esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 6 Results Using the Spoofing Detection Algorithm
### Numerical Results for Detection and Latency
The latency reported here is assuming that the spoofer starts modifying its delay at the time of 110 s of each dataset as reported in [38]. For scenarios ds4 and ds6, which are modifying the position according to their selected pattern, each satellite will be modified \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Outcome**} \\ \hline \multirow{3}{*}{**True**} & & **Spoofed** & **Impairment** & **Clean** \\ \cline{2-5}  & Spoofed satellite & 22 & 0 & 2 \\ \cline{2-5}  & Clean Scenario & 0 & 0 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for the two types of datasets processed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Base ECADLL**} & \multicolumn{2}{c}{**ECADLL w\(\backslash\)RM**} & \multicolumn{2}{c}{**Improvement**} \\ \hline  & \(DL_{base}\) **(s)** & \(CT_{base}\) **(min)** & \(DL_{RM}\) **(s)** & \(CT_{RM}\) **(min)** & **DLI (\%)** & **CTI (\%)** \\ \hline clean & - & 35 & - & 21 & - & 40 \\ ds3 & 27 & 38 & 17 & 35 & 37 & 7.9 \\ ds6 & 33 & 36 & 17 & 33 & 48.5 & 8.3 \\ ds7 & 26 & 41 & 80 & 33 & 57.5 & 19.5 \\ ds4 & 93 & 38 & 98 & 30 & 5.1 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical results for improvement of detection delay and computational load. Legend: DL = Detection Latency. CT = Computational Time. DLI = Detection Delay Improvement. CTI = Computational Time Improvement. with a different delay, unknown to us. According to our analysis, the modification of the delay for ds6 occurs closer to the 110 s than the modification for ds4. It is interesting to notice the significant improvement in latency obtained when considering ds7. Given the slower rate at which the spoofer modifies the delay with respect to the other scenarios, we get a longer detection time when only relying on the tracking lock of Unit1. When using the ratio metrics, we see that we have a much lower latency, and we are able to flag the presence of impairments in a quicker way. Values in Table 3 give us the numerical results on how the architecture performance was improved by the introduction of the RM as a simple additional tool for the monitoring block. It is also important to notice that the detection latency is not necessarily referring to a spoofer classification by the algorithm, but it represents the initial warning that the RM gives to the receiver for it to not trust the signal coming from that satellite, until it can be correctly classified or corrected by means of ECADLL.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 4 Applying Corrections to AD/NS Simulation
### Application of Glauert Tip Loss Factor \(F\)
#### 4.1.3 Glauert-C Correction  
[PERSON] et al. [43] performed their correction by directly using Equation (26) which represents the physical meaning of factor \(F\). Using the azimuthally uniform condition (\(\overline{a}=a\)), the correction was completed by replacing Equations (9) and (10) with  
\[\tilde{a}=a_{B}=a/F, \tag{54}\]  
\[\tilde{a}^{\prime}=a^{\prime}_{B}=a^{\prime}/F. \tag{55}\]  
Similar to the Glauert-A correction, this implementation involves a division operation with denominator \(F\). That causes an unphysical big value of \(\tilde{a}\) at the extreme tip where \(F\to 0\). A limiter is set in the present study to force the result to be \(\tilde{a}=1\) when it is greater than 1. The value of \(\tilde{a}^{\prime}\) is usually much less than \(\tilde{a}\) and is not limited here.
## 4 Conclusions  
1. The Tresca (found to be better suited) and Von Mises criteria are more correct (qualitatively) than the other three criteria in both intact and reduced periodontitis in the study of stress distribution in stainless-steel brackets.
2. Qualitatively, the Tresca and Von Mises criteria simulations with 0.5 N of force and five orthodontic movements generated the maximum stress areas in the attachment side and the entire base of the bracket, confirming non-homogenous stress distribution areas and risks of bond failure.
3. Qualitatively, for all five failure criteria, maximum stress areas were displayed on the applied force site/surface, on the internal surface of the bracket, and in the enamel around bracket area in a non-homogenous manner.
4. Quantitatively, all five types of failure criteria showed comparable results, with higher amounts of stress displayed on the bracket (internal surface and force appliance site/surface) when compared with the enamel component.
5. All five failure criteria quantitatively showed rotation and translation, closely followed by tipping to be the most stressful movements.
6. In all five failure criteria's simulations rotation, translation and tipping movements showed slightly lower quantitative values for 8 mm bone loss when compared with intact periodontitis, while intrusion and extrusion has shown the opposite behavior (i.e., a slight increase).
7. The periodontal breakdown has little to no influence over the stress display in the bracket and surrounding enamel area for all five orthodontic movements.  
**Author Contributions:** Conceptualization: R.A.M.; Methodology: R.A.M.; Software: R.A.M., S.M.B. and M.D.B.; Validation: R.A.M., A.G.D., S.M.B., M.D.B. and C.D.O.; Formal analysis: R.A.M., M.D.B. and S.M.B.; Investigation: R.A.M. and S.M.B.; Resources: R.A.M.; Data Curation: R.A.M.; Writing--original draft preparation: R.A.M.; Writing--review and editing: R.A.M., A.G.D. and C.D.O.; Visualization, Supervision, and Project administration: R.A.M., A.G.D. and C.D.O.; Funding acquisition: R.A.M., A.G.D. and C.D.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors were the funders of this research project.  
**Institutional Review Board Statement:** The research protocol has been approved by the Ethical Committee of the University of Medicine (158/2.04.2018).  
**Informed Consent Statement:** Informed oral consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 2 Modeling and Research Methods  
This study employed complex networks as a modeling tool to capture the dynamic behavior of the CATN and investigated its properties. Robustness measurements were utilized to quantify the network's structure and its ability to withstand potential disruptions.
## 4 Conclusions  
In this work, systematic FE simulation of the WAAM deposition was conducted with four different deposition patterns and travel speeds. The underlying mechanism is reported on how such process parameters affect the thermal field and cooling rate, and eventually how the residual stress and warpage are generated from such thermal profiles. The major findings are as follows:  
Analysis shows that the out-in spiral deposition pattern leads to the highest value of residual stress (244 MPa), while the in-out deposition generates the lowest residual stress (183 MPa).  
In the case of the line-type patterns (alternate and raster), the choice of deposition pattern has little influence on thermal profiles, residual stress generation, and warpage. However, the contour-type patterns (out-in and in-out) significantly affect the heat transfer, thermal gradient, and cooling rate. Therefore, the deposition pattern plays a crucial role in the determination of the levels of warpage and residual stress.  
The results demonstrate that the deposition pattern hardly changes the peak temperature, while the heat accumulation/dispersion and cooling rate are significantly affected by the deposition pattern.  
Compared with the line-type deposition patterns, the spiral deposition patterns lead to higher levels of warpage and residual stress. This is mainly attributed to the fact that the spiral pattern induces heat accumulation and a slow cooling rate due to the characteristics of the paths.  
The deposition travel speed changes the peak temperature. Analyses show that an increase in the travel speed leads to a reduction in heat accumulation during the deposition, which results in a lower level of residual stress generation.  
Findings in the present study could be served as the reference for the design of the WAAM process. In addition, they can also be extended or applied to the related research, for example, for the establishment of a tool-path strategy for the components with the complex geometry.  
This work was supported by Incheon National University Research Grant in 2023 (2023-0348).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 11: Maximum Mises stress for the different travel speeds.
# Direct Numerical Simulation of Turbulent Boundary Layer over Cubical Roughness Elements
## 4 Summary and Conclusions  
Turbulence statistics were investigated in turbulent flow over urban-like terrain. DNS was performed on a TBL over a staggered array of 3D cubic roughness elements, which was applied over \(x/\delta_{0}=700\) at the roughness step change. The turbulence statistics were averaged at \(Re_{\tau}=816\) and compared with the results of experiments and DNS for validation. The magnitude of \(\Delta U^{+}\) is 10.15, leading to \(k_{s}{}^{+}=238.3\)[5] in the fully rough region [22; 70]. Here, \(k_{s}\) is the sand-grain roughness height. Reversed flows at upstream and downstream elements indicate that the present flow is under the wake interference flow regime [24], similar to k-type roughness [6; 23]. Interestingly, the logarithmic variations in streamwise and spanwise Reynolds stresses and the plateau in Reynolds shear stress were clearly observed, reminiscent of [PERSON]'s attached-eddy hypothesis. Strong energy at long wavelengths was found near the top of the elements, and the energy extended to small scales, consistent with two-scale behavior [25]. The extension of energy from large scales at the top of the elements to adjacent smaller scales could be related to amplitude modulation [29; 68; 73; 74]. In addition, a quadrant analysis of Reynolds shear stress was employed. The contributions of ejection and sweep events to Reynolds shear stress suddenly changed near the top of the elements, related to low-momentum regions and hairpin vortex packets [31; 33]. The area fraction and magnitude of each quadrant are similar to those of a TBL with a smooth wall in the outer region, supporting [PERSON]'s outer-layer similarity. The transport equation of Reynolds stress was conditionally averaged. The budgets of TKE were influenced by the elements, and in particular, the role of pressure transport in energy transfer was important. Streamwise energy was primarily reduced at upstream elements. This energy was then transferred in two directions: to the wall-normal direction beside the elements and to the spanwise direction. The spanwise transfer occurred over the elements and at upstream elements near the wall. Investigating turbulence structures and their interactions would be crucial for understanding the mechanism behind energy and substance transfers in urban boundary layers.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1053438).  
Not applicable.  
Data are contained within the article.  
The authors
# Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System
## 1 Introduction
Section 2 starts with an introduction of the system model, and then the STBC is described. Section 3 introduces two conventional channel estimation methods, which will be used for comparison analysis with the proposed channel estimation method. Section 4 illustrates the proposed SVD channel estimation method. Experimental results of the proposed method are presented in Section 5. Conclusions are given finally in Section 6.
## 3 Problem Formulation  
Multi-UAV task planning requires a group of \(N\) UAVs with holonomic or non-holonomic constraints to perform the assigned tasks and achieve their targets according to the corresponding trajectory while avoiding collisions with each other and with obstacles in the environment. In particular, at each time step \(t\), each UAV \(U_{i}\in\mathcal{U}(0\leq i\leq N-1)\) receives its position \(p_{i,t}^{U}\). The targets \(T_{i}\in T(0\leq i\leq N_{T}-1)\) and obstacles \(D_{i}\in D(0\leq i\leq N_{D}-1)\) are distributed across it. The position of the target \(T_{i}\) is denoted by \(p_{i,t}^{T}\), while the obstacle \(D_{i}\) is given by \(p_{i,t}^{D}\). For each UAV \(U_{i}\), a trajectory \(\{T_{i}\}\) is planned and given by Equation (2) and the length of \(\{T_{i}\}\) is given by \(d_{i}\).  
\[T_{i}=\left\{p_{i,D}^{U},p_{i,1}^{U},\cdots,p_{i,n}^{U}\right\}, \tag{2}\]  
The optimization goal of the task planning is \(\min\left(\sum_{0}^{N-1}d_{i}\right)\) and it must satisfy the following constraints:  
1. Each target \(T_{i}\) is assigned to one UAV, i.e., \(\cup_{i=0}^{N-1}T_{i}=T,i\in\{0,\cdots,N_{T}-1\}\).
2. Each target is only assigned to one UAV, i.e., \(\forall i\
eq j,T_{i}\
eq T_{j}i,j\in\{0,\cdots,N_{T}-1\}\).
3. Each UAV's path is collision-free with obstacles, i.e., \(\forall i,jD_{i}\
otin T_{j}\in\{0,\cdots,N_{D}-1\}\), \(j\in\{0,\cdots,N-1\}\).
4. Each UAV's path is collision-free with other UAVs, i.e., \(\forall i,j,p_{i,t}^{U}\
eq p_{j,t}^{U}\) at any time \(t\).  
Meanwhile, in the dynamic environment, the number of UAVs \(N\), the number of targets \(N_{T}\), and the number of obstacles \(N_{D}\) are variables.
# Ionic Liquid-Modified Porous Organic Polymers as Efficient Metallocene Catalyst Supports
## 3 Results and Discussion
### IR, TGA and SEM Analysis of IL-POPs  
The grafting of [BVIM] [Cl], HEMA and DVB was confirmed by the typical functional group signals from FT-IR spectrums. As shown in Figure 3a, the bands at 1170 cm\({}^{-1}\) and 1652 cm\({}^{-1}\) were attributed to C-N and C=N stretching vibration of imidazole group, respectively. Additionally, the peak at 1724 cm\({}^{-1}\) assigned to the stretching vibrations of the carbonyl groups in the HEMA functional comonomer, verifying the ternary polymerization of HEMA, [BVIM] [Cl] and DVB [30; 31].  
Thermal gravimetric analysis (TGA) was used to measure the decomposition temperature of the IL-POPs. As shown in Figure 3, the prepared supports exhibit great thermal stability, the major weight loss occurs above 250 \({}^{\circ}\)C for POP-8 and POP-1. Surprisingly, the other supports dramatically losing weight until 400 \({}^{\circ}\)C. As shown in Table 1, these particles still maintain porosity after high-temperature processing, which provides further evidence for good thermal stability of the IL-POPs.  
According to the SEM images (Figure 4) of IL-POPs, the prepared supports are deposited by submicron spheres and thus are highly porosity enough to immobilize metallocene. To some extent, the ionic liquid-modified POPs are verified to be a potential candidate for metallocene immobilization.  
Figure 3: (**a**) FT-IR spectra of [BVIM] [Cl], POP-1 and POP-8; (**b**) Thermal gravimetric analysis (TGA) curves of ionic liquid-modified POPs.
# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 4 Result and Discussion
### Training Details
#### 4.2.1 Image Brightness Transformation Based on Gain Compensation  
Figure 8 shows the training details on the original and three augmented datasets using the gain compensation strategy. It can be seen that four types of loss functions on the verification set decrease when the training epochs grow. In addition, when the original datasets were augmented, the loss functions would decrease too, especially the _cls_loss_ function trained on eight times datasets decreases rapidly compared to other datasets. At the same time, we used precision, recall, and mAP to evaluate the performance of different augmented datasets. The precision, recall, and mAP have been fluctuating during the model training on the original datasets. When the training datasets were augmented two times, four times, and eight times, these three metrics gradually become stable, proving that the augmented datasets can increase the robustness of the training model. The values of recall, mAP_0.5, and mAP_0.5:0.95 of the model were improved as the datasets increased, while the values of precision reached the maximum on the four times datasets. The gain compensation strategy considers the effect of gain on radar imaging, which can augment the datasets by simulating the EW propagation through different mediums. Therefore, this strategy can improve the value of recall very well but not always improve the value of precision when the datasets increase.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Configuration** \\ \hline System environment & Linux \\ GPU & NVIDIA GTX 1080 Ti \\ GPU acceleration & CUDA 10.1 \\ Training framework & Pytorch 1.12.1 \\ Language & Python 3.9.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The configuration of the environment.
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion
### Comparative Analysis
#### 4.2.3 Lifetime of the Network  
The comparison of MS-LEACH, LEACH-based IDS mechanism, and the proposed MIDS algorithm for network lifetime is shown in Figure 12. The reference paper has 2% more network lifetime than the MS-LEACH but the performance is not effective. The CH selection process in this algorithm is done randomly where the chances of the attacker node selected as CH are higher. The MS-LEACH algorithm has the worst performance out of all. Due to the cryptographic key mechanism, the nodes have a very high communication overhead which leads to the earlier death of the nodes. The proposed MIDS algorithm has almost 62% more life span than the other two techniques. The MIDS algorithm uses an efficient way of CH selection which makes the network secure and long-lasting. MIDS algorithms, through less energy drain, make the sensor nodes alive for a longer time which extends the network lifetime.  
From comparative analysis, it is depicted that the proposed MIDS algorithm has 43% less energy consumption than the other two techniques due to the resourceful selection of the CH mechanism and the launching energy consumption model at each level during the communication phase. After CH selection, the intrusion detection agent actively responds to the attacker node which also avoids energy loss by the attacker. This less energy drain makes the nodes alive which increases the processing rounds and hence the overall lifetime of the network by 62%. Similarly, the average network throughput is increased by 65% through proficiently combating the packet drop attacks as shown in Table 2.  
Figure 11: Throughput of the network.
## 2 Background
### Cybersecurity in IoUT
An example of the implementation of such a framework (National Institute of Standards and Technology--NIST framework, [[https://www.nist.gov/cyberframework](https://www.nist.gov/cyberframework)]([https://www.nist.gov/cyberframework](https://www.nist.gov/cyberframework)), accessed on 20 November 2022) in UUVs' functions is depicted in Figure 3.  
Typically, the sequence is attack-defense-attack, and the attacker is ahead of security and intrusion detection systems (IDS). The most remarkable security challenges in IoUT  
Figure 3: Implementation of NIST cybersecurity framework during a DoS attack. are (a) node compromise, (b) routing attacks, (c) denial of service attacks [18]. However, network sniffing [55], a method of reconnaissance which is described as capturing passively information about a network environment, is the origin of every disastrous cyberattack. Being impactful especially for military operations due to the high need of confidentiality for sensitive information, this method is achieved by eavesdropping (sniffing) traffic of packets, when transmitting data over a wired or wireless network. Subsequently, regarding the underwater domain, adversaries can potentially reveal critical information about the location of an underwater asset or a surface platform. A devastating attack, which can be executed despite the presence of protection mechanisms, such as multi-factor authentication and strong encryption algorithms [18], is the denial of service (DoS) Attack and distributed denial of service (DDoS) attack; a network layer (Layer 3 of the OSI--Open Systems Interconnection model) attack, but also in the application layer (Layer 7 of the OSI model). The attacker floods the network with huge amounts of queries, forcing devices to consume resources, preventing regular traffic from reaching its destination. In a complex and hazardous environment such as the IoUT, in which interoperability between heterogeneous assets is critical, a DoS attack could cause tremendous results. Consequently, a swarm of UUVs would be unable to receive information from the central platform or redistribute proper information to the rest of the units, affecting their adaptive decision-making and path-planning capabilities. Moreover, in [56], a very common and effective physical layer attack, known as jamming, is described in detail. The aim of this attack is the disruption of communication channels between hosts, in underwater domain nodes, or between other underwater assets. Additionally, due to dependence from the energy factor in IoUT, jammers are becoming even more dangerous. Their ability to reduce the lifetime of nodes by forcing them to unnecessarily transmit packets of traffic continuously, causing congestion and latency, allow attackers to achieve their goal efficiently. By simulating jamming in a realistic channel and considering that the jammer has a limited lifetime and the same characteristics as a friendly node, we conclude that this attack is nearly always successful.
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 6 Simulation and Analysis
### MSE Estimation Performance of Different Algorithms  
Figures 6 and 7 compare the estimated and simulated values of the frequency domain noise variance MSE from the ST-DFT channel estimation algorithm and DWT channel estimation algorithm for different channel models. As can be seen from the figures, the theoretically estimated values and simulated values of the frequency domain noise variance are similar for both channels. This indicates that the frequency domain noise variance of the current channel can be effectively estimated using Equation (35). In addition, the estimated values of both channel estimation algorithms MSE are very close to the corresponding simulation values, which verifies the correctness of the analysis of Equations (36) and (37). Therefore, the estimated MSE can be used as a decision metric for switching between the two algorithms.  
Figure 4: ST-DFT algorithm to remove noise total power boundary in channel D.  
Figure 5: ST-DFT algorithm to remove noise total power boundary in channel UMi.