esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Hardware and Software for Data Protection
### Types of IDS
#### 6.3.3 Anomaly-Based Detection  
Anomaly detection is designed to uncover abnormal patterns that deviate from what is considered to be normal behaviour, whereas IDS establishes a baseline of normal usage patterns and anything that widely deviates from it gets flagged as a possible intrusion. An example of this would be if a user logs on and off of a machine eight times a day instead of the normal one or two. Additionally, if a computer is used at 2:00 a.m. when normally no one outside of business hours should have access, this should raise some suspicions. Anomaly detection can investigate user patterns, such as profiling the programs executed daily. Once again, if a user in an IT department suddenly starts to access accounting programs or recompiles them, then the system must immediately raise an alarm or alert its administrators. The major benefit of anomaly-based detection methods is that they can be very effective at detecting previously unknown threats [37].  
Through the idea and introduction of Machine Learning in the anomaly detection system, in the first stage of a deployment of an anomaly-based IDS, the system learns what constitutes normal behaviour. The controlled system is running as usual under the assumption that there are no abnormal behaviours. During the learning stage, no attack must occur in the controlled system so that the IDS does not learn to ignore the attacks. The learning process can be addressed by a variety of means such as Machine Learning or building statistical behavioural profiles.  
In the second stage of the deployment, in which the system possibly faces attacks, the IDS monitors the activities in the controlled system and compares them to the learned normal behavioural patterns. If a mismatch occurs, a level of \"suspicion\" is raised with the IPS and when the suspicion, in turn, trespasses a given threshold, the system triggers an alarm. The main advantage of anomaly detection is that it does not require prior knowledge of intrusion and can thus detect new intrusions. The main disadvantage is that it may not be able to describe what an attack is and may have a high false positive rate.  
One other such research by [PERSON], [PERSON], et al. (2020), to deal with more concise training data using more current datasets such as CICIDS 2017 and the UNSW-NB 2015 datasets (more recent to DARPA, as mentioned earlier), proposes a novel multi-stage optimized ML-based NIDS framework. According to this study, this method reduces computational complexity while maintaining its detection performance. The model results showed a significant reduction in the required training sample size. Detection accuracies were over 99% for both datasets, outperforming recent literature works lowering false alarm rates [38].  
The next sub-section goes deeper into the algorithms that are used in the modelling of the detection methods.
# Combining Gamification and Augmented Reality to Raise Interest in Logistics Careers
## 2 Theoretical Background and Practical Setting  
### Augmented Reality  
Augmented Reality (AR) is traditionally seen as part of the Reality-Virtuality Continuum [28] and typically refers to applications that superimpose virtual content over a real-world camera feed, thus providing a hybrid experience. To integrate this virtual content into the real-world, printed visual markers are frequently used to serve as anchors for digital content, allowing it to be viewed from any angle, appearing as part of the real world. Recent smart devices such as smartphones, tablets, and mixed-reality headsets also often incorporate technologies such as depth cameras and motion-tracking sensors to enable this integration without the use of such visual markers [28; 29].  
AR technology still involves several limitations that need to be considered. AR requires the use of a camera and integrated image processing technologies and is heavily dependent on the quality of the lighting and surrounding environment, just as in the case of any camera. Even when a printed marker is used to serve as a stable anchor for virtual content, reflections and poor lighting can result in poor performance and an unsatisfactory gaming experience. The number of markers that can be utilized simultaneously is limited to the processing power of the device, typically being constrained to five or six simultaneous markers. In addition, any kind of occlusion, such as a hand passing in front of the devices' camera, can briefly disrupt the tracking and potentially interfere with the augmented experience. In addition, most AR technologies simply superimpose virtual objects over the real-world vision, resulting in objects simply appearing over such an occlusion, thereby destroying the illusion of a hybrid world [30].  
As smart devices are becoming more technologically advanced, actual uses of augmented reality beyond the fields of applied research are getting more common, particularly in the fields of mobile gaming, art, education, industrial applications, and logistics [29; 31]. Although educational contexts have primarily utilized digital and/or animated extensions of examples in printed textbooks [32], AR applications can now be found as standalone apps that can be used indoors, outdoors, on table tops, or basically anywhere a smart device with a camera can be used [33]. AR technology has a fairly unique, fascinating quality that can be effectively employed for playful learning activities, allowing users to interact with a tiny virtual world right in front of them [34].
# Fusion Classification Method Based on Audiovisual Information Processing
## 3 Proposed Method
### Feature Extraction
#### 3.3.2 Image Feature Extraction  
In the design of this paper, a 2D CNN is specifically selected to process image data because of its proficiency in capturing spatial features of images. The designed 2D CNN consists of multiple convolutional layers, pooling layers, and fully connected layers. Convolutional layers extract local features from images using different convolution kernels, while pooling layers are used to reduce the dimensionality of feature maps, decrease computational load, and retain important features. Fully connected layers map the extracted features to the target category space, providing a basis for subsequent feature fusion and target classification. This section uses the image features obtained from the aforementioned image as input, denoted as \(X_{img}=(x_{1},x_{2},\dots,x_{n})\). As shown in Equation (7),  
\[\widetilde{X}_{img}=conv2D\big{(}X_{img}\big{)}\in R^{H\times W\times C}. \tag{7}\]  
Among them, \(\widetilde{X}_{img}\) represents the semantic features of the target image, \(conv\) denotes the convolution operation, \(H\) and \(W\) represent the height and width of the image, and \(C\) represents the number of channels in the image.
## 6 Simulation and Evaluation of Results
### Packet Delivery Rate (PDR)  
Figure 10 compares PDR in various approaches. This test shows the successful performance of CTSRD. However, it has a lower PDR than EEMSR (approximately 0.4%) because EEMSR uses the beta distribution to build a trust system, which is more powerful than the W-Trust mechanism presented in CTSRD. Furthermore, E-BEENISH has the weakest performance in terms of packet delivery rate (approximately 8.03% weaker than CTSRD) because it has not designed any security mechanism. As a result, attackers can increase packet loss by disrupting the intra-cluster and inter-cluster connections. To assess the security in IoT, the number of packets received by BS is evaluated in another test shown in Figure 11. If the number of packets received by BS is small, the security of this method is weaker because hostile nodes remove some data packets. According to this figure, EEMSR has the highest number of packets received by BS, and in the proposed method, BS has received approximately 3.15% less packets than EEMSR. Moreover, CTSRD is more successful than E-BEENISH by approximately 14.56%. Another point in this figure is that increasing interactions between IoT nodes over time specifies their trust level more accurately. This allows all routing methods to increase the number of packets received by the BS in the higher rounds.  
Figure 10: Comparison of different methods with regard to the packet delivery rate.  
Figure 9: Comparison of different methods with regard to the standard deviation of energy consumption.
## 2 Methodology for Implementing the SaaS Solution-Coestim
### Benefits of Artificial Intelligence in CEM
With the advent of new technologies for automatic data collection, storage, and analysis, construction industry organizations have increased the amount and types of data they collect, this change leading to a greater interest in machine learning. Due to the importance of the role of cost estimation in a project, different predictive methods have been proposed. Approaches to cost estimation range from statistical-based multivariable regression analysis to machine learning techniques such as Classification and Regression Trees (CART), M5 Tree Model (M5-MT), Artificial Neural Network (ANN), and vector support machines (SVM). * CART is a classification method that uses historical data to build decision trees. A CART model that predicts the value of continuous variables in a set of input variables is known as a regression model. A major advantage of the decision tree-based model is its ability to manage a small data set. However, a disadvantage is that it can produce unstable decision trees. The reason is that the insignificant change in the learning sample could lead to radical changes in the decision tree. * The tree model (MT-tree model) is proven to have the ability to learn effectively and can address high-dimensional regression tasks. Compared to other machine learning techniques, the MT training process is relatively fast and the results are interpretable. * ANN is a viable alternative for forecasting, construction costs and, in practice, has been used to build various cost prediction models. This method eliminates the need to find a mapping relationship that mathematically describes the construction cost based on the input variables. When the influencing factors and the structure of the ANN are specified, the task is to collect a reasonable amount of data for ANN training. However, the process of training ANN-based models is often time-consuming. * In the construction area, SVM was used in cost estimation. The principles are based on structural risk minimization and statistical learning theory. SVM-based models also involve the identification of influencing factors, data sample collection, and the training/testing process. After establishing the mapping function, the model is able to predict the future value of the project cost. The advantages are widely known for accurate prediction ability. However, the SVM training process involves high computational costs.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 4 Active Barriers
### Co-Based
#### 4.4.1 Polyamide Sacrificials
Research by [PERSON] has indicated that Co could complex with MXD6, while other metals could not [171]. Figure 12 shows the proposed formation of a complex between the nylon and the cobalt and its assumed activity as an initiating catalyst. Cobalt captures and activates triplet oxygen by reducing it to a superoxide. As a propagation catalyst, it is assumed to perform deperoxidations, following the Fenton reaction (Equation (2)) [171; 172]. Figure 11: Possible oxidation mechanisms for poly(m-xylylene adjamide) (MXD6). Only a limited amount of possibilities is shown due to the complexity of the radicalar mechanism. The different steps have been named (**a–i**) as a reference to the text. The steps where cobalt might be present as a catalyst have been marked with (Co), with is an initiation catalyst and p as a propagation catalyst [171; 172; 178]. Figure 12: Proposed mechanism of cobalt as initiation catalyst for nylon MXD6 oxidation [172]. Several patents state the presence of an induction time on the reaction [118; 179]. Conform to the autoxidation hypothesis, this has been suggested to be due to the initial build-up of hydrogen peroxide before the subsequent propagation phase. Multiple successful attempts have been made to lower this induction time by adding extra photosensizers or charging the polymer with highly energetic radiation [58; 180; 181; 182; 183]. All of these solutions are intended to boost radical initiation. Recently, low molecular weight variants of MXD6 have been marketed. These monomeric components are much better distributed inside the PET-matrix. The result of this new technology is the manufacture of clear bottles without any loss in oxygen scavenging [184; 185; 186]. To provide visually appealing bottles with high activity otherwise, MXD6 can be processed as a multilayer solution with a completely clear wall. This production method is more expensive than the monolayer version, but it is possible to add a much higher sacrificial content in the active layer, providing a scavenging capacity ranging over multiple years [82; 187]. For this reason, it is a very attractive barrier for further development in the high-end application fields. Organic electronics can be protected with zero oxygen ingress and as films of more than three layers can easily be manufactured, higher concentrations than in bottles may be employed. The main issue here will be the simultaneous protection against water vapor since these barrier types require water for their scavenging reaction. A well-defined organization of the layered materials will be necessary.
## 2 Nonlinear Distributed-Parameter Model of the MSR Plant
### Modeling of the Reactor Core  
#### 2.1.1 Neutron Kinetics Model  
The nonlinear model of the core is established based on the point-reactor kinetics in Equations (1) and (2). The drifting effect of the delayed neutron precursors is considered by the two additional terms (the third and fourth terms) on the right-hand side of Equation (2): one for the precursor loss by leaving the core and another for the precursor gain by re-entering the core [29].  
\[\frac{dN(t)}{dt}=\frac{\rho(t)-\beta}{\Lambda}N(t)+\sum_{j=1}^{2}\lambda_{j}C _{j}(t) \tag{1}\]  
\[\frac{dC_{j}(t)}{dt}=\frac{\beta_{j}}{\Lambda}N(t)-\lambda_{j}C_{j}(t)-\frac{ C_{j}(t)}{\tau_{c}}+\frac{C_{j}(t-\tau_{L})}{\tau_{c}}e^{-\lambda_{j}\tau_{L}}; \quad j=1,2 \tag{2}\]  
The term for overall reactivity \(\rho(t)\) includes the initial reactivity, the reactivity introduced by temperature variations in the fuel salt and graphite, and the reactivity inserted by the control rod:  
\[\rho(t)=\rho_{0}+\alpha_{f}(\overline{T}_{f}(t)-\overline{T}_{f}(0))+\alpha_{ g}(\overline{T}_{g}(t)-\overline{T}_{g}(0))+\rho_{rad} \tag{3}\]  
Figure 2: The schematic diagram of the discretized system model.  
The consideration of steady state in the system makes the terms representing the rate of change with respect to time in both Equations (1) and (2) go to zero, so the initial reactivity \(\rho_{0}\) can be obtained by using the following equation:  
\[\rho_{0}=\beta-\sum_{j=1}^{2}\frac{\lambda_{j}\beta_{j}}{\lambda_{j}+(1/\tau_{ \mathrm{c}})(1-e^{-\lambda_{j}\tau_{\mathrm{L}}})} \tag{4}\]  
The overall thermal power is considered to be proportional to the neutron density calculated using the point-reactor kinetic equations, and the core power distribution is assumed to have a cosine shape with an extrapolated distance of 2.4 m [30]. The relative heat deposited in the fuel salt is 91.9%, and the rest is given to graphite [29].
# System for Evaluation and Compensation of Leg Length Discrepancy for Human Body Balancing
## 3 Mathematical Model of the System
### Forward Kinematics of the 3-RPS Parallel Manipulator  
The solution to the forward kinematic problem (FKP) for the 3-RPS parallel manipulator can be found by determining the position and orientation (\(\alpha\), \(\beta\), \(z\)) of the moving platform for given lengths of linear actuators \(d_{i}\). In accordance with Figure 6, the position of spherical joints in the frame {A} can be described as follows:  
\[{}^{A}\mathbf{q}_{1}=\left[\begin{array}{c}r_{A}+d_{1}\cos\phi_{1}\\ 0\\ d_{1}\sin\phi_{1}\end{array}\right],{}^{A}\mathbf{q}_{2}=\left[\begin{array}[] {c}-\frac{r_{A}}{2}-\frac{d_{2}\cos\phi_{2}}{2}\\ \frac{\sqrt{3}(r_{A}+d_{2}\cos\phi_{2})}{2}\\ d_{2}\sin\phi_{2}\end{array}\right],{}^{A}\mathbf{q}_{3}=\left[\begin{array}[] {c}-\frac{r_{A}}{2}-\frac{d_{3}\cos\phi_{2}}{2}\\ -\frac{\sqrt{3}(r_{A}+d_{3}\cos\phi_{2})}{d_{3}\sin\phi_{3}}\end{array}\right] \tag{8}\]  
which gives the distance between the vertices of the equilateral triangle \(B_{1}B_{2}B_{3}\)[26; 27]:  
\[\begin{array}{l}|B_{1}B_{2}|=\sqrt{3}r_{B}\Rightarrow\left|{}^{A}\mathbf{q}_ {1}-{}^{A}\mathbf{q}_{2}\right|=\sqrt{3}r_{B}\\ |B_{2}B_{3}|=\sqrt{3}r_{B}\Rightarrow\left|{}^{A}\mathbf{q}_{2}-{}^{A}\mathbf{ q}_{3}\right|=\sqrt{3}r_{B}\\ |B_{3}B_{1}|=\sqrt{3}r_{B}\Rightarrow\left|{}^{A}\mathbf{q}_{3}-{}^{A}\mathbf{ q}_{1}\right|=\sqrt{3}r_{B}\end{array} \tag{9}\]  
By squaring and rewriting Equation (9) in implicit form, a system of nonlinear equations is created. The solution to the system of nonlinear equations are unknown angles \(\phi_{1}\), \(\phi_{2}\) and \(\phi_{3}\) with which the positions of spherical joints can be calculated. By substituting the angles \(\phi_{1}\), \(\phi_{2}\) and \(\phi_{3}\) into Equation (8), the position vector of the center of the moving platform \({}^{A}\mathbf{p}\) in the frame {A} can be found with the following expression:  
\[{}^{A}\mathbf{p}=\left({}^{A}\mathbf{q}_{1}+{}^{A}\mathbf{q}_{2}+{}^{A} \mathbf{q}_{3}\right)/3 \tag{10}\]  
The position of the force plate can be found by substituting Equation (10) into Equation (6) while the orientation of the moving platform can be determined with the following expression [26]:  
\[\begin{array}{l}\alpha=\text{atan2}(v_{z}/\cos\beta,w_{z}/\cos\beta)\\ \beta=\sin^{-1}\left(-u_{z}\right)\\ \gamma=\text{atan2}(u_{y}/\cos\beta,u_{x}/\cos\beta)\end{array} \tag{11}\]where \(u_{x}\), \(u_{y}\), \(u_{z}\), \(v_{z}\) and \(w_{z}\) are elements of the rotation matrix \({}^{A}\mathbf{R}_{B}\).
## 3 Future CONERSTONE Platforms
### Silicon Nitride
Many of the demonstrated devices use layers with a thickness of 300 nm and a stoichiometric composition (\(n=2.0\)) due to the readily availability of both low pressure chemical vapor deposition (LPCVD) layers with considerably low losses and low-temperature (<400 \({}^{\circ}\)C) plasma enhanced chemical vapor deposition (PECVD) films more suitable for multilayer integration. This 300 nm thick stoichiometric silicon nitride will become the standard CONERSTONE thickness for MPW calls,with a 3 \(\upmu\)m thick silicon dioxide under-cladding. Using 300 nm thick PECVD silicon nitride layers, we realized single-mode waveguides fabricated by e-beam lithography with propagation losses below 1 dB/cm in the O-band and close to 1.5 dB/cm in the C-band, as in Table 4[116]. A detailed comparison of silicon nitride technologies available at foundries and research groups around the world can be found in a publication by [PERSON] et al. [90]. Moreover, we demonstrated a library of devices working in the O and C telecom bands that are crucial for the fabrication of more complex PICs, including directional couplers, multimode splitters and Mach-Zehnder interferometers (see Figure 14). In particular, we demonstrated 4-channel (de)multiplexers based on angled multimode interferometers with insertion losses <2.0 dB, crosstalk below 20 dB and a high tolerance to temperature variations with wavelength shifts below 10 pm/\({}^{\circ}\)C. Finally, we contributed to the realization of fiber-to-chip apodized grating couplers with a staircase teeth profile that exhibited a high directionality with coupling losses of only 1.5 dB and a 3 dB bandwidth of 60 nm in the C-band [117].
# VoI-Based Optimization of Structural Assessment for Spatially Degrading RC Structures
## 4 Case Study: A Simply Supported RC Girder Bridge  
### Problem Description and Probabilistic Models  
In this case study, a reinforced-concrete (RC) girder bridge is considered as described in [18; 27; 28] and illustrated in Figure 2. For more information on the bridge geometry, the reader is referred to [18; 27; 28]. The bridge is subjected to chloride-induced corrosion, which leads to a reduced resistance over time.  
The probability of failure \(P_{f}(t)\) and the corresponding reliability index \(\beta(t)\) are quantified based on the bending limit state given by Equation (5). The probability of failure can be used to evaluate the cumulative probability of failure \(p_{F}(t)\) mentioned in Figure 1. Based on this cumulative probability of failure, the cost of failure at time \(t\) (\(C_{F}(t)\)) can be calculated (Figure 1).  
\[g(\theta,t)=K_{R}\text{min}\bigg{(}A_{s}(t)f_{y}\bigg{(}h-a-\frac{h_{f}}{2} \bigg{)};f_{c}\bigg{(}h-a-\frac{h_{f}}{2}\bigg{)}b_{f}h_{f}\bigg{)}-K_{E}(Q+P) \tag{5}\]  
In Equation (5), \(A_{s}(t)\) is the reinforcement area at time \(t\), which changes over time due to corrosion (see Section 4.2), and \(P\) represents the load effects due to permanent loads, which are among others a function of the concrete density \(\rho_{c}\) (see Table 1). For the meaning of the other variables in the limit state function and their probability distributions, the reader is referred to Table 1. The characteristic values of the load effects relate to a traffic load according to the Eurocode [29]. The reference period for the probabilistic load models and hence for the resulting probability of failure is equal to one year.  
Figure 2: The superstructure of the bridge under investigation (dimensions in mm).