esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Basic Kinematic Assessment  
The analysis of wedge, planar, and block toppling failure is a tridimensional-type problem. The visualization of the interactions between discontinuity planes and slopes is exceptionally complex to analyze using isometric projection but considerably simplified with stereographic projection, in which the planes are represented by points (i.e., poles). Kinematic assessment is based on the analysis of the relative orientation of discontinuity planes or poles regarding the slope, establishing four main types of failure: (a) planar, (b) wedge; (c) flexural toppling; and (d) direct toppling [25].  
Instabilities depend on the relationship between friction, the orientation of the joints, and the angle of the slope. The kinematic representation of data obtained in situ and the direct observation of evidence of instability in the slope enables a preliminary stability  
Figure 4: Type of stability: (**a**) stable slope no. 2 of the School of Natural Sciences University of Guayaquil; (**b**) Quasi stable slope no. 22 on the Ambato-Guaranda road, slopes of the Chimborazo volcano; (**c**) unstable slope no. 9 of the Cahuaji–Cotaló highland region road; (**d**) slope no. 15 in El Cajs, which presents overturning type rupture; (**e**) slope no. 14, in Papallacta (Ecuador), where block toppling is present in basaltic columns; (**f**) slope no. 5, in the Bellavista area, Guayaquil (Ecuador), which presents planar failure in siliceous siltones; and (**g**) slope no. 8, in the Coaque–Santa Teresa roadway (Ecuador), which shows wedge failure in andesitic basalts.  
assessment. This initial analysis provides a broader perspective of the problem and establishes a standard for the selection of values related to the joint set number (Jn), the favorable or unfavorable orientation of the discontinuities (O-factor), major discontinuity (SRFc), and stable and unstable structure.  
In order to assess the kinematic stability of the rock blocks, the shear strength of discontinuities is needed. In this paper, [PERSON]'s (2002) criteria for the \"frictional component\" of joints is used while considering the cohesion = 0 to be conservative in the design. The \"Frictional Component (FC) is given by the following formula [26]:  
\[\text{FC }(\text{deg})=\tan^{-1}\left(\frac{J_{r}}{J_{a}}\times J_{w}\right) \tag{1}\]
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 3 Materials and Method
### Data Preprocessing
#### 3.2.2 Sliding Window Processing  
As shown in Figure 4, using a sliding window, the input data can be formulated as an \(M\times K\) matrix, where \(K\) is the time step and \(M\) is the number of parameters, such that  
\[X=\begin{bmatrix}x^{(1)}_{t-K+1}&x^{(2)}_{t-K+1}&\cdots&x^{(M-1)}_{t-K+1}&x^{ (M)}_{t-K+1}\\ x^{(1)}_{t-K+2}&x^{(2)}_{t-K+2}&\cdots&x^{(M-1)}_{t-K+2}&x^{(M)}_{t-K+2}\\ \cdots&\cdots&\cdots&\cdots&\cdots\\ x^{(1)}_{t-1}&x^{(2)}_{t-1}&\cdots&x^{(M-1)}_{t-1}&x^{(M)}_{t-1}\\ x^{(1)}_{t}&x^{(2)}_{t}&\cdots&x^{(M-1)}_{t}&x^{(M)}_{t}\end{bmatrix}\in\Re^{M \times K}. \tag{9}\]  
Figure 3: Overall process of the proposed MTAD method.
## 3 Proposed Method
### ESSIM Method  
To quantify quality degradation of a distorted image \((g)\) with respect to a reference image \((f)\), the ESSIM is defined as  
\[ESSIM(f,g)=\frac{1}{N}\sum_{i=1}^{N}\frac{2E(f,i)E(g,i)+C}{(E(f,i))^{2}+(E(g,i ))^{2}+C}, \tag{1}\]  
where \(C\) is a small constant to avoid the division by zero. To be more specific, \(C\) has two roles in Equation (1): as already mentioned, it is necessary to avoid having a denominator equal to zero; secondly \(C\) is also a scaling factor. If \(C\longrightarrow\infty\), the result of similarity measure is 1. Further, \(E(f,i)\) is used to characterize the edge strength around pixel \(i\) in the reference image \(f\), and is determined as  
\[E(f,i)=max(E_{i}^{1,3}(f),E_{i}^{2,4}(f)), \tag{2}\]  
where \(E_{i}^{2,4}(f)\) is the edge strength in the diagonal directions and is determined as  
\[E_{i}^{2,4}(f)=|\partial f_{i}^{2}-\partial f_{i}^{4}|^{p}. \tag{3}\]  
Similarly, \(E_{i}^{1,3}(f)\) stands for the edge strength but in the vertical and horizontal directions and is computed as  
\[E_{i}^{1,3}(f)=|\partial f_{i}^{1}-\partial f_{i}^{3}|^{p}. \tag{4}\]  
In Equations (3) and (4), \(\partial f_{i}^{j}\) stands for the directional derivatives at pixel \(i\) in reference image \(f\). The applied directions in the ESSIM are the following: \(0^{\circ},45^{\circ},90^{\circ},135^{\circ}\). Further, the fractional derivatives in the ESSIM are implemented with the help of \(5\times 5\) Scharr operators [46]. Moreover, \(p\) is used to adapt the edge strength in the algorithm. The edge strength of distorted image \(g\) around pixel \(i\) is defined as  
\[E(g,i)=\begin{cases}E_{i}^{1,3}(g),\text{ if }E(f,i)=E_{i}^{1,3}(f)\\ E_{i}^{2,4}(g),\text{ if }E(f,i)=E_{i}^{2,4}(f)\end{cases} \tag{5}\]  
in order to guarantee that the edge strengths of \(f\) and \(g\) are compared in the same direction. Otherwise, \(E_{i}^{1,3}(g)\) and \(E_{i}^{2,4}(g)\) are determined similarly to Equations (3) and (4).
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 3 Communication Technologies for SafeCOP Co-CPSs
### Maritime Application  
This use case sees a bathymetry system based on a set of USV in addition to a manned vessel that drives and controls the measurement campaign. The terrestrial radio-systems, including very high frequency (VHF), high frequency (HF) and medium frequency (MF), are well established in the maritime community and are cornerstones of the mandatory global maritime distress and safety system (GMDSS) requirements for Safety of Life at Sea (SOLARS) vessels. Since 1970s, the mobile satellite communication has been used to the maritime community as well. Taking into consideration the cost and signal coverage, the bathymetry platoon uses the VHF radios as the primary communication channel. To ensure the reliability of communication, both the USV and manned vessel are also equipped with the transceiver for communication via the mobile network.  
The Maritime Robotics (Maritime Robotics is a leading provider of innovative unmanned solutions for maritime operations in harsh environments: [[https://maritimerobotics.com/](https://maritimerobotics.com/)]([https://maritimerobotics.com/](https://maritimerobotics.com/))), i.e., autonomous boat platoons provider, has developed a VHF protocol named next generation ham radio (NGHam ([[https://github.com/skagmo/ngham](https://github.com/skagmo/ngham)]([https://github.com/skagmo/ngham](https://github.com/skagmo/ngham)))) and the corresponding radio system: \"Owl VHF\".  
NGHam specifies both physical (PHY) layer and media access control (MAC) layer functions [58]. The modulation schemes supported by NGHam are 2-GMSK (Gaussian Minimum Shift Keying) and 4-GMSK which result in different data rates of the channel, i.e., 9.6 Kbps and 19.2 Kbps, respectively. Although the available data rate of the VHF channel is low, it is sufficient when transmitting the critical information (e.g., vessel speed, position and course) in a regular periodic manner. However, the packet header does not include any field of packet type/priority. To distinguish the content of the packet, such as vessel voyage information or safety control command, some flag/type needs to be inserted into the payload field of the packet.  
NGHam supports both carrier sense multiple access (CSMA) and time division multiple access (TDMA) schemes. Since message transmission between the USV and manned vessel requires the hard delay bound to ensure the safety of the cooperative bathymetry platoon, TDMA is a better option. It guarantees the worst-case end-to-end (E2E) delay through appropriately configuring the relevant parameters, such as TDMA frame length and slot size. TDMA requires synchronization among all users who access the shared channel to avoid interference caused by data transmission in consecutive slots. Synchronization can be realized referring to either some external clocks (e.g., global positioning system (e.g., GPS) or the internal clock of the master user.
# Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks
## 4 Epidemics Spreading in Multi-Layer Complex Networks via Markov Random Fields
### Considered Topologies and Scenarios  
#### 4.3.1 Random Geometric Graph and Scale-Free  
As the first combination of considered topologies, the Random Geometric Graph (RGG) with a scale-free (SF) topology is presented. This corresponds to a possible application where users use their mobile phones to communicate directly and in parallel access different social networks at the same time. The RGG topology corresponds to the direct connections between mobile phones, i.e., connections at the physical layer, while the SF topology corresponds to the interfaces that users have with each other in each social network (cyber layer). Figure 2 shows the results for different sizes of the single topology and for varying numbers of nodes of the second level topology. As expected, the larger the network, i.e., the denser it becomes, the higher the average number of infected nodes in the network. Another observation that stands out is that as the network density increases, the number of infected nodes increases slightly faster than linearly (comparing the results between curves corresponding to N_1 = 100, N_1 = 250 and N_1 = 500).  
Figure 2: Random Geometric Graph with scale-free, while N_1 = 100, N_1 = 250 and N_1 = 500.
# Pushing Mechatronic Applications to the Limits via Smart Motion Control
## 5 Pilot Applications and Key Results
### Generic Substrate Carrier  
#### 5.1.1 Overall GSC Description  
The Generic Substrate Carrier (GSC) is a stainless-steel conveyer belt for the highly accurate transport of substrates (Figure 8). It is capable of transporting practically any kind of substrate, such as paper, cardboard, or foils, but also wooden or glass panels. The device is particularly suited for the industrial inkjet market, which is expected to exploit the  
Figure 6: Motion control platform decomposition into BBs and Layers.  
Figure 7: List of application areas where developed building blocks and platform were validated and implemented: **(a)** Pilot 1—Generic substrate carrier, **(b)** Pilot 2—12-inch wafer stage, **(c)** Pilot 3—High-speed packaging machine, **(d)** Pilot 4—Big CNC machine, **(e)** Pilot 5—Medical manipulator.  
GSC for the emerging 1200 \(\times\) 1200 DPI registration challenge. Manufacturing equipment for single-pass digital printing faces an interesting challenge to deal with the growing productivity demand in combination with rising droplet registration accuracy. Although the administration speed of print-heads is still increasing, high throughput speeds and/or increased print resolution can only be achieved by using multiple heads in series. When using at least four different inks, the distance between the first and last print-head can become more than one meter. The GSC can meet the challenge when the relative registration accuracy over such a distance must be less than ten micrometers. The substrate carrier utilizes a stainless-steel conveyor belt in order to eliminate the mechanical (e.g., elasticity) properties of the substrate and prevent deformation of the substrate during transport. The substrate is clamped on the conveyor belt using vacuum technology. Many conventional steel conveyor belts and their steering systems cannot reach the previously mentioned accuracy target. During rotation, a belt will (always) translate axially with respect to the rollers of the conveyor due to imperfections in manufacturing of the belt and/or roller (e.g., accuracy of the weld perpendicularity). The GSC uses movable segmented rollers to actively control the position of the belt without deforming the belt. This allows accurate position control of transported substrates without deformation of the substrate over a long distance, which is essential for the digital single-pass inkjet industry.
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 3 The Emergency Relief Routing Problem  
### Problem Description  
Taking the 2019 epidemic as the background, we consider the following emergency relief process: a relief vehicle k departs from the emergency logistics center (\(O\)), delivers and collects a certain number of supplies for the infected (hereafter refers as patients), and finally returns to the emergency logistics center. When providing and collecting supplies, it is necessary to reach the patient's location within the time window of the patient \(i\). The patient's deprivation cost is \(DC_{i}\), the number of supplies that need to be provided is \(d_{i}\), to be collected is \(P_{i}\), and the time spent on the relief operation is \(s_{i}\). The patient can be reached before the left time window (\(a_{i}\)), but the relief operation cannot be performed until his left time window. It is not allowed to arrive at the patient after the right time window (\(b_{i}\)). When considering the relief routes, the shortest total travel distance is calculated, and the patient's deprivation cost is considered. The deprivation cost increases as the patient's waiting time increases, starting from his left time window. It is also necessary to calculate the default cost of the vehicle violating the patient's time window (\(q\)) and the default cost of breaking the load capacity (\(w\), the maximum loading of the vehicle is \(C\)). Thus, the objective function consists of the total travel distance, the deprivation costs, and the default costs multiplied by their respective coefficients. The relief vehicle needs to depart and return within the time window of the emergency logistics center (the left time window is \(E\), the right time window is \(L\)), the time of departure is \(w_{k}\), and the loading at this time is denoted as \(L_{ok}\). The vehicle travels at a constant speed \(v\). Table 1 displays the model's input parameters, and Table 2 shows the model's variables.
## 3 Results
Table 6 shows the reference peak moment, the predicted peak value of the SVM-LSTM model, and the error rate of the peak moment of the lower extremity and L5/S1. Error rates were 5.20 \(\pm\) 4.85 (%), 8.77 \(\pm\) 6.45 (%), 3.20 \(\pm\) 2.33 (%), and 5.19 \(\pm\) 3.18 (%) for the ankle, knee, hip and L5/S1, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Ankle** & **Knee** & **Hip** & **L5S1** \\ \hline Reference peak & 0.706 \(\pm\) 0.034 & 0.768 \(\pm\) 0.041 & 1.399 \(\pm\) 0.037 & 2.504 \(\pm\) 0.047 \\ moment (N\(\cdot\)m/BW) & 0.738 \(\pm\) 0.036 & 0.735 \(\pm\) 0.067 & 1.414 \(\pm\) 0.031 & 2.377 \(\pm\) 0.087 \\ \hline Error rate (\%) & 5.20 \(\pm\) 4.85 & 8.77 \(\pm\) 6.45 & 3.20 \(\pm\) 2.33 & 5.19 \(\pm\) 3.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Reference peak moment calculated from OTS, peak moment predicted from SVM-LSTM model, and error rate between these values. Figure 3: Relative RMSE values between reference moment and predicted moment by deep learning models.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 3 Computational Results and Discussion
### Sensitivity Analysis: Effect on Simulation Accuracy, Location, Electricity Price, and Costs
#### 3.4.4 PHS Cost Needed to Be Competitive  
For the three locations, we performed optimisations by modifying the PHS CAPEX in steps of 50 EUR/kW and 5 EUR/m\({}^{3}\) until we found a PHS CAPEX for which the PV-wind-PHS system becomes economically competitive with the PV-wind system without storage. The results are listed in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Zaragoza** & **Gran Canaria** & **Sabüñanigo** \\ \hline LSS system & 850 EUR/kW + 20 EUR/m\({}^{3}\) & 350 EUR/kW + 10 EUR/m\({}^{3}\) & 450 EUR/kW + 15 EUR/m\({}^{3}\) \\ PGS system & 700 EUR/kWh + 17.5 EUR/m\({}^{3}\) & 400 EUR/kW + 15 EUR/m\({}^{3}\) & 600 EUR/kW + 20 EUR/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: PHS CAPEX needed for the PV–wind–PHS system to be competitive with the PV–wind system without storage.  
Figure 32: Optimal systems found for the different PHS CAPEX: LSS system.  
Figure 33: Optimal systems found for the different PHS CAPEX: PGS system.  
The location with higher irradiation and wind speed (Gran Canaria) requires a higher reduction in PHS CAPEX, and therefore, the PHS would be worth considering given that their performance of wind and PV combined generation is very good. In Zaragoza, the PHS CAPEX is not far from the present one; here, PHS could be a good option for energy storage not only for grid services but also economically for the owner of the system. It is difficult to extrapolate these results to different locations and countries because the performance of the system depends on many variables which are locally dependent.
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 4 Seismic Attributes
### Coherence  
Coherence [97; 98; 99; 100] is a seismic attribute that measures the similarity of seismic waveforms at different locations. Coherence is basically a normalized cross-correlation computed between adjacent traces in the same survey. By normalizing the data, the amplitude and phase variability of source wavelet is eliminated, and waveform continuity can be quantified [87]. This attribute is used to highlight continuous and consistent reflectors; while its inverse indicates a poor correlation between the waveforms, thus suggesting a change in the reflectivity characteristics often caused by faults or other discontinuous geological characteristics in the seismic volume. For instance, coherence clearly reveals, without interpretation bias, features that are difficult to see in conventional seismic data, such as faults as well as buried deltas, river channels, reefs, and dewatering features [90; 101].  
The implementation of coherence in 3D seismic data, known as the \"coherence cube\", caused a significant impact on the industry [87]. According to [PERSON] and [PERSON] [98], their coherence methodology represented the pioneering approach for exposing fault surfaces within a 3D volume where no fault reflections had been previously detected.
# Conjugate Gradient Hard Thresholding Pursuit Algorithm for Sparse Signal Recovery
## 4 Convergence Analysis of CGHTP
### Real-World Image Reconstruction  
In this subsection, we investigated the performance of CGHTP to reconstruct natural images. Test images used in experiments consist of four natural images of size 512 \(\times\) 512, as shown in Figures 10a, 11a, 12a and 13a respectively. We choose the discrete wavelet transform(DWT) matrix as the sparse transform basis and the Gaussian random matrix as the measurement matrix. The sparsity level is set to one sixth of the number of samples, i.e., \(s=M/6\). In all tests, each experiment is repeated 50 times independently and we set an algorithm-stopping criterion: \(n>200\) or \(\left\|\mathbf{x}_{n}-\mathbf{x}_{n-1}\right\|_{2}<10^{-4}\left\|\mathbf{x}_{n}\right\|_{2}\). The reconstruction performance of images is usually evaluated by the Peak Signal to Noise Ratio (PSNR), which is defined as:  
\[\text{PSNR}_{\text{dB}}=10\text{log}_{10}\left(\frac{255^{2}}{\text{MSE}} \right), \tag{10}\]  
where MSE denotes the mean square error, and it can be calculated by:  
\[\text{MSE}=\frac{1}{ab}\sum_{i=1}^{a}\sum_{j=1}^{b}\left|\mathbf{X}(i,j)-\mathbf{\hat{X }}(i,j)\right|^{2}, \tag{11}\]  
Figure 8: Exact reconstruction rate vs. sparsity level for a Gaussian signal. Figure 9: Exact reconstruction rate vs. sampling rate for a Gaussian signal. where the matrices \(\mathbf{X}\) and \(\mathbf{\hat{X}}\) of size \(a\times b\) represent the original image and the reconstructed image. Meanwhile, we also consider using relative error (abbreviated as Rerr) to evaluate the quality of image reconstruction:  
\[\text{Rerr}=\big{\|}\mathbf{X}-\mathbf{\hat{X}}\big{\|}_{2}/\big{\|}\mathbf{\hat{X}}\big{\|}_ {2}. \tag{12}\]  
Table 1 shows the average reconstruction PSNR, Rerr, and reconstruction time of four reconstructed images by different algorithms at different sampling rates
# RF Energy Harvesting Wireless Communications:
## 1 Introduction  
Radio frequency (RF) energy harvesting has been envisioned as a promising method to drive low-power wireless systems [1]. In addition to being a role as an information carrier, RF waves in wireless systems also work as an energy medium to power devices for environment sensing, wireless communications, and signal processing.  
The RF energy is a perpetual resource that is widely present both indoors (e.g., Wi-Fi signal from wireless router) and outdoors (e.g., cellular and DTV signal). The RF energy harvesting wireless communication (RF-EHWC) technique introduces several attractive features into communication and sensing networks. For instance, it gives low-power wireless devices the self-sustainability, which allows those devices to operate semi-perpetually. In addition, by scavenging energy in the air, an RF-EHWC device can work without a chemical battery to achieve green communications. Moreover, RF-EHWC devices typically have light-weight structures compared to conventional facilities that collect renewable energy from wind, tide, and biomass. Therefore, they can be small in size, which allows them to work in applications where space and costs are critical [2; 3].  
The green, small-size, and sustainable features make the RF-EHWC technique suitable for a wide range of applications, such as the Internet of Things (IoT), body area networks (BANs), and smart infrastructures [4]. Those applications usually need the assistance of numerous wireless sensor nodesand low-power devices that have strong demands on sustainability and efficiency [5]. For instance, the wireless nodes deployed in the wilderness for environment monitoring and disaster warning should be green and durable. Obviously, RF-EHWC is a promising technique to meet the clean and self-sustainable requirements of these emerging applications.