esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Data
### L-Band PALSAR for Volume Mapping
DN values of the data from 2007 and 2009 were averaged to avoid small changes from backscattering caused by local environmental factors at that particular observation time (e.g., precipitation) and then converted to dB units. The data were then slope corrected, because they had been previously ortho-rectified but not slope corrected, resulting in enhancements of the backscatter from the rugged hills facing towards the sensor direction [28]. A correction method proposed by [PERSON] _et al._[32] was modified and applied to the 50 m PALSAR mosaic product. The correction of the SAR imagery is performed by applying a slope correction factor to the original SAR data. The equation for the correction is as follows:  
\[\sigma_{c}^{0}=SCF^{q}\times\sigma^{0} \tag{2}\]  
where \(\sigma^{0}c\) is the corrected sigma naught backscattering value, \(\sigma^{0}\) is the original sigma naught backscattering data, q is the diffuse scattering, which is polarization dependent, and \(SCF\) is the slope correction factor for the correction of the topography, expressed as:  
\[SCF=\frac{\cos i}{\sqrt{1+\left(\frac{\tan a\times\sin a\times\sin 2\beta}{2} \right)^{2}}} \tag{3}\]  
where \(\cos i\) is the cosine of the incidence angle, \(a\) is the slope and \(\beta\) is the aspect. The HV polarization image of PALSAR was corrected using this method. The value for the diffuse scattering depends on the environment and was unknown in this case due to the large study area with differing forest structures, so here it has been assumed that q = 1. The volume map for the whole of Japan was obtained by applying the volume model equation to the corrected 50 m PALSAR mosaic product.
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 3 The Proposed DCT-Based DFC System
### Data Detection  
As shown in Figure 1, the camera is frontally aligned with the display and captures images displayed on the electronic screen. In the process of capturing images, several noises are added to captured images. Although the noise in the data-embedded frames and the reference frames may differ, we can assume that noises in both frames are the same (as they are transmitted consecutively). In this way, the images received by the camera via a D2C link can be expressed as  
\[\mathbf{Z}_{t} =\mathbf{I}_{t(ref)}+\mathbf{N}_{t}\] \[\mathbf{Y}_{t} =\mathbf{D}_{t}+\mathbf{N}_{t}=\mathbf{C}^{H}\cdot\mathbf{D}_{F}+\mathbf{N}_{t}, \tag{10}\]  
where \(\mathbf{Z}_{t}\) is the received reference image frame, \(\mathbf{Y}_{t}\) is the received data-embedded image frame, and \(\mathbf{N}_{t}\) is the noise in the image. After receiving the images, the images are converted to the frequency domain using the same DCT conversion process as  
\[\mathbf{Z}_{F} =\mathbf{C}\cdot\mathbf{I}_{t(ref)}+\mathbf{C}\cdot\mathbf{N}_{t}=\mathbf{I}_{F(ref)} +\mathbf{N}_{F}\] \[\mathbf{Y}_{F} =\mathbf{C}\cdot\mathbf{D}_{t}+\mathbf{C}\cdot\mathbf{N}_{t}=\mathbf{D}_{F}+\mathbf{N}_{ F}=\mathbf{I}_{F}+\mathbf{X}+\mathbf{N}_{F}. \tag{11}\]  
Finally, the data are decoded using the subtraction data retrieval process that includes subtracting the reference frame from the data-embedded frame as  
\[\mathbf{\hat{X}}=\mathbf{Y}_{F}-\mathbf{Z}_{F}. \tag{12}\]  
Since \(\mathbf{\hat{X}}=\big{[}\mathbf{\hat{X}}_{1},\mathbf{\hat{X}}_{2},\dots,\mathbf{\hat{X}}_{Q} \big{]}\), the finally decoded data \(\mathbf{\hat{s}}_{q}\) can be expressed as  
\[\mathbf{\hat{s}}_{q}(l)=\hat{X}_{q}(s+l,q),\quad l=1,2,\dots,L. \tag{13}\]  
Consequently, the estimated data symbols can be expressed in the matrix form as  
\[\mathbf{\hat{s}}=\big{[}\mathbf{\hat{s}}_{1},\mathbf{\hat{s}}_{2},\dots,\mathbf{\hat{s}}_{Q} \big{]}, \tag{14}\]  
where \(\mathbf{\hat{s}}_{q}=[\mathbf{\hat{s}}_{q}(1),\dots,\mathbf{\hat{s}}_{q}(L)]^{T}\) is the estimated data symbol vector on the \(q\)th column of the received data-embedded image. Then, after channel decoding, the estimated bit can simply be given as  
\[\mathbf{\hat{b}}=\begin{cases}0&\forall\,\mathbf{\hat{s}}:\mathbf{\hat{s}}<0\\ 1&\forall\,\mathbf{\hat{s}}:\mathbf{\hat{s}}\geq 0\end{cases}. \tag{15}\]
## 4 Observation of LRB Failure Mode through Dynamic Test
### Simulation of After-Failure Response by Slip Analysis  
The shaking table tests confirmed that the dummy mass continued to slip within a limited range by friction, even after the LRB was completely broken. This behavior indirectly ensures the seismic safety of an isolated system using LRBs, even during BDBE. To verify the maximum displacement response resulting from the test, tests measuring the actual friction coefficient of the failed LRB and simulation slip analysis based on Equation (1) were performed for a simple block slip model and equation of motion on a friction surface shown in Figure 19. To obtain the actual friction coefficient in the case of total LRB rupture, static slip tests using inclined steel plates and dynamic tests were undertaken using a 1D shaking table. The experiments were repeated more than 10 times to ensure precision. The test averages of the static and dynamic friction coefficients \(\upmu_{\text{s}}\) and \(\upmu_{\text{d}}\) are 0.66 and 0.54, respectively. Figure 20 shows the displacement-time history of the sliding rigid block for this range of friction coefficients.  
\[\text{m}\ddot{\text{x}}+\upmu\cdot\text{m}\cdot\text{g}\cdot\text{sgn}(\dot{x })=-m\ddot{x}_{g} \tag{1}\]  
Figure 20 shows the results of the block slip analysis as the displacement-time history. The maximum displacements were approximately 42, 25, and 22 cm for friction coefficients of 0.5, 0.6, and 0.7, respectively. This does not show a significant difference, considering that the actual slip displacement of \(\sim\)25-30 cm appeared immediately after the dummy mass was completely damaged in the shake table test for the floor response seismic input corresponding to a DBE of 0.6 g. That is, considering that the friction coefficient with the floor was estimated as \(\sim\)0.5-0.7 through static and dynamic friction tests, it was concluded that the slip displacement until the time when the test was stopped due to buckling of the entire seismic isolation bearing was similar to the analysis result. In general, these data determine the horizontal margin distance between the superstructure and moat wall to prevent collisions with neighboring structures during a BDBE. The study suggests that safety issues such as interference with surrounding structures can be mitigated even with a complete failure of the LRB in small facilities due to a BDBE.  
Figure 19: Slip model and equation of motion for simulation of the ruptured LRB.  
Figure 20: Displacement of sliding block under measured friction coefficients.
# Improved Simplified Engineering Fault Displacement Hazard Evaluation Method for On-Fault Sites
## 4 Discussion of the Results
### Analysis of the Results
The benefit of the improvements of the simple engineering evaluation of fault displacement hazard is the realistic consideration of cases where the site is close to the S-W end of the fault in the case of lack of data on the fault activity. Figure 8 shows the example of the Paks site in Hungary obtained by simplified engineering methods developed via step-by-step implemented improvements. Figure 8: Estimates of the hazard curve for the Paks site showing the progress in developing simplified engineering hazard evaluation methods. The dashed green line is the result of the improved method, the continuous lines are the results of [3], the dashed black lines are the results of [4], and the dashed red line is the curve obtained by Equations (4) and (7). Figure 7: The AER curves of the contribution to the site hazard ruptures in the range of \(0\leq x_{i}\leq x_{s}\) for the site positions \(x_{s}/FL=0.2\) (**a**) and \(x_{s}/FL=0.5\) (**b**). The green lines refer to the AER of the expected displacement, and the dotted lines indicate the obtained maximum and minimum displacement AER curves. In [4], compensating for the lack of data on fault activity, a hazard evaluation method has been developed based on the magnitude-distance disaggregation of the seismic hazard that was evaluated by Probabilistic Seismic Hazard Analysis. It should be noted that the middle part of the Pannonian Basin is characterised as an area of diffuse seismicity. Since distance bins do not comply with the fault geometry, two options have been considered: (1) accounting for the distance bin that coincides with the site; and (2) considering all distance bins having essential contributions to the site seismic hazard. These estimates are indicated in Figure 8 by continuous lines. It was recognised that the fault geometry, the site's on-fault position and the probability that the rupture intersects the site should be accounted for even in the simple engineering hazard evaluation method. In [5], an attempt has been made to account for all these aspects strictly conservatively. In Figure 8, the black dashed line is the hazard curve calculated by this conservative method. In trying to evaluate the conservativeness of the procedure in [5], a need for improvements in the method has been recognised. First, the ruptures contributing to the site displacement should be precisely identified, considering the position of the rupture relative to the site and the on-fault position of the rupture. The improved method results in a realistic estimation of the hazard levels \(10^{-4}/\)a to \(10^{-7}/\)a, which are interesting for the safety of hazardous facilities like nuclear power plants. The advantage of the improvements is less expressed but is not negligible for the sites located in the middle of the mapped fault.
## 2 Location of the Study Area and the Geological Background
### Syn-rift Phase  
This phase signifies an extensional period during which the continental crust thinned and stretched, leading to faulting in certain places. Sequences associated with an Ordovician to Devonian, approximately 480 Ma to 360 Ma, basement block of Cape Supergroup quartzites and slates characterize this phase, which is topped by the 1 At1 unconformity, ~137 Ma (Hauterivian-aged) [36; 39]. Rifting ended in the very beginning of the Valanginian (~139 Ma). As migration alongside the Agulhas-Falkland Fracture Zone (AFFZ) began, the 1 At1 unconformity formed as a result of a marine retreat after rifting ended in the early Valanginian (~139 Ma) [36; 37; 44; 45].  
Figure 2: Chronostrigraphic sequence showing major unconformities and lithologies of the Bredasdorp Basin (modified after [29]).  
The initial transition phase sediments were deposited after the 1 At1 uncomformity's maximum flooding with surface shales (~137 Ma). During this period, fast thermal subsidence along the rift faults and asthenospheric cooling occurred in the Bredasdorp Basin. The central areas of the basin became deeper due to this activity, which decreased water circulation and produced an environment with low oxygen levels [36; 47].  
The drift phase includes the Aptian-aged 13A sequence, which is bound by the 13 At1 unconformity (~124 Ma) below. The top of the sequence and the 14 At1 unconformity (~108 Ma) below confine the Albian-aged 14A sequence, indicating a surface age of approximately 105.5 Ma [30; 41]. The drift stage's beginning can be marked back to the middle period of the Albian (14 At1), when the southwest Columbine-Agulhas arch cleared the 20 Falkland plateau, causing thermal deformation [46]. The last stages of regression occurred within the Tertiary period (Palaeocene age) when significant uplifts took place.  
For more information on the tectonic evolution, geological overview, depositional environment and characterization of source and reservoir rocks, several studies such as [3; 8; 37; 32; 35; 36; 37; 44; 47; 48], are recommended for further reading.
## 3 Materials and Methods
### Algorithms
#### 3.2.3 Support Vector Machine (SVM)  
It is a supervised machine learning technique used in both classification and regression problems [25]. Generally, an SVM is used for classification problems. An SVM classifies the data into the given classes by drawing a separating line or separating planes between data points of each class. The best separating line is known as a hyperplane and the nearest data points from the hyperplane of each class are known as support vectors. Planes parallel to the hyperplane and passing through these support vectors are known as marginal planes. For the best possible classification, an SVM's goals are to maximize the distance between both marginal planes.  
The equation of the hyperplane is:  
\[W^{T}(y)+c=0 \tag{4}\]  
where \(c\) is a constant.  
To calculate the distance of the data point from the hyperplane, let \(\Phi(y)\) be a data point vector from the hyperplane.  
\[d_{H}(\Phi(y_{0}))=\frac{\left|W^{T}\Phi(y_{0})+c\ \right|}{\|w\|} \tag{5}\]  
where \(\|w\|\) is the Euclidean norm of \(w\).  
Since support vectors are the nearest point from the hyperplane, we can calculate the distance for the support vector by minimizing Equation (5).  
\[d_{H}(\Phi(y_{0}))_{min}=min\left(\frac{\left|W^{T}\Phi(y_{0})+c\ \right|}{\|w\|}\right) \tag{6}\]  
The goal of an SVM is to maximize the minimum distance of the data points from the hyperplane.  
\[w^{*}=\left(min\Bigg{(}\frac{\left|W^{T}\Phi(y_{0})+c\ \right|}{\|w\|}\Bigg{)}\right) \tag{7}\]  
\[w^{*}=d_{H}(\Phi(y_{0}))_{min} \tag{8}\]  
As \(w^{*}\) in Equation (7) increases, optimal classification occurs among the given class labels.
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## 1 Introduction
Consequently, an inertially stabilized platform (ISP) with three gimbals is usually used to isolate the angular motion interference and to focus the surveying sensor onto the desired targets, such as the general ISP PAV80 and GSM3000 [7; 22; 23]. The use of ISP makes the lever arm problem more complicated. Generally, the surveying sensor and the IMU are rigidly mounted close together on the ISP, but the measuring center of the IMU cannot be ensured to coincide with the rotating center of the ISP. When the ISP rotates, the lever arm between the IMU center and the GPS antenna center changes; for this reason, we call it a dynamic lever arm. Since the lever arm is not constant in this situation, traditional lever arm estimation methods do not work. In [7; 12], the dynamic lever arm is decomposed into two constant lever arms to be compensated in flying missions, but they are still manually measured in advance. In order to avoid complex manual measurements and to improve surveying efficiency, we propose an automatic approach to estimating two separate lever arms that provide equivalent compensation. The rest of this paper is organized as follows: Section 2 describes the system model and the problems to be solved. Section 3 presents the proposed integrated navigation approach and analyzes the corresponding observability in detail. Section 4 verifies our method through simulation tests, and conclusions are drawn in Section 5.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### TiO\({}_{2}\)-Based NPs
#### 4.5.4 Genotoxicity  
Using _A. cepa_ root tip test, it was shown that TiO\({}_{2}\) NPs (>50 nm) applied at 0.1 and 100 g/L reduced the MI from 63.6% (control) to 59.5% and 53.5%, respectively, and at additional UV stress further decrease to 58.0% and 51.4%, respectively, was observed. Whereas at treatment with 0.1 mg/L TiO\({}_{2}\) NPs (anaphase) multiple chromatin bridges, at exposure to 10 mg/L and 100 mg/L TiO\({}_{2}\) NPs distributed and fragmented chromosomes can be observed. The chromosomal aberration of _A. cepa_ root cells can be associated with accelerated generation of ROS and higher MDA levels at the presence of TiO\({}_{2}\) NPs [245]. Similar results were observed also by [PERSON] et al. [267] or [PERSON] et al. [268] who reported that due to oxidative stress induced by of TiO\({}_{2}\) NPs the ROS levels as well as SOD and CAT activities of _A. cepa_ roots increased by 10%, 20.8%, and 12.4%, respectively, and the levels of O\({}_{2}\)\({}^{-}\) in roots showed a dose-dependent increase. TiO\({}_{2}\) applied at a dose 1000 mg/L caused DNA damage in _A. cepa_ root meristem cells, which, expressed as %DNA tail, achieved 56.54 \(\pm\) 3.82% and 37.57 \(\pm\) 3.60% for TiO\({}_{2}\) NPs of 50 and 21 nm, respectively [269].  
Considering 2.4-fold enhancement of glaucine in _Nigella arrensis_ L. plants cultivated in hydroponic system in the presence of 2500 mg/L TiO\({}_{2}\) NPs, this method seems to be suitable to achieve enhanced levels of this aporphine alkaloid in _N. arrensis_ plants [147] (Table 1).
## 3 Fault Diagnosis of the Protection System Based on Gradient Boosting Decision Tree (GBDT)
### Principle and Training Steps of Gradient Boosting Decision Tree
The schematic diagram of the GBDT multi-classification algorithm is shown in Figure 3.  
In this paper, when conducting 12-classification training for complex faults in the protection systems of intelligent substations, the fault feature information of the protection system in Table 3 is used as a variable, and the complex fault types of the protection system in Table 2 are used as the fitting target. The training process is as follows:  
1. Select the fault sample \(i\) in the training set. The fault feature information set of this sample is \(=X_{i}=\left\{A_{i}\,,I_{i},C_{i},S_{i}^{\prime}\right\}\), and the fault type is \(f_{1}\)--\"Main DSP module failure of merging unit\" in Table 2. Then, the true classification label (probability) of the fault sample in the 12 binary classifiers is the fitting target y = (1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0). Because the fault sample belongs to the first fault \(f_{1}\), '1' is used to indicate that it belongs to the fault, and '0' is used to indicate that it does not belong to the other 11 faults, forming the input consisting of the fault feature information set and the fitting target: \((X_{i},1)(X_{i},0)(X_{i},0)\)... \((X_{i},0)\);
2. Input the input into 12 weak classifiers and obtain the output results: \(F_{1}(X_{i}),\ F_{2}(X_{i})\), \(F_{3}(X_{i}),\ldots,F_{12}(X_{i})\);
3. Convert the output result into probability, as shown in Formula (11): \[p_{n}(X)=\frac{\exp(F_{n}(X_{i}))}{\sum_{k=1}^{12}\exp(F_{n}(X_{i}))},\ n=1, \ 2,\ 3,\ \ldots,\ 12\] (11)
4. Calculate the loss function and solve the negative gradient of the loss function. The loss function formula is shown in Formula (12): \[L(y,p(X))=-\sum_{n=1}^{12}y_{n}\ \log p_{n}(X),\ n=1,\ 2,\ 3,\ \ldots,\ 12\] (12)  
Figure 3: Schematic diagram of GBDT multi-classification algorithm. The negative gradient formula of the loss function is shown in Formula (13):  
\[\widetilde{y}_{n}=-\frac{\partial L(y,p(X))}{\partial F_{n}(X_{i})},\ n=1,\ 2,\ 3,\ \ldots,12 \tag{13}\]  
* Generate a new fitting target, namely \((X_{i},\widetilde{y}_{1})(X_{i},\widetilde{y}_{2})(X_{i},\widetilde{y}_{3})\)\(\ldots\)\((X_{i},\widetilde{y}_{12})\), and repeat steps 2 to 5. Iterate M times according to the above steps, and after generating M weak learners, one training is completed. The training set contains 12 kinds of complex faults in the fault set and has several corresponding training samples. After each sample of the training set is trained once, the GBDT 12 classification model with a high accuracy can be obtained. It should be noted that the values of the number of iterations M and the learning rate \(\sigma\) require multiple verifications, and this process is described in detail in Section 3.2.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 8 Assessing State-of-the-Art Defensive Measures
### Betray Oneself: A Novel Audio DeepFake Detection Model (M2S-ADD)  
Betray Oneself [75] is a new method for detecting audio deepfakes. The method focuses on identifying deepfakes via the conversion of mono (single-channel) audio into stereo (dual-channel) audio. This conversion aims to expose any discrepancies or artefacts that may have been introduced during the deepfake generation process. The researchers tested their proposed model (M2S-ADD) on the ASVspoof 2019 dataset and achieved better results than all listed baselines in their paper.  
**Procedure.** To ensure that the model was functioning correctly and producing the same outcomes as outlined in the Betray Oneself paper, we initiated the code using the pre-trained model and dataset offered by the authors. After that, we trained the model on the RFP dataset.  
**Result.** The EER obtained by applying the M2S-ADD model on the evaluation set of the RFP dataset was 18.69%. In contrast, the EER achieved in the Betray Oneself paper on the ASVspoof2019 dataset was only 1.34%. Based on these results, we suggest that with further improvement, the M2S-ADD model has the potential to yield better outcomes for PF speech detection.
## 4 SkyDRL for Multi-Cloud Service Composition Problem
### Reward Function  
The reward function is a fundamental element in deep reinforcement learning. In this approach, the optimal value is continually searched through trial and error, and the network is updated based on the feedback obtained by the interaction between the agent and the environment. This process results in the adjustment of the action selection for better performance. Hence, the reward function has a critical role in finding the optimal solution in deep reinforcement learning.  
The quality of service composition can partly reflect user satisfaction. Therefore, selecting QoS attributes to model the reward function is appropriate. Additionally, users have different preferences for QoS, with some placing more weight on response time than cost and vice versa. Hence, we use \(w\) to represent user preferences for specific QoS attributes. Cost, response time, and reliability are commonly considered QoS attributes, and we consider these factors in this paper. Furthermore, energy consumption affects service composition, and hence, it needs to be incorporated into the reward function.  
\[R_{i}=R_{i}^{\text{qos}}+R_{i}^{cc} \tag{12}\]  
\[R_{i}^{\text{qos}}=w_{c}q_{i,c}+w_{rt}q_{i,rt}+w_{rt}q_{i,rl} \tag{13}\]  
\[R_{i}^{cc}=w_{cc}\times\frac{1}{EC_{i}^{total}} \tag{14}\]  
Figure 5: State and action.  
\(R_{i}\) denotes the reward value of the \(i\)-th cloud service in a given service composition. Since the goal is to maximize the QoS value and minimize the energy consumption value of the cloud service composition, it is a multi-objective optimization problem. Therefore, we divide the reward value into two parts: the first part, \(R_{i}^{qos}\), represents the reward obtained from the QoS attributes of the cloud service, while the second part, \(R_{i}^{ec}\), represents the reward obtained from the energy consumption of the cloud service. The sum of these two parts represents the total reward value of the cloud service. Additionally, \(q_{i,c}\) denotes the cost attribute value; \(q_{i,rl}\) denotes the response time attribute value, and \(q_{i,rl}\) denotes the reliability attribute value. Furthermore, \(EC_{i}^{total}\) is defined in Section 3.2 as the total energy consumption of the cloud service. To consider user preferences for different QoS attributes, we adopt weight values for each attribute and denote them as \(w_{c}\) for cost, \(w_{rt}\) for response time, \(w_{rl}\) for reliability, and \(w_{ec}\) for energy consumption. Moreover, we have  
\[w_{c}+w_{rt}+w_{rl}+w_{ec}=1 \tag{15}\]  
We model the reward function based on the QoS and energy consumption of cloud services, which can guide the decision-making of the agent, prioritizing cloud services with higher QoS values and lower energy consumption.