esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 5 Experimental Results
### Evaluation Metrics  
This section describes the evaluation metrics used to assess the performance of DL-based IDS. Table 3 shows the evaluation metrics based on various attributes of the confusion matrix.  
Where  
* True positive (TP) indicates the instances where the classifier correctly classifies an attack.
* False negative (FN) indicates the instances where the classifier wrongly classifies an attack as normal.
* False positive (FP) indicates the instances where the classifier wrongly classifies a normal instance as an attack.
* True negative (TN) indicates the instances where the classifier correctly classifies normal instances.  
In addition, researchers also use several other evaluation metrics in their studies, such as precision, recall, false alarm rate, true negative rate, accuracy, and F-measure.
* Precision is the proportion of attacks correctly predicted vs. all samples predicted as attacks.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{12}\]
* Recall, or detection rate, is the proportion of all samples correctly classified as attacks vs. all attack samples.  
\[\text{Recall}=\text{DetectionRate}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{13}\]
* False alarm rate is also known as the false positive rate and is defined as the ratio of incorrectly predicted attack samples vs. all normal samples.  
\[\text{False Alarm Rate}=\frac{\text{FP}}{\text{TN}+\text{FP}} \tag{14}\]
* True negative rate is defined as the proportion of correctly classified normal samples vs. all normal samples.  
\[\text{True Negative Rate}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{15}\]
* Accuracy, or detection accuracy, is the proportion of instances correctly classified vs. the total number of instances. However, it is only a useful performance metric when a balanced dataset is used.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{16}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Class** & \\ \hline \hline \multirow{2}{*}{Actual Class} & Attack & Normal \\  & True Positive & False Negative \\  & False Positive & True Negative \\ \hline \hline \end{tabular}
\end{table}
Table 3: The attributes of a confusion matrix.  
F1-measure is the harmonic mean of precision and recall. In other words, it is a statistical technique involving precision and recall for examining a system's accuracy.  
\[\text{F1 Measure}= 2\times(\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}) \tag{17}\]  
The metrics used in this research are commonly used to evaluate the performance of IDS, such as in [31]. The proposed approach's evaluation involves calculating all those evaluation metrics.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 5 Conclusions & Future Work  
In this work, we presented a methodology for detecting specific SAR patterns in vessel trajectories, using shapelet transformation and classification and a trajectory generator for creating our training data. The methodology introduces several techniques that handle the segmentation and sampling of trajectories, that optimize the shapelet feature selection process in order to improve classification performance and take advantage of multiple trajectory features that change during a SAR maneuver in order to achieve better results. The results on synthetic data are excellent, and the application of the learned models on real data, is very promising since it reveals and correctly classifies SAR patterns in a real vessel trajectory dataset.  
Since GENDIS is a genetic-based algorithm it does not guarantee to extracts the best shapelets, especially with few iterations. It is on our plans to compare it with shapelets extracted using the original shapelets algorithm or one of its optimizations as Fast Shapelets, that assure the correct result. Also, since the final result relies both on the quality of the extracted shapelets and the classifier performance, we will experiment with more classification algorithms. It is on our next steps to extract shapelets from more trajectory parameters and examine how they contribute to the predictive capabilities of the classification models. We also plan to evaluate the proposed approach in a multi-class classification task, using additional classifiers and classifier ensembles.  
Another extension of this work is towards the direction of pattern extraction from real trajectory data. The application of a trajectory clustering algorithm on the sub-trajectories that contain maneuvers will allow to extract unknown or undocumented patterns that take place in the real world but are not necessarily described in a naval manual. This will allow the shapelet methodology to be extended to more trajectory classification and trajectory annotation problems.  
[PERSON], [PERSON] and [PERSON] worked on the conceptualization of the work. Then [PERSON] worked on data curation and with [PERSON] developed the final software solution. [PERSON] worked on the experiments and the original draft preparation. [PERSON] and [PERSON] helped with the writing-review and editing of the final manuscript and had the overall project supervision.  
This work has been developed in the frame of the MASTER project, which has received funding from the European Union's Horizon 2020 research and innovation programme under the [PERSON] grant agreement No 777695.  
This work was supported in part by MarineTraffic which provided data access for research purposes.  
The authors declare no conflict of interest.
_Appl. Sci._**2013**, \(3\), 189-213; doi:10.3390/app3010189  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Photoelectron Angular Distribution and Phase in Two-Photon Single Ionization of H and He by a Femtosecond and Attosecond Extreme-Ultraviolet Pulse**  
**[PERSON] \({}^{1,\ast}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Photon Science Center, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan  
\({}^{2}\) Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Katahira 2-1-1, Aoba-ku, Sendai 980-8577, Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +81-3-5841-0710.  
_Received: 31 December 2012; in revised form: 7 February 2013 / Accepted: 8 February 2013 / Published: 5 March 2013_  
**Abstract:** We theoretically study the photoelectron angular distribution (PAD) from the two-photon single ionization of H and He by femtosecond and attosecond extreme-ultraviolet pulses, based on the time-dependent perturbation theory and simulations with the full time-dependent Schrodinger equation. The PAD is formed by the interference of the \(s\) and \(d\) continuum wave packets, and, thus, contains the information on the relative phase \(\delta\) and amplitude ratio between them. We find that, when a spectrally broadened femtosecond pulse is resonant with an excited level, the PAD substantially changes with pulse width, since the competition between resonant and nonresonant ionization paths, leading to \(\delta\) distinct from the scattering phase shift difference, changes with it. In contrast, when the Rydberg manifold is excited, and for the case of above-threshold two-photon ionization, \(\delta\) and the PAD do not depend much on pulse width, except for the attosecond region. Thus, the Rydberg manifold and the continuum behave similarly in this respect. For a high-harmonic pulse composed of multiple harmonic orders, while the value of \(\delta\) is different from that for a single-component pulse, the PAD still rapidly varies with pulse width. The present results illustrate a new way to tailor the continuum wave packet.  
**Keywords:** two-photon ionization; photoelectron angular distribution; free-electron lasers; high-order harmonic generation
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 3 Common Attacks  
According to the Kerckhoff principle, it is assumed that the attacker knows all the information about the watermarking algorithm except the secret key. The attacks that threaten the security of watermarks can be divided into three categories: WOA (watermarked only attack), KMA (known message attack), and KOA (known original attack) [6]. Among them, WOA refers to that the attacker only has the data containing the watermark; KMA denotes that the attacker not only has mastered the data involving watermarks, but also grasped some contents of watermarks, and that the secret key can be estimated through a number of observations; KOA means that the attacker knows the data including the watermark and the corresponding original carrier. Additionally, WOA is the most difficult attack to achieve. Because the primary purpose of semi-fragile watermarking technology is to ensure the authenticity and completeness of the digital image content, there has appeared a \"pseudo-authentication\" attack against the watermark authentication algorithm. That is, while the image content is tampered by this kind of attack, the watermarking information involved in the image will not change, so that the tampered image can still pass the authentication system. According to the existing digital watermarking attack algorithms, the following common attacks are summarized:
## 2 Materials
### Landslide Inventory  
An accurate landslide inventory map is the basis for effective landslide susceptibility assessment [35]. Landslide data in this study originates from a 0.5 m resolution multiband remote sensing image obtained by the Pleiades satellite in 2014. Based on remote sensing image interpretation and field investigation verification, 493 historical landslides were identified in the study area. According to the Varnes classification system [49], the  
Figure 1: Landslide inventory map and location of the study area: **(a)** location of Sichuan Province in China; **(b)** location of the study area; **(c)** study area and landslide inventory map.  
landslides in the study area mainly belong to rock fall, and a small part of them belong to debris fall and debris flow. The total landslide area is 15.6 km\({}^{2}\), accounting for 1.69% of the study area. The average area, maximum area, and minimum area of landslide are 0.032 km\({}^{2}\), 0.991 km\({}^{2}\), and 0.00041 km\({}^{2}\) respectively. Roads in the study area are the main infrastructure that suffers from landslide damage, causing enormous economic losses. In this study, the geometric center of the landslide surface is taken as the landslide point.  
According to the data and prior knowledge, a 30 m \(\times\) 30 m grid was selected as the basic evaluation unit. Consequently, 1,024,455 grids were created for the study area, and 493 landslide points were located in different evaluation units, with a total of 493 landslide units. By random sampling, 70% (345 landslides) of landslides were used as training data for modeling, while the other 30% (148 landslides) were used for testing. A landslide inventory map was established using these data (see Figure 1a).
## 1 Introduction
For accurate analysis, a coupled analysis considering the interaction between the WEC and the structure was performed. As a demonstration site of the proposed Wavestar-system simulation, Buan, one of the nearshore regions of Korea, was selected for the detailed case study [19; 20], using various sources of ocean wave data nearby [5; 20; 21; 22; 23; 24; 25]. The hydraulic PTO system was simplified by linear equivalent damping for the rotational motions of the arms. The foundation platform was modeled by three-dimensional beam elements, and its vibration characteristics were evaluated using modal analysis. The frequency-dependent hydrodynamic coefficients of the multiple floating bodies were obtained using a BEM-based 3D diffraction/radiation panel program, WAMIT [26]. The body-nonlinear Froude-Krylov force based on linear wave theory and body-nonlinear hydrostatic force were calculated in the time domain by considering the instantaneous submerged body surface at every time step [27; 28]. Then, a fully coupled dynamic simulation was performed in the time domain to evaluate the respective body responses, relevant forces and stresses, and the corresponding power production in typical random waves of the given site. The coupled analysis results were compared with the results of several simpler approaches.
## 2 Circuit Design
### Bias and Stability Network Design  
Most RF and microwave transistors are potentially unstable at both high and low frequencies. GaN HEMT could easily oscillate and burn down due to high gains and high output power. Therefore, for designing a GaN RF amplifier, it would be the first priority to keep the amplifier stable and prevent oscillation. The bias network combing the stabilized network is shown in Figure 6a. The bias network is designed using the 795 \(\Omega\) gate resistor \(R_{g}\) with the total bypass capacitance of 16 pF. The stabilized network is designed using a 7 \(\Omega\) resistor \(R_{s}\) and a parallel RC network, a 72.4 \(\Omega\) resistor \(R_{sp}\) and a 2 pF capacitor \(C_{sp}\). Later in the layout phase, the capacitor \(C_{sp}\) would be separated into two for layout symmetry. The parallel RC network, \(R_{sp}\) and \(C_{sp}\), is tuned for in-band stability. The parallel RC network could keep the amplifier circuit stable and does not overly degrade the gains in operation bands. The bias resistor \(R_{g}\) is used for low-band stability. The simulated Rollet's stability factor (\(K\)), \(\Delta\) and stability measure (\(B_{1}\)) are shown in Figure 6b. The necessary and sufficient conditions for unconditional stability are \(K>1\), \(\mid\Delta\mid\) \(<1\), and \(B_{1}>0\)[25]. In Figure 6b, the unconditionally stable conditions are fully met at both high and low frequencies. Thus, the small-signal stability was assured. The series resistor \(R_{s}\) is used for large-signal stability. The simulated large-signal stability factor \(K\) is shown in Figure 6c. The input power is swept from 0 to 25 dBm, and the \(K>1\) requirements are well-fit at 3.0-4.5 GHz. Hence, the large-signal stability was also assured.  
Figure 5: The simulated large-signal output performances of the 10 \(\times\) 200 \(\upmu\)m transistor at 3.7 GHz: (a) power and PAE contours; (b) dynamic load line; (c) simulated \(G_{m}\) versus \(P_{in}\) under different bias voltages (\(V_{GS}\)).
## 5 Methodology
### Anomaly Detection Pipeline  
The pipeline for real-time detection implemented at edge nodes consists of the following stages:  
1. Feature Extraction and Normalization: Input data (e.g., IoT telemetry and network logs) is preprocessed into fixed-length, normalized feature vectors or sequences.
2. Model Inference: Data is passed through either the autoencoder or LSTM model to compute anomaly scores as follows: \[\bullet\] Autoencoder: Mean squared reconstruction error; \[\bullet\] LSTM: Deviation between predicted and observed sequences.
3. Thresholding: Anomalies are detected based on a dynamic or percentile-based threshold \(\theta\) as follows: \[\text{If Score}(x)>\theta\text{, then }x\text{ is anomalous}\]
4. Alerting Generation: Depending on policy, edge nodes may \[\bullet\] Trigger alerts (e.g., to SIEM or admin systems); \[\bullet\] Log events for future FL training; \[\bullet\] Enforce mitigation policies (e.g., quarantine or reset).  
Edge-FLGuard supports pipeline variants for both unsupervised (autoencoder-based) and semi-supervised (LSTM with labeled anomalies) detection schemes.  
Figure 3 illustrates the anomaly detection pipeline used by Edge-FLGuard during inference.
# Magnetic Localization Method for Vehicles Based on Transformer
## 3 Methods  
Compared to magnetic pedestrian positioning, the use of magnetic fields for vehicles has some beneficial characteristics. First, mileage information can be integrated based on the speed obtained from the speed sensor with which every vehicle is equipped [15]. This information can be used to transform a magnetic time series into a spatial sequence, which avoids the spatial inconsistency problem of magnetic data caused by the different speeds of vehicles and the different sampling rates of magnetometers. Second, unlike human data collection, which has a high cost, vehicle data collection is more efficient. Therefore, it is easier to construct a training set from multiple actual measured data, which is more realistic than obtaining pedestrian positioning data through random walks and data augmentation. Third, three-axis magnetic fields can be directly used after eliminating the carrier interference because of the repetitive nature of the postures of vehicles travelling on the same path.
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
## 4 Research Progress of Flyer Morphology
### Investigation of Flyer Morphology
#### 4.1.7 Transient Imaging Displacement Interferometry (TIDI)  
TIDI has been used to investigate surface morphological changes produced by laser ablation, and image reconstruction may be accomplished by data analysis utilizing a two-dimensional Fourier transform [94]. [PERSON] et al. described the TIDI operating principle in detail [95] and utilized TIDI to obtain a full-field surface displacement image of the copper target. A displacement sensitivity of 10 nm and a lateral spatial resolution of 5 \(\upmu\)m were achieved within a 1 mm\({}^{2}\) field-of-view. Eight displacement maps are permitted for a single loading event, making it possible to trace the progression of the surface motion [96].  
The aforementioned flyer morphology investigation technologies are compared in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Measurement Technology** & **Advantage** & **Disadvantage** \\ \hline \hline \multicolumn{1}{c}{Streak photography} & Simple operation; wide application & Unable to obtain frontal images of the flyer \\ \hline \hline \multicolumn{1}{c}{Interferometer technology} & Synchronous with speed test & High requirements for probe layout \\ \hline \hline \multicolumn{1}{c}{Shadowgraphy} & Nanosecond level temporal resolution; multiple working modes & One-dimensional information \\ \hline \hline \multicolumn{1}{c}{DIH} & Complex and expensive device & \
\begin{tabular}{c} Image washout caused by the ambient \\ luminosity; complex structure \\ \end{tabular} \\ \hline \hline \multicolumn{1}{c}{X-ray diagnostic method} & High time resolution; 3D image & Complicated to operate \\ \hline \hline \multicolumn{1}{c}{Optical fiber probe method} &
\begin{tabular}{c} Electromagnetic interference immunity; fast \\ response; low cost; simple structure \\ \end{tabular} & Dependent on probe layout \\ TIDI & High temporal and spatial resolution & High cost \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different flyer morphology investigation technologies.  
Figure 24: Optical fiber probe configuration.
# Balloon Design for Mars, Venus, and Titan Atmospheres
## 2 Theoretical Model
### Equations of Motion  
The motion of a balloon can be described by the ordinary differential equations (ODE) that illustrate the time evolving behaviour of the balloon system. The North East Down (NED) coordinate frame is used for the ODEs that describe the horizontal and vertical motion of the balloon system. The balloon vertical acceleration \(a_{\rm z}\) is found by summing up the vertical components of applied forces on the system, and can be written as:  
\[m_{\rm v}a_{\rm z}=GI-M_{\rm gross}g-D_{\rm z} \tag{9}\]  
In Equation (9), the mass \(m_{\rm v}\) is the virtual mass and it accounts for the mass of air that is dragged along the balloon. It can be written as: \(m_{\rm v}=M_{\rm t}+C_{\rm mass}\rho_{\rm a}V\), where \(M_{\rm t}\) is the total mass of the balloon system, \(C_{\rm mass}\) is the virtual mass coefficient and it is taken as 0.5 in this work [37]. \(M_{\rm t}\) is the total mass and can be written as: \(M_{\rm t}=m_{\rm p}+m_{\rm b}+m_{\rm e}+m_{\rm g}\). Here, \(m_{\rm p}\) is the payload mass, \(m_{\rm e}\) is the balloon envelope or film mass, \(m_{\rm b}\) is the ballast mass, and \(m_{\rm g}\) is the gas mass. The gas mass \(m_{\rm g}\) changes due to ducting (i.e., venting at maximum volume), valving, or permeation. In Equation (9), \(GI\) is the gross inflation and is defined in Equation (5). The Drag \(D_{\rm z}\) in Equation (9) can be calculated using:  
\[D_{z}=\frac{1}{2}C_{\rm D}\rho_{\rm a}A_{\rm xy}v_{\rm rel}^{2}, \tag{10}\]where \(A_{xy}\) is the area of the balloon projected at a particular time, \(C_{\rm D}\) is the drag coefficient, and \(v_{\rm rel_{z}}\) is the relative velocity in the \(z\) direction. The drag model used in this work is taken from [41]:  
\[C_{\rm D}=5.4856\cdot 10^{9}\cdot\tanh(4.3774\cdot 10^{-9}/{\rm Re })+0.0709\cdot\tanh(700.6575/{\rm Re})\\ +0.3894\cdot\tanh(74.1539/{\rm Re})-0.1198\cdot\tanh(7429.0843/{ \rm Re})\\ +1.7174\cdot\tanh(9.9851/{\rm Re}+2.3384)+0.4744&{\rm Re}<2\cdot 10^{5} \end{]} \tag{11}\]  
\[C_{\rm D}=8\cdot 10^{-6}\cdot(({\rm Re}/6530)^{2}+\tanh({\rm Re})-8 \cdot\ln({\rm Re})/\ln(10))\\ -0.4119\cdot\exp(-2.08\cdot 10^{43}/({\rm Re}+{\rm Re}^{2})^{4})\\ -2.1344\cdot\exp(-(({\rm In}({\rm Re}^{2}+10.7563)/\ln(10))^{2}+9.9867)/{\rm Re}\\ +0.1357\cdot\exp(-(({\rm Re}/1620)^{2}+10370)/{\rm Re})-8.5\cdot 1 0^{-3}.\\ (2\cdot\ln(\tanh(\tanh({\rm Re})))-2825.7162)/{\rm Re}+2.4795 \qquad 2\cdot 10^{5}<{\rm Re}<=10^{6} \tag{12}\]  
\[C_{\rm D}=0.212546\qquad\qquad{\rm Re}>10^{6} \tag{13}\]  
The Reynolds number \({\rm Re}\) used in Equations (11)-(13) can be defined as:  
\[{\rm Re}=\rho_{a}\cdot D_{\rm b}\cdot v_{\rm rel}/\mu_{a} \tag{14}\]  
The balloon diameter is given by \(D_{\rm b}\) and \(\mu_{a}\) is the atmospheric dynamic viscosity. Further, there are no horizontal forces acting on the balloon except the aerodynamic drag (horizontal winds), and, therefore, the equations of horizontal accelerations are:  
\[M_{\rm t}a_{x}=D_{x} \tag{15}\]  
\[M_{\rm t}a_{y}=D_{y} \tag{16}\]