esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 4 Proposed Intrusion Detection Framework  
The proposed framework used in this work is presented in Figure 9. The first stage comprises obtaining the flight logs as DAT files from the VTO labs drone dataset repository. The DAT files were then converted into CSV files using the tools and processes mentioned in [27]. The extracted dataset was then analyzed for various anomalous data points that match the attack scenarios listed, namely, GPS signal loss, jamming and DoS attacks.  
Based on the types of cyber attacks that target UAVs, the abnormal data points were labeled as attack and remaining data points as normal. For the DoS attack scenario, additional data points that replicate frame loss were inserted into the dataset that reflect the drone state during the attack. The various flight logs were then combined to form a single dataset with multiple flight scenarios. From the dataset, features that highlight the flight path were extracted for classification tasks. The features extracted were: 'IMU_ATTI(0):Longitude', 'IMU_ATTI(0):Latitude', 'IMU_ATTI(0):relativeHeight:C', 'IMU_ATTI(0):roll:C', 'IMU_ATTI(0):pitch:C', 'IMU_ATTI(0):yaw:C', 'IMU_ATTI(0):distanceTravelled:C', 'RC_Info:frame_lost:D', 'Motor:Speed:RFont', 'Motor:Speed:LFront', 'Motor:Speed:LBack', 'Motor:Speed:RRack', 'osd_data:navHealth', 'IMU_ATTI(0):numSats' and 'Output'. The pre-processing stage then further removes data points with valid values, such '\(NAN\)' are removed from the dataset before the classification task.The dataset details are listed in Table 3.  
navHealth is a log feature that indicates the health of communication messages between a drone and GPS satellites. Anomalous navHealth values can be attributed to a drone's inability to communicate properly with satellites, and this can also potentially be attributed to a GPS jamming attack wherein, radio signals are impeded to prevent routine drone to satellite communication, and thus disrupting flight operations. Figure 5 presents the anomalies as obtained through analysis of the navHealth parameter values.  
Once the pre-processing is completed, the dataset is then fed to a machine learning algorithms such as random forest, navie bayes, linear regression and SVM. The best performing model is then selected for deployment for intrusion detection in drones.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories** & **Dataset** & **Total Flights** & **Types of Attacks Included** \\ \hline \multirow{4}{*}{Normal} & \multirow{4}{*}{97255} & \multirow{4}{*}{18} & - & Deliberate motor shutdown (all four motors speed = 0) \\  & & & - & DoS attacks (frame loss = 1) \\ \cline{1-1}  & & & - & GPS jamming (navhealth = 0) \\ \cline{1-1} Attack & 14590 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset description.  
Figure 9: Framework to detect malicious attacks in UAVs.
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 3 Results
### Static Scenario  
If the noise of VADASE solutions (around few mm/s) is well known when GNSS measurements from geodetic class receivers are analyzed, the model noise on mass-market devices (e.g., Android smartphones) has to be evaluated. In fact, the noise level of variometric solutions could increase due to the poor quality of GNSS measurements acquired with extremely low-cost equipment. Furthermore, knowing the VADASE noise level allows one to immediately understand whether, for example, a fast periodic movement can be detected and analyzed.  
Fifteen minutes of 1 Hz measurements in static and open-sky scenarios were acquired with a Xiaomi Mi 8. The comparison with 3D variometric velocity estimated at the same time with a geodetic class receiver placed nearby allowed us to evaluate how the different observation qualities affected the achievable accuracy (Figure 2). M0 SE, one of the permanent stations within IGS/EUREF network, was used in this analysis in order to consider similar atmospheric conditions: the distance between the two receivers was around 100 m.  
The root mean square error (RMSE), computed for each component of the 3D velocity, is representative of the noise level of the VADASE solution.  
Table 1 shows that higher RMSEs were obtained when measurements from the Xiaomi Mi 8 were considered. Although the accuracy achievable with Android measurements is lower with respect to solutions obtained with high-end receivers, the results show that movements with planimetric velocity higher than 1 cm/s could also be reconstructed with mass-market devices, opening the way to different innovative applications.  
Figure 2: Differences between 1 Hz VADASE (Variometric Approach for Displacements Analysis Stand-alone Engine) solutions from M0 SE (orange) and Xiaomi Mi 8 (blue).
## 4 Methodology
### Subkey Generation  
The key schedule algorithm of AES has a predictable pattern of subkey generation that makes the algorithm prone to attacks. A robust KSA makes the cipher more resilient to linear and differential attacks. In our work, we have considered the idea of sub-key generation by the Khazad function, and the function is:  
\[K_{i}=K_{i-8}\oplus H(S(K_{i-4}))\oplus C^{[(i-8)/4]} \tag{5}\]  
\(i=(8,9,\ldots\ \ldots\,43)\)  
In which \(H\) is a linear diffusion function, \(S\) is a byte substitution function, and \(C\) is a round constant. The second-round keys are generated as:  
\[K_{8}=K_{0}\oplus H(S(K_{4}))\oplus C^{0} \tag{6}\]  
\[K_{9}=K_{1}\oplus H(S(K_{5}))\oplus C^{0} \tag{7}\]  
\[K_{10}=K_{2}\oplus H(S(K_{6}))\oplus C^{0} \tag{8}\]  
\[K_{11}=K_{3}\oplus H(S(K_{7}))\oplus C^{0} \tag{9}\]  
The first-round subkey (K\({}_{0}\), K\({}_{1}\), K\({}_{2}\), K\({}_{3}\)) (\(K_{0}\), \(K_{1}\), \(K_{2}\), \(K_{3}\)) can only be generated from the original key. The next set of round keys are generated as follows:  
\[K_{4}=K_{0}\oplus K_{2} \tag{10}\]  
\[K_{5}=K_{1}\oplus K_{3} \tag{11}\]  
\[K_{6}=K_{4}\oplus K_{5} \tag{12}\]  
\[K_{7}=K_{5}\oplus H(S(K_{6}))\oplus C^{0} \tag{13}\]Assume that the attacker knows (\(K_{4}\), \(K_{5}\), \(K_{6}\), \(K_{7}\)), but they still cannot construct (\(K_{0}\), \(K_{1}\), \(K_{2}\), \(K_{3}\)), as \(K_{7}\) only depends on \(K_{5}\), and \(K_{6}\), \(K_{6}\) depends only on \(K_{4}\) and \(K_{5}\), and so on. The attacker needs to carry out 232 exhaustive attacks to get the first-round keys and 264 to guess the original key. Therefore, the proposed algorithm meets the safety requirements.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 5 Performance Evaluation
### Simulation Environment  
To evaluate the performance of our EGR protocol in the mentioned simulation environment, it is necessary to adopt some performance metrics. The quality of a routing protocol is indicated by the performance metrics [32]. We will consider five performance metrics to compare the performance of EGR with the existing works. They are average residual energy, average end-to-end delay, network lifetime, average packet delivery ratio, and cluster formation time. The performance metrics are defined as follows:  
* Average residual energy is the average remaining energy of a node after a certain amount of time in simulation.
* Network lifetime is the elapsed time until half of the nodes are alive.
* Average end-to-end delay is the average time required for a packet to reach its destination from source.
* Average packet delivery ratio is the ratio of the received packets over packets sent in the network.
* Cluster formation time is the time taken to form all clusters in the network.  
The simulation environment consists of \(100\times 100\) m network space. For the simulation, we consider a random deployment of 100 multimedia sensor nodes, and there are three sinks nodes in the network. The environment consists of a moving target. Still images are captured by the nodes who win FoVs of the target component. Each node has an initial energy of 2 J and can sense up to 30 m. Table 3 shows the simulation environment for the deployment of the proposed protocol. Table 4 shows the energy expenditure of a multimedia node for some basic operations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operation** & **Value** \\ \hline Camera initialization & 1725.4 mj \\ Frame grabbing & 537.2 mj \\ Object recognition & 144.2 mj \\ Shutdown & 768.5 mj \\ \hline \hline \end{tabular}
\end{table}
Table 4: Basic energy expenditure of sensor nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network area & \(100\times 100\) m \\ Number of sensor nodes & 100 \\ Data rate & 2 Mbps \\ Image size & 176 \(\times\) 144 \\ Frame rate & 30 fps \\ Offset angle & 60\({}^{\circ}\) \\ Sensing radius & 30 m \\ Initial energy of each node & 2 J \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
In Table 5, the workload parameters of our simulation are summarized. Not only the traffic load including packet outgoing rate, packet size, and transmission rate but also the radio transmission rage is shown for the network workload incurred during the simulation. Note here that the radio transmission rage is also included in the table because it also affects the workload in accordance with interference.  
Apart from the simulation results obtained using different performance metrics, we also show the effectiveness of the data redundancy avoidance algorithm (Algorithm 1) in EGR through simulation in the following section.
## 3 Results  
### Determination of the Saturation Light Pulse Intensity  
We examined the relationships between the actinic PPFDs and ChlF yields in the dark and solar light adaptation periods for paprika leaves (Figure 3) as the saturation PPFD required to saturate the photosynthetic electron transport varies with the intensity of actinic light. Figure 3A,B show the results for a dark-adapted leaf (SPAD: 47.5, measured 3 times) and solar light-adapted leaves of 700 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) (SPAD: 48.2, measured two times: \(\Phi\)F1 and \(\Phi\)F2) and 1300 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) (SPAD: 42.6, measured four times: \(\Phi\)F3, \(\Phi\)F4, \(\Phi\)F5, and \(\Phi\)F6), respectively.  
The ChlF yields of the dark-adapted leaf increased dramatically with a slight increase in PPFD and levelled off approximately at a PPFD of 800 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) (dashed line in Figure 3A). The ChlF yields of the solar light-adapted leaf increased more gradually than those of the dark-adapted leaf, leveling off approximately at a PPFD of 1800 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) (dashed line in Figure 3B). As a result, we confirmed the saturation of the photosynthetic electron transport at PPFD values over 1800 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) at constant solar PPFD of 1300 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\). Therefore, we decided to use the saturation pulse of the red laser that provides sufficiently strong PPFD of 6000 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) for 0.8 s for paprika, maize, and pachira plants. This result can also be used in the PAM measurement.
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 4 Conclusions  
To resolve failure prediction problem of the rotating machinery, a novel method was developed in this paper. Because CEEMDAN not only can overcome the mode mixing effect of EMD but also should be able to avoid the decomposition errors caused by white noise in EEMD and CEEMD, it was applied to the original vibration signals to get a series of IMFs, and all eligible IMFs were selected by MIC to reconstruct the signals with helpful information. A feature named CEEMDAN-ApEn was proposed to describe the entire degradation process of the rotating machinery. Compared with existing features in this field, the new proposed CEEMDAN-ApEn feature can maintain an good balance among all influence factors of failure prediction. An efficient RUL prediction model by the combination of the AR model and UKF was developed to obtain the RUL of the rotating machinery. The experimental results show that the proposed method is beneficial and suitable for failure prediction of the rotating machinery.  
It should be noted that the threshold of the final failure is an important parameter for the proposed failure prediction method, and now it is selected according to the practice and experience. Considering the diversity of the rotating machinery, future work will be devoted to select the appropriate value of this parameter adaptively. Moreover, the proposed method will be applied to real cases study concerning the failure prediction of the rotating automaton.  
Conceptualization, J.Y.; Formal analysis, Y.C.; Funding acquisition, J.Y.; Methodology, J.Y.; Project administration, J.W.; Resources, T.G.; Software, T.G.; Supervision, J.Y.; Validation, J.W.; Visualization, Y.C.; Writing--original draft, Y.C.; Writing--review & editing, T.G. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the editors and the reviewers for their helpful comments.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Carbonization Process  
For the carbonization process, the copolymer EGDMA + St + 4L was chosen as a representative sample. It was characterized by a large share of lignin and spherical shape without agglomeration tendency. Characteristic of the carbonization process are summarized in Table 6.  
At first, the reference material (EGDMA + St + 4L-C) was prepared in the following way: 4 g of the output sample was carbonized for 30 min at 400 \({}^{\circ}\)C in the nitrogen atmosphere. No activating agent was used in this case. The yield of carbonization processes was various from 8% (without the activator) to 26% (in AgNO\({}_{3}\)).  
The carbonization process showed how the specific surface would be affected by the activator and temperature (Table 7). All the materials obtained by carbonization possessed larger porosity parameters (S\({}_{\mathrm{BET}}\) and V\({}_{\mathrm{TOT}}\)) than the output polymeric sample. Depending on the kind of surface activators the specific surface areas ranged from 1 to 299 m\({}^{2}\)/g. In the case of carbons prepared by activation with the phosphoric acid, a larger area was obtained for the diluted acid. Analyzing the results for EGDMA + St + 4L-P2, EGDMA + St + 4L-S, and EGDMA + St + 4L-N it should be noted that the diluted sulphuric acid was the most efficient in the activation process and nitric acid was less efficient, which can be associated with the increase of thermal stability of their molecules. The same relationship is observed when the porosity of EGDMA + St + 4L-N and EGDMA + St + 4L-Ag is compared. The more thermally stable silver nitrate (ionic compound) gives carbon that is characterized by larger porosity. The volatile  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reagents and Concentration** & **Yield (\%)** &
\begin{tabular}{c} **Carbon Name Acronym** \\ **(Abbreviation)** \\ \end{tabular} \\ \hline Temperature only & 8.3\% & EGDMA + St + 4L-C (C) \\ H\({}_{3}\)PO\({}_{4}\) (85\%) & 9.1\% & EGDMA + St + 4L-P1 (P1) \\ H\({}_{3}\)PO\({}_{4}\) (85\% diluted in volume ratio 1:1) & 10.2\% & EGDMA + St + 4L-P2 (P2) \\ H\({}_{2}\)SO\({}_{4}\) (95\% diluted in volume ratio 1:1) & 10.5\% & EGDMA + St + 4L-S (S) \\ HNO\({}_{3}\) (65\% diluted in volume ratio 1:1) & 23.9\% & EGDMA + St + 4L-N (N) \\ AgNO\({}_{3}\) (9\%) & 26.2\% & EGDMA + St + 4L-Ag (Ag) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Characteristics of the carbonization process.  
Figure 10: Derivative thermogravimetric curves of the obtained materials.  
products of decomposition of an activating agent react with the carbonized polymer. During this process, the oxidation and removal of some fragments of the polymer network takes place, as a result free space in the structure is generated, which forms porosity. The described process is more effective if the starting material is porous. Taking into account that the output sample was nonporous the obtained results seem to be quite satisfactory.
# Optical Flow Estimation with Occlusion Detection
## 4 Occlusion-Aware Optical Flow Estimation  
In the classical variational model, consistency constraints are used to construct energy functions. Occlusion will destroy the assumption of color consistency, and causes the existing optical flow estimation algorithm to fail. Therefore, the whole image should be divided into occlusion areas and non-occlusion areas and estimated optical flow separately.  
For non-occlusion areas, we estimate the optical flow by constructing the energy function with the occlusion coefficient \(\theta\), which is vectorization of the occlusion detection result \(M\). We optimize the following model for optical flow.  
\[\begin{split} E(\omega)=\theta(E_{color}(\omega)+aE_{gradient}( \omega)+bE_{smooth}(\omega)+cE_{flow}(\omega,\omega_{1}))\\ E_{color}(\omega)=\int_{\Omega}\rho(|I_{2}(x+\omega(x))-I_{1}( x)|^{2})dx\\ E_{gradient}(\omega)=\int_{\Omega}\rho(|\bigtriangledown I_{2 }(x+\omega(x))-\bigtriangledown I_{1}(x)|^{2})dx\\ E_{smooth}(\omega)=\int_{\Omega}\rho(|\bigtriangledown u(x))+ \bigtriangledown v(x)|^{2})dx\\ E_{flow}(\omega,\omega_{1})=\int_{\Omega}\rho(|\omega(x)-\omega_{ 1}(x)|^{2})dx\end{split} \tag{5}\]  
where \(a\), \(b\) and \(c\) are tuning parameters, \(x\) indexes a particular image pixel location, \(\omega\) is two-dimensional optical flow and \(\omega(x)\) the optical flow in \(x\) position, \(\rho\) is the Charbonnier penalty function [18], \(\Omega\) is the whole image domain. The data term penalizes deviations from the assumption which is the corresponding points should have the same gray value or color. Due to the illumination effects, it is not reliable to use the color and gray value. We supply the gradient constraint, which is invariant to additive brightness changes. The smoothing term, also called the regularity term, is used to reduce the ambiguous solutions. In the flow term, \(\omega_{1}(x)\) denotes the initial optical flow obtained from matching.  
We estimate optical flow \(F_{noc}\) in the non-occlusion areas using coarse-to-fine warping scheme, where the down sampling factor is 0.8, and the minimum sampling size is 16. There are many ways to deal with the model mentioned above. [PERSON] et al. [28] solved the problem with GNC [39], and they found that GNC is useful even for a convex robust function. Same as LDOF, EpicFlow uses successive over-relaxation (SOR) to iteratively solve their linear system of equations and stops the iteration before convergence. TRW-S [40; 41] and the Newton method are also used by FullFlow and [PERSON] et al. [31]. We choose the SOR for our model and we refer to publication like [42] for some details of the Eular-Lagrange equations, the derivative calculation and the nested fixed-point iterations.  
For occlusion areas, we can reason that the optical flow is based on motion continuity because of the lack of necessary feature information to construct the energy function. Based on \(F_{noc}\) mentioned above and the edges of frame 1, the optical flow \(F_{occ}\) is interpolated by EpicFlow finally. The final result is shown in Equation (6).  
\[F=\widetilde{\theta}F_{noc}+\theta F_{occ} \tag{6}\]
## 7 Results and Performance Evaluation
### Number of Nodes in the WSN  
We did not find any significant relation between the increase or decrease of jamming indices with increase or decrease of the number of nodes. However, we found that the number of jammed nodes increased with increase of the total number of nodes and it decreased as the latter was decreased, as long as the nodes were within the range of the jammer (40 m and 727 m for the low and high power jammers respectively). We now present the results. The MATLAB output of the simulation set-up, discussed in Figure 6, is given in Figure 7. Table 5 gives the output values of the simulation results of the set-up depicted in Figure 6 and Figure 8 represents the same graphically.  
\begin{table}
\begin{tabular}{|p{28.5 pt}|p{28.5 pt}|p{28.5 pt}|p{28.5 pt}|p{28.5 pt}|p  
We find that there is a relationship between the various types of jammers with respect to the jamming indices, the power received at the nodes, and the inter-nodal distances in the WSN. We observe that it is possible to detect the type of jammers also through our method by using this relationship. The relationship is presented in Table 6. The relationship has to be read row-wise, e.g., the first row in the table will read, 'If the PDPT of the node is from 5 to 10 AND the total received power by the node is 300 to 400 nW AND the inter-nodal distances of the node from its neighbors are 10 to 30 m, then the node is under the attack of a Constant Jammer.' This table is relevant for all of the 720 simulations set-ups which we have described. Similar templates will have to be made for other set-ups. We find that our jammer discrimination results given in our Table 5 matches well with those of [PERSON] _et al._[1] given in their Figure 7.  
Figure 8: It graphically represents the output of the simulation for the set-up depicted by Figure 6.  
\begin{table}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|} \hline
19 & 80 & 80 & 3852 & 3903.30 & 51.534 & 73.31 & 6.160 & 13.53 & Not jammed & Not jammed \\
20 & 100 & 80 & 777.0 & 808.371 & 31.345 & 24.02 & 6.821 & 13.53 & Not jammed & Not jammed \\
21 & 20 & 100 & 49.87 & 111.304 & 61.435 & 0.799 & 24.425 & 88.95 & Jammed & Jammed \\
22 & 40 & 100 & 94.41 & 155.845 & 61.435 & 1.512 & 11.553 & 47.95 & Not jammed & Not jammed \\
23 & 60 & 100 & 235.4 & 284.327 & 48.914 & 4.716 & 8.515 & 40 & Not jammed & Not jammed \\
24 & 80 & 100 & 777.0 & 811.775 & 34.749 & 21.74 & 10.025 & 13.66 & Not jammed & Not jammed \\
25 & 100 & 100 & 433.4 & 457.586 & 24.225 & 17.18 & 8.913 & 13.53 & Not jammed & Not jammed \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._
## 5 Discussion
### Effects of Initial Estimates on the Prediction Accuracy of NNG and ALASSO  
We investigated whether the choice of initial estimates (OLS, ridge, and lasso) affects the prediction accuracy of NNG and ALASSO in scenarios with limited and sufficient information. Under limited information, the three initial estimates produced models with different predictions, with ridge initial estimates performing best. The superiority of ridge estimates over lasso and OLS may be explained by the limitations of the latter two: lasso may exclude important variables due to a variable selection process in the first stage, whereas OLS often exhibits high variability. These drawbacks can adversely affect the prediction performance of the NNG and ALASSO models [11; 15; 17]. In contrast, when sufficient information was available, all three initial estimates produced models with comparable predictions, which agrees with the findings reported by [PERSON] and [PERSON] [19] for low-correlation settings with high \(R^{2}\). This is because sufficient information enhances the estimation accuracy of both initial estimates and tuning parameters.  
Our findings suggest that, if the primary aim of the analysis is prediction, ridge initial estimates may serve as an alternative to OLS initial estimates in both low- and high-correlation settings, aligning with the findings of [PERSON] and [PERSON] [60]. However, when the goal is to obtain simpler, more interpretable models for descriptive purposes, lasso estimates may be preferable, especially when the data contain sufficient information to allow lasso to accurately select important variables in the first stage. This is crucial because once a variable is eliminated in the first stage, it cannot be reintroduced into the NNG or ALASSO model in the second stage [49].