esa-sceva/satcom-chunk-collection huggingface dataset:
# Pinion Failure Analysis of a Helical Reduction Gearbox in a Kraft Process
## 3 Investigation on Gear Design and Alignment
### Investigation on Gear Design Using the AGMA Approach  
The design check of a gearset is a reasonably challenging task as it involves the satisfaction of many design constraints. However, it is widely understood that gear teeth may fail through bending fatigue at its root and pitting fatigue on its surface. Thus, it was decided to check the safety factor against bending and pitting of pinion and gear, mainly at high load 3.75 MW employing AGMA standards. AGMA has proposed the following relations for finding the safety factor under bending and surface fatigues [25; 26; 27]:  
AGMA bending stress relation:  
\[\sigma_{b}=\frac{W_{t}}{FmJ}\cdot\frac{K_{a}K_{m}}{K_{p}}\cdot K_{s}K_{B}K_{I} \tag{1}\]  
AGMA bending fatigue strength relation:  
\[\sigma_{fb}=\frac{K_{L}}{K_{T}K_{R}}S^{\prime}_{fb} \tag{2}\]  
Safety factor relation for bending fatigue:  
\[N_{b}=\frac{S_{fb}}{\sigma_{b}} \tag{3}\]  
AGMA surface stress relation:  
\[\sigma_{c}=C_{p}\sqrt{\frac{W_{t}}{FId}\cdot\frac{C_{a}C_{m}}{C_{p}}\cdot C_{s} C_{f}} \tag{4}\]  
Figure 3: (**a**) Healthy pinion teeth (without deformation) when operated at 3 MW; (**b**) deformed teeth of pinion near thrust disc when operated at 3.75 MW.  
AGMA surface fatigue strength relation:  
\[S_{fc}=\frac{C_{L}C_{H}}{C_{T}C_{R}}S^{\prime}_{fc} \tag{5}\]  
Safety factor relation for contact fatigue:  
\[N_{c}=\left(\frac{S_{fc}}{\sigma_{c}}\right)^{2} \tag{6}\]  
Symbols appearing in Equations (1)-(6) are named in the nomenclature. The design calculations based on AGMA standards are presented in the \"Results and discussion\" section.
## Materials and Methods
### Sample Measurements
#### 2.3.3 Statistical Data Processing  
The software SOLO 8.9.1, 2021 (Eigenvector Research Incorporated, 2022 Manson, WA, USA) was used to perform the statistical evaluation of experimental data. Partial Least Squares-Discriminant Analysis (PLS-DA) is a multivariate supervised method that allows the creation of discrimination models, giving the maximum covariance between measured data (isotopic and elemental content) and the response variable (represented, in this study, by the membership degrees with respect to their geographical origin and animal rearing system) [14]. Therefore, this technique was applied in order to classify meat samples according to the geographic origin and to determine significant differences between animal meat coming from rural regions and industrial farms. The performance of the differentiation models was evaluated in an unbiased manner, by applying the 10-fold cross-validation technique in order to compute the accuracy, sensitivity, and specificity measures.
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 4 Experiment
### Experimental Setup
#### 4.3.2 Network Setup  
For VQ-VAE parameter configuration, the encoder is implemented as six convolutional layers with kernel size 4, stride step 2, padding 1, and followed by ReLU, one convolutional layer with kernel size 3, stride step 1, padding 1 and followed by eight residual blocks, which are implemented as ReLU, \(3\times 3\) conv, ReLU, \(1\times 1\) conv for each block. Images are encoded as \(z_{e}\) with shape \(32\times 32\times 32\). The decoder is a symmetrical structure of the encoder using transposed convolutions. The dimensionality of the codebook and \(z\) are designed as \(512\times 32\) (\(K=512\)) and \(32\times 32\), respectively. The weight factor \(\beta\) is set to 0.25. We use the ADAM optimizer with learning rate 3e-4 and train for 100 epochs with batch size 256. For PixelSNAIL parameter configuration, the model consists of one residual block and one attention block, both of which are repeated three times due to the limitations of GPU memory trained for 150 epochs with batch size 64, ADAM optimizer, and learning rate 0.0003. We set the parameters d, color sigma, and space sigma of the bilateral filter to (20,75,75).
# Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT
## 3 Federated Learning-Based Resource Management with Blockchain Trust Assurance Mechanism
### Blockchain of Things  
With the addition of a massive amount of IoT devices and more IoT data generation, IoT has become a big target for cyber-attacks nowadays. Blockchain, a distributed shared ledger technology, offers new technical promise for IoT security [28]. Blockchain has a strong potential to create a more secure system as it is used for recording transactions, tracking transaction updates, and establishing trust mechanisms between different nodes. In addition to its application in digital currency, blockchain technology is changing the transactions paradigm in various trustless environments. The combination of blockchain and IoT (called Blockchain of Things, IoT) will make IoT applications more powerful.  
Blockchain can be applied to IoT with natural advantages. Firstly, blockchain is an open system; any IoT device can join or leave the blockchain at any time, which does not have any impact on the transactions of the whole blockchain recorded information. This is in line with the characteristics of IoT devices switching on or off at any time. Second, blockchain is a decentralized and distributed system. The nodes in the chain ensure that  
Figure 1: Federated Learning in IoT.  
their stored transaction records are consistent using consensus algorithms, which eliminates the weakness of single point of attack in traditional IoT. Third, the transactions in blockchain are secure and trustworthy. The database storing transaction records in each block can only be extended and cannot be deleted or changed, which ensures the non-repudiation of transaction records.  
By using blockchain in IoT application scenarios, it is possible to identify security vulnerabilities in IoT and realize information sharing and interaction in IoT applications in a secure and effective way [19]. In addition, some IoT devices have limited resources, so it is difficult to store a complete blockchain ledger. Therefore, in IoT entities, including end devices, IoT gateways, IoT servers, etc., should all be added to the blockchain in a \"decentralized\" mode. Moreover, one or more blockchain nodes and \"decentralized\" applications (dApps) can be deployed on an IoT entity.  
The exploration of industry applications of blockchain of Things started around 2015, and the typical application areas include smart city, industrial Internet, and IoT payment. At present, the integration application of blockchain and IoT has received widespread attention, and has also been rapidly developed and practiced in the industry.
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 2 Related Work  
### APT Attack Model  
Different APT attacks have different attack modes but have similar stage characteristics in the attack process. All APT attacks can be roughly divided into the following five stages, which is shown in Figure 1: information gathering, embedded intrusion, communication control, lateral penetration, and data returning.  
* Information gathering is the process by which APT attackers use multiple means to collect various information about the target network [15]. The collected information mainly includes basic information about the network environment, overall architecture, defense system software, and system vulnerabilities of the target system. Based on the collected information, the weak points of the target network are analyzed and targeted attacks are carried out.
* Embedded intrusion Embedded intrusion is the stage where the APT attack formally begins, where the APT attacker writes targeted malware to adapt to the target network environment based on the information gathered about the target system network during the intelligence-gathering phase.
* Communication control After successfully compromising the target network, the APT attacker continuously invades the internal core network devices based on the control of the internal network devices already gained, which usually requires establishing a command and control communication channel between the C&C server and the target infected host [16; 17; 18; 19]. In this process, the data transmitted between the malware and the C&C server are usually encrypted with SSL, so it is difficult for the target network's defense system to determine whether the data transmitted in the communication channel are malicious communication with the C&C server or normal communication with a normal external server. However, the communication traffic between malware and C&C servers exhibits a significant difference from normal traffic [20], and APT attackers will employ domain-flux [21] techniques, and infected hosts may try to connect to a large number of domains suspected to be C&C servers.
* Lateral penetration Lateral penetration is the stage in which an APT attack spreads to infect hosts. After an APT attacker has compromised one target network host, he continuously elevates his privileges and steals data within the scope of the privileges he has gained. However,  
Figure 1: APT attack model.  
APT attack activities are not satisfied with retrieving data on only one machine and need to continuously find new internal network hosts for spreading propagation and infiltrating the core network [22].
* Data Returning The data-returning phase sends the discovered data packaged and compressed back to the C&C server to complete the theft of data. The network traffic in the data returning phase is not significantly different from normal network traffic [23], and detection of this phase is difficult to achieve.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 4 Analysis of the Influence of the Vertical Parameters of the Mega-Sub Isolated Structure
### Analysis of the Influence of Vertical Frequency Ratio  
In the following section, the vertical stiffness of the seismic isolation layer is varied to change the vertical self-oscillation frequency of the isolated substructure. The dynamic response of the main and substructure is obtained as a function of the vertical self-oscillation frequency ratio \(\eta\) (\(\eta\) = vertical self-oscillation frequency of the isolated substructure/first-order vertical frequency of the main frame) where the self-oscillation frequency of the isolated substructure is calculated according to Equation (3).  
\[\omega_{\mathrm{z}}=\sqrt{\left(K_{\mathrm{s,z}}/m_{\mathrm{s}}\right)} \tag{3}\]  
In the above equation, \(\omega_{\mathrm{z}}\) is the vertical self-oscillation frequency of the isolated substructure, \(K_{\mathrm{s,z}}\) is the vertical stiffness of the isolated substructure, taken as the sum of the vertical stiffnesses of the isolated bearings, and \(m_{\mathrm{s}}\) is the substructure mass.
# A Review of Foot-Terrain Interaction Mechanics for Heavy-Duty Legged Robots
## 4 Further Research
### Study of Effective Contact Area between Irregular Foot and Dynamic Deformable Terrain
#### 4.3.2 Construction of Resultant Force Mathematical Model  
The six-dimension force sensors are typically installed on the feet of the heavy-duty legged robots. The forces and moments in three directions are measured. When a robot travels on a slope, the proportion of normal and tangential forces acting on its foot in the resultant force is different. The difference in proportion is significant at a certain moment. Therefore, in model design, \(u\) is considered as the ratio of tangential force to normal force,  
Figure 39: Measurement of actual contact area between foot and terrain.  
as shown in Equation (34). A mathematical model for evaluating the resultant force on the foot is derived, as shown in Equation (35). Then  
\[u=F_{\Gamma}/F_{\text{N}} \tag{34}\]  
\[F_{\Sigma}=\begin{cases}F_{N}&u<0.1\\ F_{\Gamma}&u>0.9\\ (F_{N}+\tan\varphi F_{\Gamma})(1-\exp(-j/k))&0.1\leq u\leq 0.9\end{cases} \tag{35}\]
## Appendix A  
This part shows the precise description of the mixing function \(p_{i}(t)\).  
The basic structure of MWC is shown in Figure 11[31]. We assume that \(x(t)\) is a continuous time signal with a range within \(\mathcal{F}=[-1/2T,1/2T)\), where the Nyquist frequency \(f_{N\mathrm{YQ}}=1/T\).  
MWC consists of \(m\) parallel channels. In each channel, the multiband signal \(x(t)\) is multiplied with a different mixing function, and \(p_{i}(t),i=1,2,\ldots,m\). Each mixing function is periodic with the period \(T_{p}=1/f_{p}\). \(p_{i}(t)\) is set as a sign function for each of the \(M\) equal time intervals and other forms are possible, as the system only requires \(p_{i}(t)\) periods, which is expressed as follows  
\[\alpha_{ik}=p_{i}(t),\hskip 14.226378 ptk\frac{T_{p}}{M}\leq t\leq(k+1)\frac{T_{ p}}{M} \tag{1}\]  
where \(0\leq k\leq M-1\) and \(\alpha_{ik}\in\{+1,-1\}\).  
The purpose of this mixing function is to create aliases, so that the mixed signals \(x^{\prime}_{i}(t)\) have information about the entire spectrum in the baseband \(\mathcal{F}_{p}=\left[-\frac{f_{p}}{2},\frac{f_{p}}{2}\right]\). Taking the \(i\)th channel as an example, the Fourier expansion of \(p_{i}(t)\) is as below  
\[p_{i}(t)=\sum_{l=-\infty}^{+\infty}c_{il}e^{l2 nlf_{l}t} \tag{2}\]where \(c_{il}\) is the Fourier series coefficient and is expressed as  
\[c_{il}=\frac{1}{T_{p}}\int_{0}^{T_{p}}p_{i}(t)e^{-\beta\pi lf_{p}t}dt \tag{11}\]  
The Fourier transform of the mixing signal \(x_{i}^{\prime}(t)=x(t)p_{i}(t)\) is  
\[x_{i}^{\prime}(f)=\sum_{l=-\infty}^{+\infty}c_{il}X(f-lf_{p}) \tag{12}\]  
Suppose that the filter \(H(f)\) is an ideal rectangular function. Consequently, the uniform sequence \(y_{i}[n]\) has only frequencies in the \([-f_{s}/2,f_{s}/2]\). The DTFT of \(y_{i}[n]\) is expressed as  
\[Y_{i}\big{(}e^{j2\pi fT_{s}}\big{)}=\sum_{l=-L_{0}}^{+L_{0}}c_{il}X(f-lf_{p}), \qquad f\in\mathcal{F}_{s} \tag{13}\]  
where \(L_{0}\) is selected as the smallest integer as  
\[-\frac{f_{s}}{2}+(L_{0}+1)f_{p}\geq\frac{f_{nyq}}{2}\to L_{0}=\left| \frac{f_{nyq}+f_{s}}{2f_{p}}\right|-1 \tag{14}\]  
where \(f_{s}=1/T_{s}\) is the sampling rate; and \([\bullet]\) means round up.
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 2 Materials and Methods  
### Materials  
Unnamed Ground Vehicle (UGV) and Terrestrial Laser Scanner (TLS)  
Data acquisition for this work was performed with a TLS, Faro Focus 3D 330, mounted on a UGV controlled by an automatic route planner [43]. The path planner uses a 2D drawing of the building as input data, being able to calculate the number of stations needed and the positions of each scan point according to the overlap and the accuracy established [43]. Using this approach, the UGV can work autonomously, without supervision, following the path planned and operating at night to avoid interruptions during the building tasks. The TLS Faro Focus 3D 330 (Figure 1) is used as a static scanning system through a Stop&Go process. In the Stop&Go mode [44], the TLS captures data when the UGV is stopped, making a conventional static TLS measurement. Once the scan is finished, the UGV continues its route to the next target point, where another scan is performed. Thus, the TLS is fully autonomous and acquires data with a precision of \(\pm\)2 mm at 10 m.  
The Faro Focus 3D 330 consists of an infrared laser scanner that measures coordinates directly using the phase difference of the laser ray in the range of 0.60-330 m in a wavelength of 1550 nm. The field of view covers 300\({}^{\circ}\) vertically and 360\({}^{\circ}\) horizontally, with a resolution of 0.009\({}^{\circ}\) and a measurement rate of 122,000-976,000 points per second, also registering radiometric information for each point. The nominal precision of the TLS is \(\pm\)2 mm at 10 m distance in normal illumination conditions to a 90% reflective surface, with a ray divergence of 0.19 mrad. All these characteristics ensure that the quality standards in construction can be achieved and determine its selection as the scanning system. The Faro Focus system has been mounted on an UGV, Guardian Robotnik, with a payload of 100 kg, speed of 3 m/s, autonomy between 3 and 10 h in normal performance and capacity to detect occlusions and obstacles using two laser scanner SICK S300 Expert integrated at the front and the back of the UGV. Additionally, the path planning developed by [43] was incorporated to give the UGV full autonomy in the acquisition tasks.
## 2 Laser Acceleration Simulations
### Description of Few-Cycle Chirped Focused Laser Pulses  
We consider a circularly polarized laser field propagating along the \(z\)-direction. Like in [35], the vector potential for the focused ultra-short laser beam is represented as \(\mathbf{A}=\frac{E_{0}}{k_{0}}\left(\hat{x}\psi(\mathbf{r},\eta)+i\hat{y}\psi( \mathbf{r},\eta)e^{i\pi/2}\right)e^{i\eta}\). Here, the following notations have been introduced: the electric field amplitude is \(E_{0}=4\sqrt{P/c}/w_{0}\), where \(P\) is the peak power of a laser pulse, \(c\) stands for the light velocity in vacuum, and \(w_{0}\) is the laser focal radius. The phase of the field is given as \(\eta=\omega_{0}t-k_{0}z\). The wave number is \(k_{0}=\omega_{0}/c\), where \(\omega_{0}\) is the original (unchirped) frequency of laser at the focal  
Figure 1: A schematic view of the studied laser acceleration setup. A chirped few-cycle laser pulse is shot on a target. The ionized electrons are accelerated fast in the forward direction, which is followed by the more inert ions, also directly accelerated by the laser pulse. Note that the transverse target size is magnified for illustration purposes. In the simulations it is much smaller than the focal radius of the laser beam. point. When the chirping effect is taken into account, the laser frequency and radius have to be modified as follows: \(\omega=\omega_{0}\left(1+b\eta\right)\), and \(w=w_{0}/\left(1+b\eta\right)\), where \(b\) is the dimensionless chirp parameter. In these formulas, we introduced the quantities \(\psi=f(1+i\eta/s^{2})e^{i\phi_{0}-f\rho^{2}-\eta^{2}/(2s^{2})}\), \(f=i/(i+\
u/z_{r})\), \(\
u=z+\eta/(2k)\), \(\rho=r/w\), \(r=\sqrt{x^{2}+y^{2}}\), \(s=\omega_{0}\tau/2\sqrt{2\log 2}\), and \(\tau\) is pulse duration. Furthermore, \(z_{r}=kw^{2}/2\) is the Rayleigh length, \(k=\omega/c\), and \(\phi_{0}\) is a constant phase. When introducing the frequency modulation, the phase parameter \(\eta\) and the pulse length parameter \(s\) do not need to be modified. Here, \(\hat{x}\) and \(\hat{y}\) stand for unit vectors in the \(x\) and \(y\) directions orthogonal to the lasers propagation direction. Note that the temporal envelope of the laser beam is not factorized in this pulsed solution of the wave equation. The scalar potential \(\phi\) is thought to have a similar expression as that of the vector potential \(\mathbf{A}\), and it can be calculated from the Lorentz gauge condition \(\partial\phi/\partial t+\
abla\cdot\mathbf{A}=0\).
## 3 Research Methodology  
### Principle of Small Baseline Subset Time Series Analysis Technique  
Acquire a set of \(N+1\) SAR images covering the same area, record the acquisition times \((t_{0},t_{1},t_{2},\ldots,t_{N})\), register the images to a common coordinate system, and generate a series of \(M\) spatiotemporal differential interferograms with values below a specified threshold. The \(M\) following conditions should be met:  
\[\frac{N+1}{2}\leq M\leq N\cdot\frac{N+1}{2} \tag{1}\]  
The phase value of the pixel in the \(k\)-th interferogram is:  
\[\delta\varphi_{k}=\varphi(t_{n})-\varphi(t_{A})\approx\frac{4\pi}{\lambda}[d( t_{n})-d(t_{A})] \tag{2}\]  
In Equation (2), \(\varphi(t_{i})\) represents the line-of-sight cumulative deformation relative to the initial time \(t_{0}\), and \(\lambda\) is the radar wavelength.  
After correcting for terrain-induced phase errors, atmospheric delay phase, and noise phase in the obtained cumulative deformation variables, the transformed results yield the deformation rate \(v\) for high-coherence points in the study area:  
\[Bv=\delta\varphi \tag{3}\]  
In Equation (3), \(B\) represents the coefficient matrix of \(M\times N\), and \(\delta\varphi\) is a matrix composed of phase values. The deformation rate \(v\), therefore, can be expressed as:  
\[v^{T}=[\frac{\varphi_{1}}{t_{1}-t_{0}},\ldots,\frac{\varphi_{N}-\varphi_{N-1} }{t_{N}-t_{N-1}}] \tag{4}\]  
When the coefficient matrix \(B\) is of full rank, the deformation rate \(v\) can be determined using the least squares method. If the matrix is not of full rank, the singular value decomposition (SVD) method is employed to compute the deformation rate \(v\).  
Figure 2: Illustration of the study area boundary.  
After obtaining the deformation rate, a temporal integration is conducted to derive the deformation phase values over the specified time period. Multiplying the deformation phase values by the phase-to-deformation conversion coefficient \(\frac{\lambda}{4\pi}\) yields the deformation time series on the ground's line of sight (LOS) direction, as seen by the radar.  
Finally, the deformation component along the radar LOS direction \(d_{LOS}\) is transformed into the vertical deformation \(d_{v}\) component on the Earth's surface, as follows:  
\[d_{v}=\frac{d_{LOS}}{\cos\theta} \tag{5}\]  
In Equation (5), \(\theta\) represents the satellite incidence angle and \(d_{LOS}\) signifies the line of sight (LOS) deformation component.