esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacting Robustness in Deep Learning-Based NIDS through Poisoning Attacks
## 2 Related Work
### Poisoning Attacks
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Methodology** & **Conclusion** \\ \hline \multirow{3}{*}{[40]} & \multirow{3}{*}{KDD99 NSL-KDD} & Developed a Recurrent Neural Network & Comparative experiments showed that the GRU is more suitable as a memory unit for intrusion systems than LSTM. \\  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{3}{*}{[41]} & \multirow{3}{*}{UNSW-NB15} & Focused on the utility of DL frameworks for NIDS systems that scan traffic across networks to detect and record a violation based on the intrusion’s behavioral patterns discovered in the dataset. & The suggested technique achieved a total accuracy of (95.4\% and 95.6\%) for the prepartitioned and multiple-class categorization systems. \\ \hline \multirow{3}{*}{[42]} & \multirow{3}{*}{ISCXIDS 2012} & Proposed Deep Neural Network (DNN) that trains an NIDS using supervised training and uses an autoencoder to identify and categorize attack traffic by unsupervised learning. & DNN works satisfactorily in supervised IDSs, particularly when only the first 3 octets of IP addresses are processed. Autoencoders can additionally recognize anomalies when trained on benign traffic. \\ \hline \multirow{3}{*}{[43]} & \multirow{3}{*}{CICIDS2018} & Developed a DL technique for detecting cloud computing assaults, utilizing [PERSON]’s Correlation method. & The suggested system has an extremely low FPR of 0.003\%. This minimizes the need for human analysts to manage many alerts. \\ \hline \multirow{3}{*}{[44]} & \multirow{3}{*}{MNIST} & Suggested an attack to employ a gradient ascent technique, in which the gradient is calculated using characteristics of the SVM’s best solution to raise the classifier’s test errors. & The findings show that the gradient ascent approach accurately determines the local maxima of the non-convex validation error surface. \\ \hline \multirow{3}{*}{[45]} & \multirow{3}{*}{ImageNet} & Focused on targeted poisoning assaults that change the classification in modern-DNN of unaltered test images, compromising system integrity. & This approach enables highly successful data poisoning assaults against completely retrained models. \\ \hline \multirow{3}{*}{[46]} & \multirow{3}{*}{PDF file.} & A methodology has been suggested to better analyze and define assaults on feature extraction strategies, which are: LASSO, ridge regression, and the elastic net. & Malware detection outcomes indicate that feature selection approaches can be greatly compromised during attacks, focusing on the necessity for particular countermeasures. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work.
# An Efficient and Robust Topology Optimization Method for Thermoelastically Damped Microresonators
###### Abstract  
The challenges of computational cost and robustness are critical obstacles in topology optimization methods, particularly for the iterative process of optimizing large-scale multiphysical structures. This study proposes an efficient and robust topology optimization method for minimizing the thermoelastic damping of large-scale microresonators. An evolutionary structural optimization method is adopted to passively determine the search direction of optimizing large-scale thermoelastic structures. To efficiently reduce the computational cost of the iterative process of an optimizing process, a model reduction method is developed based on the projection-based model reduction method whose reduced basis is generated within the Neumann series subspace. However, the projection-based model reduction method may be unstable when topology modifications are made during an iteration optimization process. To ensure robustness, a modal validation technique is first implemented in the iterative process to stabilize the iteration and narrow down the search domain, and a posterior evaluation of the Neumann series expansion is then developed to retain the convergence of the projection-based model reduction method. Furthermore, the efficiency and accuracy of the proposed topology optimization method are validated through numerical examples. Two large-scale numerical models are also used to demonstrate the advantage of the proposed method. It is found that large-scale thermoelastic structures with a phase-lag heat conduction law can be designed passively, precisely, and efficiently by using the proposed topology optimization method.  
Keywords:model reduction method; non-Fourier heat conduction; topology optimization; Neumann series expansion; posterior evaluation; efficient and robust method +
Footnote †: journal: Applied
## 3 Results
### Cell Viability of Vitamin E and Resveratrol  
Before investigating the effect of vitamin E and resveratrol on the inflammatory gene expression of OA-like chondrocytes, the cell viability of different concentrations of vitamin E and resveratrol was analyzed. When L92 cells were cultured with vitamin E for 1, 2, or 3 days, the cell viability was higher than 90% regardless of the concentration (Figure 2A). Although the cell viability was higher when vitamin E concentration was higher, there was no significant difference compared with 5 uM vitamin E. When the cells were cultured with resveratrol, the cell viability was higher than 80%, with no statistical differences among the  
Figure 1: The induction of osteoarthritis by H\({}_{2}\)O\({}_{2}\) in porcine chondrocytes. (**A**) The level of oxygen-derived free radicals was represented by the level of lumen density. The level of lumen density increased significantly when the chondrocytes were treated with H\({}_{2}\)O\({}_{2}\) for 30 min and 2 h. **_p_ < 0.01 compared with no induction. ++ \(p\) < 0.01 compared with 30 min induction. (**B**) The expressions of IL-1\(\beta\), TNF-\(\alpha\), and COL2A1 were higher when the chondrocytes were induced by H\({}_{2}\)O\({}_{2}\) for 30 min (white bars) than for 2 h (gray bars). Gene expression with no induction is shown in black bars. * \(p\) < 0.05, **_p_ < 0.01, and ***_p_ < 0.001 compared with no induction.  
different concentrations (Figure 2B). The concentrations of vitamin E and resveratrol tested in this study demonstrated good biocompatibility.
## 5 Results and Discussion
### Identification Discuss of Core Neutron Dynamic Module
#### 5.2.2 Identification of Temperature Feedback Coefficient for the Core Neutron Dynamic Module  
Given the power set a step change from 1 to 0.9 at 1 s, the fuel temperature and the coolant temperature feedback coefficients could be identified assuming that \(\beta\), \(\lambda\) and \(l\) were already known. The identification of five parameters (i.e., \(\beta\), \(\lambda\), \(l\), \(\alpha_{\mathrm{F}}\) and \(\alpha_{\mathrm{C}}\)) of the three-input single-output core neutron dynamic module was attempted using the RP-PSO algorithm with the parameter scope of \(1\times 10^{-8}\) to 1. Table 3 listed the parameters for core neutron dynamic module. Figure 16 showed the comparison results after identification.  
As the power set was reduced, the control rod insertion decreased the reactivity. \(N_{\mathrm{r}}\) decreased rapidly with instantaneous change of \(T_{\mathrm{F}}\), and then slowly raised to 0.9. \(T_{\mathrm{av}}\) firstly presented an increasing trend (see Figure 16).  
The RP-PSO was compared with two other algorithms namely PSO and CPSO (i.e., the cooperative particle swarm optimization algorithm) [26]. Figure 17 shows the fitness function distribution of optimization solutions with three kinds of identification algorithms.  
Due to the orders of magnitude (i.e., for the parameters to be identified were far less than 1), the PSO and CPSO algorithms were easier to cause a certain parameter to reach the set boundary value of \(1\times 10^{-8}\) with the fitness function value reaching about a negative 6 order of magnitude (see Figure 17). The identification results of these two algorithms were bad. However, the RP-PSO algorithm produced some better results with the fitness function value of one group solution reaching a negative 11 order of magnitude. For this group of solution, the simulation results were acceptable  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Identified** \\ \hline \(l\) & \(2.1\times 10^{-5}\) s & \(8.30\times 10^{-6}\) s \\ \(\beta\) & \(4.4\times 10^{-3}\) & \(0.02529\) \\ \(\lambda\) & \(0.0767\) s\({}^{-1}\) & \(0.07666\) s\({}^{-1}\) \\ \(\alpha_{\mathrm{F}}  
(see Figure 17). The identified parameters of \(\alpha_{F}\), \(\alpha_{C}\) and \(\lambda\) were consistent with the real value. However, \(\beta\) and \(l\) were slightly different from their real values since the input signal of \(\rho\) was related to \(\beta\), which affected the identification as analyzed in Section 5.2.1. From the above analysis, it could also be concluded that, to a certain extent, the parameter scope might affect the optimization results of the algorithm.  
The identification process that would obtain five parameters provided a way to get appropriate core neutron dynamic module parameters using the measured data of various working conditions, or under different fuel life cycles. Also through the identification based on the integrated primary loop system model, the temperature feedback coefficients had a significant effect on the identification results.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 6 Test Campaigns and Results
### Wi-FiTM RTT Ranging
#### 6.2.2 Position Accuracy and Precision  
Albeit not having a true reference location of the smartphone, a first quality assessment of the positioning solution could be based on the standard deviation of the East/North/Up (ENU) components relative to the average position of the time series. The time series of the ENU components are shown in Figure 12.  
It can be seen that, under optimal conditions (static device with no nearby obstructions), the precision of the solution of 0.3 m/0.5 m/1.5 m in ENU respectively can be achieved. As expected, the North component is the one performing the worst due to a similar reason as in GNSS, the Dilution of Precision (DOP): All routers were placed at the same height as the device. If the WAP were located at different heights (providing more geometric diversity in the height component), the precision of the Up component would also be improved.  
Figure 12: East/North/Up components of the Wi-FiTM RTT position estimates relative to the average position of the smartphone (in static). Standard deviation of each component is also included in the plot.  
Albeit Wi-Fi(tm) RTT ranging can be a potential solution to provide indoor navigation capabilities, its main limitation, as of today, is scalability. This limitation stems from the RTT range measurements side because 802.11 mc is a bi-directional protocol and therefore a high number of simultaneous users performing the RTT query might clog the router. There are already various works trying to overcome this limitation in order to convert WiFi routers closest to a one-way navigation system such as GNSS (see for instance [63]). On the _testbed_ side (i.e., data-management side provided by WALS), there is no limitation on the number of users because the container can be replicated in mirror servers as the number of users increase.
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 3 Through Metal Scenarios
### Aluminum  
Figure 13 shows the measured results of harvested power through aluminum plates of different thicknesses. Aluminum is more conductive than stainless steel [33], so the harvested power is considerably decreased as the thickness of the plates increases from 1 mm. For a 1.55 mm-thick plate, the harvested power is only 74 mW, less than half of the harvested power using a 1 mm-thick aluminum plate. If the plate thickness is increased to 2.235 mm, 36 mW are available. When the thickness is 3.15-mm, almost three times the thickness of the base line plate of 1-mm-thick aluminum, only 17 mW are available. Further increasing the barrier to 4.98 mm makes the harvested power limited to less than 3 mW. In conclusion, stainless steel is much better than aluminum for power transfer and signal communication through it.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 4 Deep Learning Models
### Convolutional Neural Networks (CNNs)  
Convolutional neural networks (CNNs) are a class of deep learning models primarily used for the processing and analysis of data with a grid-like structure, such as images and audio. The design inspiration for CNNs stems from the understanding of the animal visual system in biology. CNNs have achieved remarkable success in various domains, including image recognition, image classification, object detection, and semantic segmentation. A typical CNN consists of convolutional layers, pooling layers, and fully connected layers. In the convolutional layers, the network applies a series of filters to extract features from the input data, generating feature maps. The pooling layers serve to reduce the spatial dimensions of the feature maps, thereby decreasing the computational complexity, often utilizing operations such as max pooling or average pooling. Finally, the fully connected layers map the high-level features to the final output, performing tasks such as classification or regression. These characteristics enable CNNs to effectively capture the features of traffic data, thereby excelling in traffic flow prediction tasks. The corresponding structure is shown in Figure 3. Since convolution is computationally intensive, especially in deep CNNs, they require a large amount of GPU computing power; thus, this method has a high computational cost, but it is also highly scalable and can handle more complex tasks after adding convolutional layers and parameters. Taking the basic CNN as an example, the data transfer is shown in Equation (2). In the formula, \(Y\) is the prediction result; \(X\in\mathbb{R}^{N\times T\times F}\) is  
Figure 2: Structure of the MLP.  
the input of the CNN; N is the number of nodes; T is the length of the time series; F is the number of features; FC is the fully connected layer; Pool is the pooling layer; ReLU is the activation function; and Conv is the convolutional layer.  
\[Y=\text{FC}(\text{Pool }(\text{Re LU}(\text{Conv }(X)))) \tag{2}\]  
Fast R-CNN, introduced by [PERSON] [19], incorporates multiple innovations to improve both the training and testing speeds, while also enhancing the detection accuracy. [PERSON] et al. [20] proposed a deep neural network using a CNN-LSTM architecture for multi-step prediction. The CNN-LSTM framework is capable of identifying spatial and temporal relationships within traffic data sourced from GPS trajectories. Moreover, they introduced a data reduction technique and compared it with the advanced TF algorithm, yielding improved prediction results.
## 2 Related Work
This article describes the LPS _LOSNUS_ (Localization of Sensor Nodes by Ultra Sound) [3] as a complete and independent LPS where the same system can be used for calibration and localization. It enables a calibration process without expensive tools and/or laborious time in order to get accurate positions of beacons. _LOSNUS_ delivers high locating accuracy and can also tolerate single failures for the ToA measurements allowing arbitrary failure mode. Low cost and high accuracy are important properties of this system.
## 2 Methodology
### Pilot Study
#### 2.2.1 Mechanical Testing  
The pin bending test was conducted to identify pin mechanical properties. Four pins were selected and pin bending under varying (increasing) transverse loads were measured (Figure 6). The system was pre-loaded with 5 N and loaded till 60 N, at 10 mm/min.  
Interface Test  
The clamp pin assembly was subjected to the same transverse loading conditions as the pins were, in the pin bending test (Figure 7). Objective of this test was to understand the combined properties of the following interfaces:  
* Pin-Small Clamp;
* Small Clamp-Large Clamp;
* Large Clamp-Bar;
* Nut-Small Clamp.  
The tightening torque of the nut and bolt were varied to observe changes in the behavior between the interfaces. Nuts were tightened to 6 Nm, 8 Nm, 10 Nm, and 12 Nm using a torque wrench (Norbar TTi50 Torque Wrench, Banbury, UK) prior to testing. The system was preloaded with 5 N and loaded till 60 N, at 10 mm/min.  
Figure 6: Diagram of pin bending test. The schanz pin is fixed on to the instron base using a testing block and the actuator provides a loading perpendicular to the pin axis.  
System Test  
Complete systems with 4 pins fixed to bone analogues, under different configurations were subject to axial compression tests (Figure 8). homopolymer acetal (Delrin (C)) bars were used to represent simplified long bone geometry as they exhibit properties similar to natural bone [49], with measurements in the range similar to that of a tibia. Fractured bone was created by fixing schanz pins in 2 polymer bars (Delrin (c)). Distance between pins and distance between shaft and bone were varied during the tests (Figure 8) (Table 1). Pins were placed in 3 specific positions on each bone analogous shaft with the distance to the center of the fracture site varying between 45 cm, 90 cm, and 135 cm. The system was preloaded with 20 N and loaded till 200 N, at 10 mm/min.  
Figure 8: **Top**: Diagram of system test. **Bottom**: Pin placement options used in the system test. positions 1, 2, 3 are 45, 90 and 135 mm from the fracture site, respectively.  
Figure 7: Diagram of interface test. The schanz pin is fixed on to the instron base using the 2 clamps used in the pin clamp assembly. The actuator provides a loading perpendicular to the pin axis.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 5 Real-World Evaluation
### Assessing the Mechanisms That Control the Adaptation Process
Figure 9: Network lifetime during sub-network detection. In the next experiment, the performance is investigated in combination with duty cycling and how it can help improve the overall network lifetime of the IoT deployment. The same six devices are used, deployed in the same locations. This set of experiments demonstrates that duty cycling can considerably improve the network lifetime without affecting the performance of the sub-network detection mechanism. Figure 10 indicates the network lifetime as recorded by the gateway device. As is evident in Table 1, for a duty cycling rate of 50%, the network's lifetime is increased by 88.5% on average, while the decrease of rate down to 30% extends the lifetime at 172%. An important point we have to note here is that the actual duty cycling rate achieved differs from the targeted rate. This happens mainly because interruption-like timer events are executed even when the device is set to sleep mode. Figure 11 shows exactly how, for extremely low duty cycling rates (<50%), the actual rate is higher than requested. This fact explains how the operation of higher protocols is adversely affected in such conditions. Figure 11 shows how the duty cycling service actually operates and achieves the requested timings. This is a requirement as the timers scheduled by our protocols and any external interruptions can cause the device to wake-up and continue its operation while the timer routine is served. As each function call is processed in a few milliseconds, this may actually seem of minimal impact, but, as it is obvious for extremely low duty cycle rates, we fail to actually conserve that much. \begin{table}
\begin{tabular}{c c c} \hline \hline
**DC** & **Lifetime** & **Extension** \\ \hline
100\% & 1709 min & - \\
80\% & 1861 min & 8.9\% \\
70\% & 2035 min & 19\% \\
50\% & 3222 min & 88.5\% \\
30\% & 4662 min & 172\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network lifetime during sub-network detection. Figure 10: Effect of duty cycling on network lifetime during sub-network detection.
# Safety-Aware Optimal Attitude Pointing for Low-Thrust Satellites
## 3 Results  
We applied the steps described in Section 2 to create attitude maneuvers for a geostationary satellite from initial configuration \(R_{0}\) to final configuration \(R_{D}\). We show two examples:  
\[\text{Path 1}:\quad R_{0}=\begin{bmatrix}-0.9933&0.0997&-0.0584\\ 0.0998&0.9950&0\\ 0.0581&-0.0058&-0.9983\end{bmatrix},\quad R_{D}=\begin{bmatrix}0.5817&0.6517&0.4 868\\ 0.8134&-0.4660&-0.3481\\ 0&0.5985&-0.8011\end{bmatrix} \tag{22}\]  
\[\text{Path 2}:\quad R_{0}=\begin{bmatrix}-0.6504&0.0653&-0.7568\\ 0.0998&0.9950&0\\ 0.7530&-0.0756&-0.6536\end{bmatrix},\quad R_{D}=\begin{bmatrix}0.21078&0.9761 &0.0525\\ 0.2947&-0.0123&-0.9555\\ -0.9320&0.2170&-0.2903\end{bmatrix} \tag{23}\]  
Rather than simply plotting these two maneuvers, we ran a series of simulations on an incremented set of the final position of Paths 1 and 2 to create a class of curves starting from the initial point \(R_{0}\) and going to a set of perturbations of the end point \(R_{D}\). This showed that the method successfully adjusted each curve in the class to remain within the pointing set, rather than showing a single curve from \(R_{0}\) to \(R_{D}\), which could represent an exceptional case.  
The incremented sets of the final positions for Paths 1 and 2 are given by:  
\[\mathcal{R}_{1}=\{\exp(A_{2}p_{i})R_{D}\mid p_{i}=-0.5+i/18,i\in[0,1,\dots,9]\},\]  
\[\mathcal{R}_{2}=\{\exp(-2A_{3})\exp(\frac{1}{2}A_{1})\exp(\frac{\pi}{2}A_{2}) \exp(A_{1}p_{i})R_{D}\mid p_{i}=-0.2+i/22.5,i\in[0,1,\dots,9]\}\]  
where the rotation matrix used to determine the iterations was chosen such that the curves in the set from the initial point \(R_{0}\) to the final points in the sets \(\mathcal{R}_{i}\) would violate the conditions stated for each path.  
The plots of the maneuvers can be seen in Figures 3(a),b and 4(a),b. Running 10 examples also allowed us to calculate the average CPU time for each of the two cases: for Path 1, the average CPU time was 1.437 s, and for Path 2, the average CPU time was 2.113 s. The CPU time was calculated as the sum of the CPU time for the MATLAB script used to calculate the curves in Figures 3(a) and 4(a) (using Steps 1-3), the Simulink run to determine the new \(c_{i}\) values, and then, the second MATLAB script used to calculate the curves in Figures 3(b) and 4(b) (using Steps 1-3).  
We chose Path 1 where the original optimal trajectory could be seen to cause the telescope to pass across the bright spot \(S\), while in Path 2, the original optimal trajectory designed for the antenna did not fall in the desired pointing region. One can see from the adapted trajectories in Figures 3(b) and 4(b) that the \(c_{i}^{0}\) values were successfully updated in order to point the telescope \(\mathbf{a}(t)\) away from the forbidden region at all times while maintaining the communications antenna curve \(\mathbf{b}(t)\) in the desired region.