esa-sceva/satcom-chunk-collection huggingface dataset:
# Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method
## 3 GTC\(-\)Facet Sub-Pixel Algorithm
When applying, it is necessary to convolute the complete image with the three partial derivative templates of the Gaussian function in order to calculate the Hessian matrix elements at the corresponding positions. However, when we perform sub-pixel edge localization, truly useful information in the image only exists in the edge transition region. Therefore, we can first perform a coarse localization of the gear contour edge to determine the edge transition region, thereby reducing the image area that needs convolution. The rough positioning of gear contour edges is achieved using the Canny operator [18], and the edge transition region can be obtained through integer level pixel edges and corresponding gradients extracted by the Canny operator. Finally, an edge transition region with a width of 5-10 pixels is retained for convolution calculation. According to the Steger algorithm [19], if there is a point \((x_{0},y_{0})\) on the integer level pixel edge of the gear contour, and \((n_{x},n_{y})\) is the absolute value of the maximum eigenvalue of the Hessian matrix at this point corresponds to the unit eigenvector, then \((n_{x},n_{y})\) is the unit normal vector (also as the unit gradient vector) of the gear edge contour. At \((x_{0},y_{0})\), referring to Equation (14), the grayscale of point \((x_{0}+tn_{x},y_{0}+tn_{y})\) in the normal direction can be expressed as follows:  
\[f(x_{0}+tn_{x},y_{0}+tn_{y})=f(x_{0},y_{0})+[tn_{x},tn_{y}][f_{x}(x_{0},y_{0}), f_{y}(x_{0},y_{0})]^{T}+\frac{1}{2}[tn_{x},tn_{y}]H(x_{0},y_{0})[tn_{x},tn_{y}]^{T} \tag{20}\]  
As shown in Figure 4, the gear profile conforms to the ramp type edge, so the sub-pixel coordinates should be located at the maximum grayscale value on the edge. Therefore, let \(\frac{\partial f}{\partial t}=0\), then the solution is as follows:  
\[t=-\frac{n_{x}r_{x}+n_{y}r_{y}}{n_{x}^{2}r_{xx}+2n_{x}n_{y}r_{xy}+n_{y}^{2}r_{ yy}} \tag{21}\]  
If \(tn_{x}\) and \(tn_{y}\) can simultaneously satisfy \(tn_{x}\in[-\frac{1}{2},\frac{1}{2}]\) and \(tn_{y}\in[-\frac{1}{2},\frac{1}{2}]\) and the sub-pixel coordinate points are located within the current pixel, then the sub-pixel coordinates of the gear contour are as follows:  
\[\left\{ \begin{array}{l}x=x_{0}+tn_{x}\\ y=y_{0}+tn_{y}\end{array} \right. \tag{22}\]  
The specific steps of the gear sub-pixel contour extraction method based on the GTC\(-\)Facet model are depicted in Figure 5.  
Figure 4: Gray distribution in the normal direction at a certain point on the profile.
# Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
## 3 Anomaly Detection
### Anomaly Detection in CPPS  
As previously said, the I4.0 paradigm emergence has some difficulties. Using anomaly detection in this context entails identifying essential components of these issues and the standard anomaly detection in time series problems. These issues vary depending on the unique context of industrial application, but a good anomaly detection technique must include the following features:  
* Generalization: It is impossible to forecast the kinds of anomalies that will arise and how they will emerge in a time series. For example, if a time series is highly periodic, techniques that account for seasonal trends will do very well in identifying deviations. Nevertheless, the same strategy would perform poorly in unstable time series. A decent anomaly detection technique must be consistently successful in an agnostic way to time series typical behavior and presume that the intended behavior is dynamic.
* Time performance: In industrial applications, anomaly detection must be discovered within usable time, since late detection can result in massive losses and jeopardize safety.
* Resource efficiency: According to the I4.0 concept, CPPS components are anticipated to be self-monitoring. As a result, components must execute anomaly detection decentralized, which raises issues about resource utilization. These components are frequently resource constrained, both in themselves and in their use of network infrastructures (i.e., communication itself must be efficient).
* Little need for labeled data: There is a scarcity of publicly accessible datasets for industrial settings under assault. Many techniques to appropriate learning need massive volumes of data, which are not feasible in this scenario. Furthermore, depending on the labeled data, techniques may eventually have difficulties adapting effectively if  
Figure 6: Types of network anomaly detection methods.  
the system since the model would overfit the data from which it has learned, which would be contrary to the feature indicated in the first bullet point.
* Robustness and safety: Components must be able to adapt to unfavorable situations by continuing regular operation without jeopardizing the equipment's or human life's safety.  
These difficulties are not trivial, and to the best of our knowledge, no solution seeks to handle all of them simultaneously. Instead, they focus on a subset of them most of the time. Some of these challenges motivate the design and development of AIS applied to anomaly detection.
## 4 Discussion
### Advantages and Limitations  
The main advantages of the RQA method are simplicity, non-invasiveness, and automation, which reduces subjectivity. The method is less expensive and more practical compared to traditional methods that require complex equipment and human intervention for interpretation. The issue of time consumption is likewise addressed by the automatic method. After 30 to 40 min (including the preparation, exercise, and recovery phases) of the CPET test, the time needed to determine metabolic thresholds drops from 10 s of minutes for an operator to about 3 s. This is the amount of time needed for the computational time of the Python program, which includes all stages (preprocessing, data cleaning, and threshold calculation). This proves that it is more than possible to include this automatic system for real-time detection in wearable technology.  
Limitations of the study include the number of subjects: a larger and more diverse sample could provide a more comprehensive understanding of the methodology's applicability and accuracy. Group C is limited to a specific cardiovascular pathology; further investigations are needed to verify the applicability of the methodology to individuals with different health challenges. The within-subject variability could be studied if multiple tests on the same subject were conducted. The sample consisted of individuals from a predominantly European background. We acknowledge the importance of ethnic diversity and plan to conduct further studies on more heterogeneous populations to investigate any potential differences and further validate our method.
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 6 Numerical Results
### Scalability and Complexity Evaluation  
In this section, detailed scalability analysis and complexity evaluation are provided in terms of the framework performance for increasing number of evacuees and available evacuation routes. ESCAPE was tested and evaluated with an Intel(R) Core(TM) 2 Duo CPU 77500 @ 2.20 GHz laptop with 2.00 GB RAM. For demonstration purposes and without loss of generality, we considered that evacuees have mobile devices of similar computing capabilities, e.g., a commercial smartphone device. Specifically, Figure 4a presents the necessary reinforcement-learning component's convergence iterations to PNE, the corresponding real execution time, as well as the average reward probability (discussed in Section 3), as the number of evacuees increases. We observed that the proposed ESCAPE framework scales quite well with respect to the increasing number of evacuees, presenting almost linear behavior. Moreover, it was observed that the average reward function decreases as the number of evacuees increases due to the fact that the evacuation routes become more congested and eventually evacuees tend to choose routes where the number of evacuees that are already in, i.e., \(|M|_{\sigma}\), is high. Thus, based on Equation (1), the average reward function decreases. In Figure 4b, we present the behavior of the proposed framework in terms of the required reinforcement-learning algorithmic component's iterations to converge to the PNE and of the average reward function as the number of the available evacuation routes increases. We can clearly discern that, while the number of evacuation routes increases, the number of ESCAPE iterations and also the real execution time decrease because routes are more; as a result, evacuees have the potential of exploring the disaster area more and eventually choosing evacuation routes that are not so congested. Consequently, the reinforcement-learning component needs a smaller number of iterations to reach the PNE. For exactly the same reason, the average reward function increases as the number of the available evacuation routes increases.
## 5 Results
### Topology 1: Results
#### 5.1.6 Scenario 6  
In Scenario 6, the links on the path h26->h30->27 were emulated with less delay and rate limit and a packet loss of 10%, whereas the links on the right path h26->h29->27 were configured with a long delay and no packet loss. static was configured to select the h26->h30->27 path.  
Figure 12(a) shows that the average response time with snnr was 15-40% and 2-35% better than snndp and snnd, except at the bundle rate of 10 bundles/s. Both snnr and snndp showed worse performance than static at lower bundle rates and up to 80% better performances for bundle rates of 8-10 bundles/s. snndp was worse than snnd at lower bundle rates, but showed 7-41% better response at 9-10 bundles/s. snndp was 28-100% worse than snnp.  
Figure 12(b) shows the packet loss percentage with snnr being up to 50-93% better than snndp and snnd. snnr showed 1-130% worse performance than snnp, but showed 20-66% better performance at bundle rates of 1-3 bundles/s. snnr and snndp showed 65-95% and 14-60% better packet loss percentage than static. snndp showed up to 50% and 500% inferior performance to snnd and snnp.  
Figure 12(c) shows that snnr had 30-200% and 15-130% better throughput than snndp and snnd. snnp had 2-17% and 23-60% better throughput than snnr and snndp. snndp showed up to 47% lower throughput than snnd. Both snnr and snndp showed 143-381% and 17-267% better throughput than static.  
Figure 13: Routing performance: Scenario 6. (**a**) Average response time; (**b**) Packet loss ratio (%); (**c**) Throughput.  
Figure 12: Link selection at Node h26: Scenario 5.  
Figure 14 shows that snnd and snndp selected h30 most since both intended to optimize delay, whereas snnr and snnp selected h29 most.
# A New Blockchain-Based Multi-Level Location Secure Sharing Scheme
## 4 Our Proposed Scheme  
### System Model  
The system model of our proposed scheme is shown in Figure 2.  
In addition to the _Miners_ that maintain the blockchain, the proposed blockchain-based, multi-level, location-secure-sharing scheme consists of three entities: _LP_, _LD_ and _Fnode_. Any user, if they have sufficient computing and storage capacity, can also act as a _Minter_ to maintain the blockchain to gain some benefits.  
Figure 1: Merkle Tree.  
\(LP\) is the owner and the provider of location data. \(LP\) sets the number of access levels \(N\) and the accuracy \(r=\{r_{1},r_{2},...,r_{N}\}\) according to the demand of \(LD\)s.  
\(LD\) is the requester of location data. \(LD_{i}\) denotes the \(LD\) with level ID \(i\), \(i\in[0,N]\); the larger the level ID, the lower the access level. \(LD_{k}\) is allowed to determine that the \(LP\) is located in a circular area with radius \(r_{k}\), \(k\in[1,N]\). \(LD_{0}\) is allowed to obtain the exact location of \(LP\). Before applying for \(LP\)'s location data, \(LD\) calls the smart contract \(SC\) to record the license for location-sharing into the blockchain. The license contains the addresses (public-key) of \(LD\) and \(LP\), and \(LD\)'s level ID. \(LD\) uses the license to obtain the secret key \(k_{ud}\) for location updates that \(LP\) or other \(LD\)s have received. Therefore, \(LD\) is not only the data requester, but also the provider. In addition, the license has timeliness. After the block where the license is recorded is mined, the license is only valid within a valid time. With the blockchain, \(LD\) can verify the received data.  
\(Fnode\) verifies, reserves and provides \(LD\) the virtual location data from \(LP\) according to the valid license, which is similar to the queried network node mentioned in Section 8 of [30]. However, in our proposed scheme, only the root node of Merkle tree is recorded in the blockchain, not the data to be verified. \(Fnode\) can be a network node that intends to gain benefits and be honest but curious.  
In our proposed scheme, the smart contract \(SC\) is designed to do two works: (1) Implement attribute-based access control. \(LD\) calls \(SC\) to record a license for location-sharing into the blockchain. (2) Establish an incentive and punishment mechanism. Ensure that nodes can work honestly in the network. We will specifically explain the work of (1) and (2) in Sections 4.3 and 4.4.
# Characterization of Monovacancy Defects in Vanadium Diselenide Monolayer: A DFT Study
## 2 Materials and Methods  
DFT-based calculations were conducted in the Vienna Ab Initio Simulation Package (VASP). The valence electron and ionic core interactions were treated with the projector augmented wave (PAW) method. The generalized gradient approximation (GGA) with the Perdew\(-\)Burke\(-\)Ernzerhof (PBE) exchange-correlation density functional [63] was implemented for geometry optimization and electronic structure calculations. The Van der Waals interactions were treated with the Becke88 (optB88) optimization functional [64], as this functional is widely used to study the structure of defects in 2D materials [43; 65]. Due to the consideration of systems based on transition metal state, spin-polarized DFT calculations were performed [66; 67; 68]. A cut-off energy of 400 eV was used to expand the wavefunctions. The 2D Brillouin zone integration was sampled by a 12 \(\times\) 12 \(\times\) 1 \(k\)-grid mesh for the unit cell structure optimization, while a 5 \(\times\) 5 \(\times\) 1 \(k\)-grid mesh was used for structure optimization and electronic property calculations of supercell systems. The proper choice of supercell size is essential to avoid possible inter-vacancy interactions in the replicated cells. In this work, to study defects in the VSe\({}_{2}\) monolayer, a 5 \(\times\) 5 supercell with a vacuum layer of a thickness larger than 20 A was selected based on previous studies [69; 70] to avoid inter-vacancy interactions. In the supercell systems optimization calculations, the atomic positions were freely optimized until the atomic force on each atom was smaller than 0.001 eV/A. The defect migration energies \(E_{\text{migration}}\) were estimated following the climbing image\(-\)nudged elastic band (NEB) method [71].  
The formation energy \(E_{\text{form}}\) of a particular defect was calculated as follows:  
\[E_{\text{form}}=E_{\text{(supercell with defect)}}-E_{\text{(perfect supercell)}}+n\ \mu(\text{V})+n\ \mu(\text{Se}) \tag{1}\]  
where \(E_{\text{(supercell with defect)}}\) is the energy of the supercell with a given defect, \(E_{\text{(perfect supercell)}}\) is the energy of the perfect supercell, \(\mu(\text{V})\) and \(\mu(\text{Se})\) are the chemical potentials of V and Se atoms, respectively [72; 73; 74], and \(n=-1\) corresponds to the case when the defect is a single vacancy.  
The theory for simulating scanning tunneling microscopy imaging via DFT calculations is based on the Tersoff-Hamann approach [75; 76], where the simulated STM image depends on the scanning distance between the surface of the sample and the tip. Equation (2) describes tunneling from the occupied states of the sample to the tip.  
\[I\ (r,\ E)\ \approx\int_{E_{f}}^{E_{f}+eV}dE\ n(r,E), \tag{2}\]where the tunneling current \(I\), which depends on the position of the tip \(r\) and the voltage applied \(V\), can be found via the integrated local density of states.
# Advances in Permeation of Solutes into Hair: Influencing Factors and Theoretical Models
## 4 Permeation Models of Solutes in Hair  
The permeation models of solutes in hair include the two-part/two-state model proposed by [PERSON] et al. [62] and [PERSON] et al. [47], the porous media diffusion model proposed by [PERSON] et al. [28], the homogenous medium diffusion models proposed by [PERSON] [44] and [PERSON] et al. [56], and the heterogeneous media distribution and diffusion models proposed by [PERSON] et al. [26] and [PERSON] et al. [63]. The permeation models of solutes in hair are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Permeation Model of Solute** & **Factors** & **Factors** \\
**in Hair** & **under Consideration** & **out of Consideration** \\ \hline \multirow{2}{*}{Two-Part/Two-State Model} & Hair structure and composition, hair & Hair cuticle, the physical and chemical \\  & moisture content, solvent temperature, & properties of solute, hair \\  & and pH & charge characteristics \\ \hline \multirow{2}{*}{Porous Media Diffusion Model} & The size, shape, and charge properties & Limited data, hair composition, \\  & of solute & solute hydrophobicity \\ \hline \multirow{2}{*}{Homogeneous Diffusion Model} & \multirow{2}{*}{Fick diffusion law} & Hair microstructure and composition, \\  & & physicochemical properties of solute, \\  & & and solvent pH \\ \hline \multirow{4}{*}{Heterogeneous Partition and Diffusion Model} & Hair microstructure and composition; solute & Limited data, charge characteristics, \\  & hydrophobicity; solute dissociation constant & and solvent pH \\ \cline{1-1}  & Hair composition and charge properties, & \\ \cline{1-1}  & solute hydrophobicity, dissociation, charge & Hair permeation channel and \\ \cline{1-1}  & properties, and solvent pH & solvent temperature \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main permeation models of solutes in hair.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Factors** & **References** \\ \hline \multirow{6}{*}{Hair} & Pore size, shape, and density & [27; 28; 44] \\  & Transcellular/intercellular channel & [1; 11; 38; 45; 46] \\  & Charge characteristics & [11; 23; 26] \\  & Swelability & [11; 57; 58] \\  & Wettability of surface & [55; 59] \\  & Keratin binding & [51] \\ \hline \multirow{2}{*}{Solute} & Size and shape & [28; 44; 47; 48] \\  & Charge characteristics & [23; 26; 28; 55] \\  & Hydrophobicity & [26; 49; 50; 51; 52] \\ \hline \multirow{2}{*}{Solvent conditions} & pH & [11; 23; 28; 55; 56; 57; 58; 59; 60; 61] \\  & Temperature & [23; 44; 56] \\ \cline{1-1}  & Composition of solvent & [55; 56] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main factors that affect permeation and absorption of solutes in hair.
## 3 Results and Discussion
The conductance from the Lorentzian fitted curve is presented in Table 4. The agreement with DFT-Landauer values was very good, where the structures with the widest peaks showed a conductance closest to DFT-Landauer. Table 4 also reports the conductance values calculated from the Lorentzian fit using the DFT + \(\Sigma\) resonance positions. Agreement with experiment was significantly improved. For the adatom-pyramid and trimer-pyramid structures, the corrected conductance was 1.0 \(\times\) 10\({}^{-2}\) G\({}_{0}\). This value still exceeded the experimental conductance, but only for a factor of about 1.6, a substantial improvement as compared to the previous factor of 10. For the adatom-pyramid and trimer-pyramid structures, the corrected conductance was about 6.3 \(\times\) 10\({}^{-3}\) G\({}_{0}\), which perfectly matches with the experimental value [25; 51; 52]. Furthermore, for the pyramid-pyramid structure, the corrected conductance was 3.5 \(\times\) 10\({}^{-3}\) G\({}_{0}\), even falling below the experimental conductance value by a factor of about 1.8. Additionally, the variation between the calculated conductance values among all tip structures was modest, about 18%. This is comparable to the experimental variation of conductance, about 8% [25]. Therefore, the combination of corrections to resonance positions within the DFT + \(\Sigma\) methodology, and a Lorentzian model of conductance, leads to a very good quantitative agreement with measured conductance values of BDA. The approach that we have described here would be of use to calculate the level-corrected conductance for broad classes of molecular junctions from standard DFT-Landauer calculations and rather straightforward corrections to resonance positions, combined with a Lorentzian transport model.
## 5 LMS Channel Models
### Three-State Markov Channel Model  
Figure 4 shows a three-state Markov model for LMS communications. This model proposes considering the receiving signals as composed of a LoS component and multipath components. Three types of fading channels are defined, state #1: LoS condition; state #2: moderate shadowing condition; state #3: deep shadowing condition. The change among channel states is a relatively slow process [41]. In this model, each state represents a specific channel state and, in particular, \(S1,S2,S3\) denote the respective channel states, and \(P_{ij}\) is the probability that the Markov process will go from state #i to state #j. The switching among each state is described by a transition matrix \(P\), which is represented in Figure 7.  
Based on the properties of the Markov process, a stationary state vector \(\hat{\pi}\) is calculated as defined, \(\hat{\pi}\cdot P=P\); then:  
\[\hat{\pi}=(\pi_{1}\pi_{2}\pi_{3}). \tag{17}\]  
Many papers in the literature propose the use of a three-state Markov channel model, taking into account the LoS, moderate shadowing, and deep shadowing or blackage [42; 43; 41; 11; 23]. In [44] the authors present a LMS propagation channel model/simulator based on a three-state Markov model plus the Loo distribution. They provide the results of an experimental campaign performed at different elevation angles and environments. Another work proposing this type of model is [41]. In this work, the authors present a model that is capable of describing both narrow- and wide-band conditions, and it is also suitable for links in both GEO and NGEO satellites. Moreover, they provide model parameters extracted from a comprehensive experiment in different environments and elevation angles at the L-, S-, and Ka-bands. A measurement campaign for the LMS systems in the Ku-band is presented  
Figure 4: Three-state Markov channel model.  
in [45], where the authors provide a series of experiments achieving data and comparing two-state and three-state channel models, taking into account the main propagation phenomena in a satellite environment.