esa-sceva/satcom-chunk-collection huggingface dataset:
# Intrusion Detection Model Based on Improved Transformer
## 4 Results
### Experimental Results and Discussion
#### 4.3.4 Performance Analysis and Comparison Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **F1-Score** & **Training Time (s)** \\ \hline CNN & 0.793 & 0.785 & 22 \\ CNN-LSTM & 0.801 & 0.794 & 52 \\ CBA-CLSVE & 0.861 & 0.854 & 25 \\ SSC-OCSVM & 0.833 & 0.827 & 17 \\ CNN-GRU & 0.813 & 0.805 & 38 \\ FCNN-SE & 0.856 & 0.851 & 24 \\ RTIDS & 0.870 & 0.864 & 30 \\ VIT & 0.859 & 0.855 & 15 \\ CNN-Transformer & 0.866 & 0.861 & 25 \\ Ours & 0.887 & 0.882 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison experiment of binary classification ability. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **F1-Score** & **Training Time (s)** \\ \hline CNN & 0.788 & 0.782 & 23 \\ CNN-LSTM & 0.797 & 0.791 & 55 \\ CBA-CLSVE & 0.832 & 0.825 & 28 \\ SSC-OCSVM & 0.815 & 0.811 & 19 \\ CNN-GRU & 0.800 & 0.795 & 41 \\ FCNN-SE & 0.830 & 0.824 & 27 \\ RTIDS & 0.835 & 0.830 & 35 \\ VIT & 0.828 & 0824 & 20 \\ CNN-Transformer & 0.831 & 0.827 & 27 \\ Ours & 0.841 & 0.838 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison experiment of multi-classification ability. The three experiments conducted above demonstrate that the proposed model outperforms existing intrusion detection models and has a faster model training speed, as well as a certain level of robustness. Moreover, compared to recently proposed Transformer-based models, the proposed model in this paper has higher accuracy and a faster model training speed. These results confirm the effectiveness and efficiency of the proposed model in detecting network intrusions, indicating its potential application in real-world scenarios.
## 3 Results
### Integrating a SRAS System inside a SLM Build Chamber  
The optical bench-top system shows that a rough-surface SRAS system is possible. However, an engineering challenge exists in implementing a SRAS setup inside an additive manufacturing build environment, specifically a SLM build chamber, if the aim is to demonstrate the potential of an in-situ inspection system.  
To enable this investigation, a barebone SLM shell was acquired from Renishaw (AM250, Renishaw plc., Stone, UK). The rough-surface SRAS setup was modified, incorporating two galvanometer 50 mm mirrors (O-QS30A50D40B24001, Nutfield Technology, Hudson, NH, USA) and a \(f=100\) mm f-theta lens, to provide translation and to collect a large amount of the reflected speckle (which is then imaged onto the SKED), shown in Figure 9a,b. This optical arrangement is commonly seen in SLM machines for translating the build laser--the integrated SRAS system was intentionally designed to provide compatibility with the SLM design, strengthening the case for a 'SRAS AM add-on tool'.  
As this work is in progress, the optical arrangement requires further optimisation. A scan of Ti64 strips, built with parameters similar to the sample in the previous section, reveals that SRAS signals can be seen only at certain locations on the sample (shown in Figure 9c,d). This is due to topographical changes on the sample, where there were not enough light returns through the f-theta lens to make a measurement. Plans for improving the integrated SRAS setup include changing the beam profile of the detection laser, where incident energy density is maintained but power is increased, increasing the repetition rate of the generation laser whilst proportionally increasing signal averaging, and implementing a multi-point detection system around the build chamber.  
Figure 8: (**a**) Optical micrograph of the Ti64 SLM sample (**b**) SRAS velocity map of a 3 mm \(\times\) 5 mm area on the Ti64 SLM sample. The sample was scanned using a step size of 25 \(\upmu\)m in both directions, therefore the image produced is oversampled. In areas where a low or no signal is measured, it is coloured grey. Similar features can be seen in both images, which includes surface cavities and the line mark.
## 5 Future Work  
After proving that the developed procedure can be successfully employed to synthesize the IKMs of a multi-legged walking robot (Lynxmotion's BH3-R hexapod) and a non-redundant manipulator with an in-line wrist (PUMA 560), the next logical step is to extend the procedure to also cover the end effector's orientation, in order to complete the calculation of the full pose of all types of non-redundant robots. It is important to highlight that the extension to the generic 6R manipulator will present more than four solutions, and therefore it will not be possible to find an analytical solution, like it was done in this project. That case will surely require the application of numerical methods to find the final solution. However, all the cases of non-redundant manipulators that have an in-line wrist,  
Figure 11: Trajectory tracking analysis for the hexapod’s IKM. The red dots represent the outputs of the reference model, for each of the leg’s DoFs, when it is supplied with the predetermined trajectory, while the continuous blue lines are the IKM’s outputs for that same trajectory. The upper left figure presents the computed outputs for the first DoF (\(q_{1}\)), the upper right one corresponds to the second (\(q_{2}\)), while the bottom figure is the one for the third DoF (\(q_{3}\)). The data show that the IKM’s outputs follow the ones of the reference model, with high accuracy and a negligible error along all the desired trajectory, in all three DoFs.  
non-redundant multi-legged walking robots, Cartesian robotic systems, and SCARA robots would still have analytical solutions, as it was shown in this work.  
Another proposed future work is the extension to redundant robots, to fully cover all the spectrum of open-chain robotic systems. The application of Groebner Basis theory to a redundant robot will surely produce an underdetermined equation system that has infinite solutions. The final solution of these types of equation systems will require the application of kinematic restrictions, such as Lagrange multipliers, as is common for redundant robotic systems.
# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 4 Experimental Results
### Dataset  
We perform experiments on the three widely used few-shot image recognition datasets and the Miner Unsafe Behavior dataset (MUB). MiniImageNet [10] is the derivative of ImageNet [32], and CIFAR-FS [33] and FC100 [34] are extracted from CIFAR-100 [35; 36]. The Miner Unsafe Behavior dataset is a video dataset established by this research team that contains a wide range of miners' unsafe behaviors.  
The miniImageNet dataset is a widely used dataset in few-shot learning. The dataset is extracted from ImageNet and contains 100 classes, each containing 600 images with a resolution of 84 \(\times\) 84 pixels. Out of the total classes, 64 are used as a meta-training set, 16 as a meta-evaluation set, and 20 as a meta-testing set.  
The CIFAR-FS dataset, fully called CIFAR100 few-shot dataset, is extracted from the standard CIFAR100. It contains 100 classes in total, each containing 600 images with a resolution of 32 \(\times\) 32 pixels, with 60,000 images. Out of the total classes, 64 are used as a meta-training set, 16 as a meta-evaluation set, and 20 as a meta-testing set.  
The FC100 dataset is another subset of CIFAR100, containing 100 classes, each containing 600 images having a resolution of 32 \(\times\) 32 pixels, with a total of 60,000 images.  
However, FC100 is divided into super-classes rather than classes. Among them, 12 super-classes (60 classes) are used as meta-training sets, 4 super-classes (20 classes) are used as meta-evaluation sets, and 4 super-classes (20 classes) are used as meta-testing sets.  
The MUB dataset contains 51 classes of miners' unsafe behaviors and 1530 sets of video actions. Each behavior consists of 30 video actions performed three times by 10 members of the research team. Out of the total classes, 31 are used as a meta-training set, 10 as a meta-evaluation set, and 10 as a meta-testing set.
## 3 Adaptive Traffic Light Signal Control Based on DRL  
ATSC algorithms at the beginning of their development were based on _rule-based_ methods. The _rule-based_ methods were firstly implemented within the fixed signal time strategies, e.g., changing the predefined signal plans in accordance with the characteristic period of the day (non-peak and peak-hours, night- and daytime, working days and weekends, the season in the year, etc.). The heuristic _rule-based_ methods such as Longest-Queue-First (LQF) or _Greedy policy_ conduct traffic light phase switching in accordance to the measured traffic demand at each intersection bound [34]. Those types of algorithms provide improvements over the aforementioned algorithms since they take into account the information about the local traffic scenario by processing the data from sensors installed at the controlled intersection. It is noticed that those ATSC algorithms have reduced effectiveness in dissolving traffic congestions which are affecting urban traffic environments. Those environments are characterized by the strong fluctuations in the increased traffic demand over the day [35; 36]. Those poor results are the product of the modeling process which is mostly based on common traffic scenarios without the inclusion of human errors and sudden increase in traffic demand due to various social reasons. The stochastic nature of traffic flows is tackled by the RL based algorithm such as Q-learning since they do not require a model of the controlled system. That control approach has shown constraints regarding computational efficiency, learning stability, and scalability [37; 38; 39]. The core problem with the Q-learning algorithm is linearly parameterized function approximation, which requires a set of carefully handcrafted features from sensory data to perform optimal control [34]. The use of the DNN model in the RL context enables the feature extraction directly from the raw traffic data, which allows a more comprehensive ATSC independent of the Q-table usage and human errors related to feature extraction. Even early results achieved by the application of a DRL approach in ATSC for one isolated signalized intersections are promising.
## 5 Performance Evaluation
### Simulation
#### 5.2.1 No Population Churn
Since we did not allow any population churn in this experiment, the only collisions arise from the reseelection at \(RC=0\) with the blind choice of the resource for the next streak. The lookahead turns the blinded ventures into the enlightened choices in SPS/LA, thereby significantly reducing the collision probability.
## 1 Introduction  
Oxidative stress induce inflammation via reactive oxygen species (ROS) formation, which are reflected the strong inflammatory mediators. Several ROS are produced inside lipopolysaccharideacharide (LPS)-stimulated macrophages, such as the superoxide radical anion, which is produced primarily, but not exclusively, by NADPH oxidase (NOX) [1]. NOX is present in various cells, especially specialized phagocytic and endothelial cells [1]. Polymorphonuclear neutrophils lead to increased ROS production at the site of inflammation, resulting in endothelial dysfunction and tissue damage [2]. Because antioxidants reduce inflammation, therefore, an antioxidant drug may be an anti-inflammatory drug and vice versa.  
Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor that can obstruct inflammatory events. Antioxidant gene expression mediated by Nrf2 is proposed to diminish the macrophage M1 phenotype and ROS production. Since Nrf2 stimulators are reported to be an efficient therapeutic strategy for several diseases [3], such as inflammatory disorders and cardiovascular diseases [3], the Nrf2 signaling pathway is considered to be a critical cascade in inflammation. Nrf2 mitigates inflammatory reactions by inducing anti-inflammatory enzyme heme oxygenase-1 (HO-1), and negatively regulating the expression of proinflammatory cytokines and chemokines [4]. Therefore, developing new pharmacological interventions that induce Nrf2 and HO-1 is crucial for the therapeutic control of inflammatory diseases and oxidative stress.  
Increasing attention has been paid to antioxidant-derived metal complexes as novel methods of protecting living organisms and cells against damage caused by oxidative stress and free radicals [5]. Transition metal complexes have been reported to have various biological activities through free radical scavenging properties [6]. Previous research has also found that ruthenium complexes exhibited anticancer activity [7]. Ruthenium complexes have been reported to exhibit strong scavenging activity on 2, 2'-azinobis (3-ethylbenzothazoline-6-sulfonic acid) (ABTS) and DPPH radicals and antiproliferative effect against some cancer cell lines [8]. Our previous study showed that [Ru(16-cymene)2-(1H-benzoimidazol-2-yl)-quinoline Cl]BF4 (IQ-6), a synthetic ruthenium (II)-derived compound, exhibited potent anti-inflammatory activity in vitro and effectively inhibited liver injury in vivo. This research further suggested that nuclear factor (NF)-xB is a potential target for the IQ-6's preventive effects in LPS-induced inflammation and liver injury [9]. In addition, we showed that IQ-6 inhibited OH\({}^{\bullet}\) formation and platelet activation in collagen-stimulated platelets, suggesting that free radical scavenging action can be involved in TQ-6-mediated inhibition of in vivo thrombogenesis [10]. The present study examined the antioxidant property of TQ-6 by determining its radical scavenging and Nrf2/HO-1 induction potential in vitro. Moreover, the anti-inflammatory action of TQ-6 was compared with the nonsteroidal anti-inflammatory drug indomethacin in the rat paw edema model.
## 2 Sample Environment Components
### Gas Flow Setup
In order to test the gas flow system, we adjusted the gas flow through one water-filled washing bottle from 0 to 100% (100% was the equivalent to 1 L min\({}^{-1}\)) in 20% steps and adjusted the gas flow on the pure nitrogen line accordingly to a total flow of 1 L min\({}^{-1}\) (N\({}_{2}\) flow at 100 down to 0%). Then, we increased the temperature from 20 to 50 \({}^{\circ}\)C and repeated the same experiment. The evolution of the temperature and humidity at the chambers center were recorded with an SHT31 sensor and are shown in Figure 5. The humidity within the chamber starts at 0% RH (0% H\({}_{2}\)O and 100% N\({}_{2}\) flow) before rising to 20% over the course of around 7 min as we adjust the flow rates accordingly (20% H\({}_{2}\)O and 80% N\({}_{2}\) flow). The measured humidity continues to follow the adjusted flow rates in 20% steps, which proves the good control of the gas flow system. Eventually, a humidity of 96%RH (100% H\({}_{2}\)O and 0% N\({}_{2}\) flow) is reached. Condensational losses at the windows and the tubes prevented the humidity from being even higher, although no visible evidence of condensation during and after any performed experiments was found. Furthermore, the equilibration time increases as we aim to obtain a pure water or nitrogen atmosphere, which is due to the relatively large volume of the chamber. After adjusting the flow rates back to a pure nitrogen flow, it takes around 17 min to dry the atmosphere back to below 1% RH. The repeated experiment at a temperature of 50 \({}^{\circ}\)C, shows an equally good control over the humidity and similar equilibration times. The highest achieved humidity is slightly lower  
Figure 4: Scheme of the gas flow system. The gas flow circuit (orange) consists of a dry nitrogen supply (**A**) regulated by three gas flow controllers (**B**), two washing bottles for humidification (**C**), a water heated tube to the chamber (**D**), and a washing bottle for gas cleaning (**E**). The control circuit (green) consists of the gas flow controllers (**B**), a power supply and readout system (**F**), and a computer with the running software (**G**). The temperature circuit (blue) consists of a thermal bath (**H**) and the tubing around the washing bottles to the chamber and within the chamber itself. Non-return valves are used to prevent backflow and uncontrolled diffusion from the washing bottles. at 93% RH due to the stronger temperature gradient at the windows. In conclusion, the presented gas flow system allows for the study of a broad and well-controlled temperature and humidity range despite the large chamber volume and generous window sizes. This system was already used in neutron scattering experiments that investigated thin-film responses under changing atmospheric compositions, which also demonstrated the use of solvents other than water [45; 46]. The setup currently runs without a feedback loop, as set humidity (via the flow rates) and temperature values are reached accurately.
## 3 Methodology
### CTG Signal Analysis
#### 3.2.1 Signal Preprocessing  
The CTG signal can involve different types of artifacts, such as loss of data and outliers, which are mainly generated by the loss of the sensor's contact because of movements that can temporarily interrupt the signal's acquisition.  
On the one hand, for the FHR signal preprocessing, we apply the artifact rejection method proposed in [55]. First, the signal values considered physiologically inconsistent, i.e., outside the range between 50 bpm and 210 bpm, are removed from the signal. Then, loss-of-signal data \(\leq\)75 s in length are interpolated by using a Hermite spline method.  
On the other hand, for the UC signal, loss-of-signal data less than 25 s in length are interpolated; then, the signal is filtered by a moving average filter of 15 s windows length. This filtered UC signal is using for the deceleration identification, as explained in Section 3.3. The preprocessed FHR and UC signals are shown in Figure 3a,b, respectively.
# Laser-Accelerated Plasma-Propulsion System
## 5 LAPPS Performance Limits
Both laser efficiency \(\eta_{L}\) and interaction efficiency \(\eta_{I}\) contribute to this limit according to the general relation  
\[\eta_{LP}\equiv\frac{E_{R}\eta_{I}\eta_{L}}{\epsilon c^{2}dM}, \tag{28}\]  
where \(E_{R}\) is the total energy generated by the system (\(\dot{E}_{R}=P_{R}\)). From Equation (28), we also obtain the relation between \(I_{sp}^{max}\) and \(I_{sp}^{L}\):  
\[I_{sp}^{max}\simeq I_{sp}^{L}\sqrt{\frac{\eta_{I}E_{R}}{\eta_{L}c^{2}dM}}. \tag{29}\]  
For typical laser systems and targets, \(\eta_{L}\) and \(\eta_{I}\) are of the same order of magnitude while \(E_{R}\ll c^{2}dM\). According to Equation (29), the relation \(I_{sp}^{max}<I_{sp}^{L}\) is therefore usually fulfilled. However, in the limit of \(\epsilon\to 1\), the conditions \(E_{R}\lesssim c^{2}dM\) and \(I_{sp}^{max}>I_{sp}^{L}\) become potentially achievable (although without any specific advantages for space propulsion due to the low specific impulse which characterizes this condition). Realistically, the conditions (27) and (28) exclude the possibility of obtaining a relativistic propellant speed from any optimized laser-based closed cycle scheme. This is a general property which depends on the low efficiency of the energy transfer from the power supply to the laser pulse. In conclusion, despite laser-plasma interaction has not yet been discussed in detail, basic properties and limitations of the LAPPS scheme have been already obtained. These results suggest that a space thruster based on laser-plasma interaction should primarily generate a well collimated particle beam with a low fraction of wasted target material. This also implies that schemes, as for example laser-driven plasma-based electron accelerators [33], where the largest fraction of the target (usually a gas-jet) is not accelerated, are not indicated as thrusters.
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 2 Application of VSL and RM Control in HDV Traffic Flows
### Impact of VSL and RM on HDV Traffic Flows  
The first analysis of the impact of VSL on traffic flow dates back to 1972 [59]. This study was conducted with traffic data collected on German motorways. The same author extended the earlier analysis and concluded in [33] that VSL significantly contributes to the homogenization of vehicle speeds in traffic when VSL is applied to the light or medium traffic demand. In this case, VSL consequently causes a decrease in the average speed of the traffic flow and leads to speed homogenization. Based on the results of [59], study [60] provides a quantitative description of the impact of VSL on the fundamental flow-density diagram. A model of the basic diagram was constructed, showing that as the amount of the speed limit is reduced, the capacity of the fundamental diagram increases, i.e., there is an increase in the vehicle flow. Such an increase in capacity was later found to be inaccurate. Research in [61] could not find any capacity increase attributable to the VSL application. In [62], the analysis on the effectiveness of the VSL in relation to the vehicle speed was carried out on 22 sections of national roads in Poland. The research included 14 rural sections with legal speed limits of 90 km/h, 70 km/h and 50 km/h, and eight sections of national roads with a VSL system installed with 90 km/h and 50 km/h speed limits. The analysis showed a very positive impact of such a system, resulting in an effective and significant reduction in average speeds and the number of noncomplying vehicles, as well as improved speed harmonization.  
In [63], the impact of the fixed time RM and ALINEA RM on peak hour traffic flow was analyzed and compared. The results show that RM has no significant effect on traffic flow at average demand, but at high demand, the delay time of vehicles in this segment was reduced by 91% and 64.8% for the fixed time RM and ALINEA algorithm, respectively. At low demand, the delay time was increased both on the motorway and on the on-ramp, thus proving the RM is ineffective in such scenarios. It is noted that the RM has positive effects on traffic operations and reliability, but it could create a new (possibly \"hidden\") bottleneck to occur downstream, thus diluting the overall benefits [64]. In [64], ALINEA and HERO RM strategies were tested. ALINEA produced a better \(TT\) than HERO with smaller differences between the two. The throughput and congestion duration were also improved at all congestion levels. In the following subsections, the surveyed algorithms are grouped according to common metrics and performance indicators of the algorithm, such as safety, emissions, and macroscopic traffic parameters. First, the impact of VSL is analyzed, and then the impact of RM is added into the analysis.