, 29]. This motivates the use of this technology in dynamic management of the variable communication resources in WLAN. To cope with the adverse properties of wireless communications, the cross-layer design methodology, a technique that is gaining increasing importance in networking applications, is incorporated with feedback scheduling.
###### Abstract  
In this paper, the tool-tip responses for ball end milling, due to the coupling effects of a flexible rotor-bearing system, are investigated numerically. The milling machine tool spindle is modelled as the flexible rotor-bearing system. The critical speeds, natural modes, and unbalance responses of the system are calculated by applying the generalized polynomial expansion method. This generalized polynomial expansion method expresses the displacement as a series formed by the product of generalized coordinates and axial coordinate polynomials. According to the dynamic cutting force obtained by some scholars in the past, combined with the characteristics of the flexible rotor, the dynamic response of the tool-tip for ball end milling is numerically analyzed. The responses, including time histories, orbits, and FFT diagrams, are plotted to analyze the dynamic behaviors of the tool-tip. The coupling effects of the flexible rotor-bearing system on the system for ball end milling are first studied using the generalized polynomial expansion method. Unlike previous studies, the natural frequency varies with spindle speed and which of the different modes are included in the tool-tip response depends mainly on the spindle speed. Thanks to the gyroscopic effect, the critical speeds and responses of tool-tips can be discussed with respect to various spindle speed and tool flutes. The natural modes are accurately determined, and will excite critical speeds for certain modes, including forward and backward modes, thereby significantly affecting tool-tip response. In addition, the cutting force component associated with the tool-tip response affects the rotor-bearing system parameters, complicating the issue. Milling at higher spindle speed (2160-19,950 rpm), an important new result is found that the tool-tip oscillates with the cutting-force frequency, accompanied by a longer period vibration of the first backward mode of the rotor-bearing system. It can also be seen from the frequency spectrum analysis that, as the spindle speed increases, the peak amplitude of the first backward mode becomes larger. Milling at lower spindle speed (960, 1320 rpm), the in-plane vibration trajectory of the tool-tip gradually expands outwards clockwise around the origin until a stable loop is reached. This is because only the first backward mode of the rotor-bearing system is excited. Considering the coupling effect of the rotor-bearing system to perform the vibration analysis of the milling machine system, the parameters of the system can be designed or the spindle speed can be selected to avoid severe vibration during machining.  
tool-tip response; ball end milling; coupling effects; flexible rotor-bearing system; generalized polynomial expansion method +
Footnote †: journal: Accepted: 14 June 2023
# High-Fidelity Fin-Actuator System Modeling and Aeroelastic Analysis Considering Friction Effect
## 1 Introduction
\(\sigma_{0}\) is the stiffness of the assumed elastic spring, and \(\sigma_{1}\) is the damping that is associated with micro-displacement. The bristles deform because of the action of external force, and the sum of the force generated by bristles' deformation plus the viscous friction represents the friction force [44]. The average deformation of the bristles reflects the characteristics of dynamic friction. Once the bristles reach their maximum deformation, which also means friction force has been larger than the maximum static friction force \(F_{\rm s}\), slip starts, as illustrated by the schematic representation in Figure 2.  
In this article, a high-fidelity simulation model of a typical electromechanical actuator was established, considering its structural characteristics, details of servomechanism, freeplay, and friction. The actuator was divided into modules to solve the problem that the previous model was not universal and extensible. Then the fin-actuator system was established by connecting the actuator with the fin using the branch mode method. The unsteady aerodynamic force acted on the control fin. The influence on the system of freeplay and friction was studied using numerical simulation. Compared with previous literature, the friction model used in this paper could better reflect the real physical phenomenon, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Viscous** & **Stribeck Effect** & **Pre-Sliding** & **Hysteresis** \\ \hline Coulomb & No & No & No & No \\ Viscous & Yes & No & No & No \\ Stribeck & Yes & Yes & No & No \\ Dahl & No & No & Yes & Yes \\ LuGre & Yes & Yes & Yes & Yes \\ Leuven & Yes & Yes & Yes & Yes \\ GMS & Yes & Yes & Yes & Yes \\
2 SEP & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Friction characteristics reflected by different models. Generalized Maxwell-Slip, GMS; two-state friction model, 2 SEP.  
the mechanism of friction effect on aeroelastic system was studied. Based on the results, some guidelines applicable to engineering projects are put forward. This paper is organized as follows. Section 2 shows the study object of this article, which is a typical fin-actuator system with a friction sheet. Section 3 presents the high-fidelity modeling method of a fin-actuator system considering aerodynamics. The frequency domain and time domain flutter analysis methods are established in Section 4. Section 5 introduces the results and discussion of the stability analysis of the fin-actuator system, considering freeplay and friction nonlinearity of the actuator, and proposes some rules for the design reference of flutter suppression.
## 4 Simulation and Performance Evaluation
### Pairwise keys  
We also evaluated the performance of the proposed framework for the pairwise keys mechanism. The key distribution time, memory usage, energy consumption, and scalability of the framework were evaluated for different numbers of sensor nodes in the network. The results of the evaluation are shown in Figure 6.  
As shown in Figure 6, the key distribution time, memory usage, and energy consumption increase with an increase in the number of sensor nodes in the network. However, the proposed framework exhibits good scalability for up to 100 sensor nodes in the network.  
Figure 5: Performance evaluation for pre-distributed keys mechanism.  
Figure 6: Performance evaluation for pairwise keys mechanism.