esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
## 3 Technologies
### Algorithms
#### 3.2.3 PSA in IGSA  
PSA is an optimized method without any gradient information for any objective function, which is suitable to solve a variety of optimization problems. It computes a sequence of points that may or may not approach the optimal point. PSA starts with base points (or initial points) which are provided by the GSA. Around the base point, a set of points called mesh would be established. The mesh is created by adding the current point to a scalar multiple of a set of vectors called a pattern. The algorithm adaptively adjusts its mesh size and base points by fitness value comparison of the base point and mesh points. If a point in mesh has a better objective fitness value, it becomes the current point at the next iteration. The features of PSA ensure its superior exploitation ability which compensates the insufficiency of GSA.  
The PSA process in IGSA is as follows:  
1. Building pattern. The PSA pattern is obtained by constructing a group of vectors. The group consists of a list of new points around the base point, which is used for the fine search of a base point. The vector of \(i\)-th base point is built in this way: \[\Omega_{i}=[I,-I]_{2D\times 2D},\] (24) where \(I\) is an identity matrix, \(D\) is the encoded length of spectrum allocation, \(\Omega_{i}\) is a vector group built for the base point \(X_{i}\).
2. Forming mesh. The PSA establishes a series of points centered around the base point, which is called mesh. The following formula is used to generate mesh: \[\{S_{i}\}=X_{i}+\Delta_{m}\cdot\{\Omega_{i}\},\] (25) where \(\{S_{i}\}\) represents the new alternative point set of the base point \(X_{i}\), \(\Delta_{m}\) is the mesh size.
3. Polling. In the search process of PSA, the optimum point among \(S_{i}\) and original base point is selected to be the new base point of the next iteration, the new point is denoted as \(X_{i+1}\). Polling has two kinds of results: polling success and failure. Polling success means that an alternative point in mesh is better than the base point, otherwise polling failure.
4. Adjusting mesh size. If polling success, the new alternative point replaces the base point and then use Equation (26) to enlarge mesh size (or step size) of searching, otherwise, the original base point is retained and then use Equation (27) to shrink the mesh size. \[\Delta_{m+1}=\beta_{e}\times\Delta_{m},\] (26) \[\Delta_{m+1}=\beta_{s}\times\Delta_{m}.\] (27) where \(\beta_{e}\) and \(\beta_{s}\) denote the enlarge factor and shrink factor, respectively.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 5 Security Analysis of WoT
### WoT Attacks
#### 5.3.2 A2: Eavesdropping  
Eavesdropping involves intercepting communication between WoT devices to steal sensitive information. This threat primarily targets the network layer and the web layer, where unencrypted communication and weak network security can be exploited. The vulnerability lies in the failure to implement strong encryption protocols for data in transit. The consequences of eavesdropping include data leakage and privacy violations, which can result in the misuse of intercepted data for identity theft [4].
# Probabilistic Seismic Hazard Assessment of the Southwestern Region of Saudi Arabia
## 2 Methodology
### Earthquake and Focal Mechanism Catalogs
In contrast, the last approach introduces a probabilistic model-based approach, utilizing statistical methods such as Bayesian frameworks to assess the conditional probability of earthquakes being aftershocks given the occurrence of a mainshock. Overall, these techniques are used to find earthquakes in the catalog that fall within the same temporal and spatial window, determining the window size based on the corresponding magnitude. The main event is defined as the largest earthquake found during the searching process. This declustering technique is essential since PSHA's computations for any region frequently assumes that the earthquake occurrence follows a distinct distribution in both time and space (Poissonian distribution) [35]. The main rift of the Red Sea, situated between the Arabian and Nubian plates to the east and west, is where the declustered earthquakes shown in Figure 2 show a relatively greater concentration of seismic activity. The seismic source model in this work was defined by employing data from focal mechanism solutions that were accessible until 2015. This was carried out to obtain indicative evidence of the seismotectonic conditions and the prevalent stress regime, in addition to aiding a better characterization of prospective seismic zones in the area. We primarily accessed the inventory of focal mechanisms created in [42]. Certain published studies [55; 56] were given the greatest priority, followed by data from the Swiss Seismological Service for events with magnitudes above Mw 5.5 since 1976, the GCMT, and finally the NEIC-USGS catalog. The frequency of normal-faulting solutions along the Red Sea Rift between the Nubian and Arabian Plates is clearly observed in Figure 2. Strike-slip faulting processes, the second most prevalent type, are clearly visible at the transverse faults of the Red Sea Rift and are predominant in the Gulf of Aden area. Lastly, in a few cases, reverse-faulting mechanisms are observed; these may be connected to local tectonic processes.
_Article_  
**Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory**  
**[PERSON] 1,*\(\copyright\), [PERSON] 1, [PERSON] 1\(\copyright\), [PERSON] 2\(\copyright\) and [PERSON] 1\(\copyright\)**  
Received: 31 October 2019; Accepted: 6 December 2019; Published: 10 December 2019  
**Abstract:** Landslide susceptibility mapping (LSM) can serve as a basis for analyzing and assessing the degree of landslide susceptibility in a region. This study uses the object-based geons aggregation model to map landslide susceptibility for all of Austria and evaluates whether an additional implementation of the Dempster-Shafer theory (DST) could improve the results. For the whole of Austria, we used nine conditioning factors: elevation, slope, aspect, land cover, rainfall, distance to drainage, distance to faults, distance to roads, and lithology, and assessed the performance and accuracy of the model using the area under the curve (AUC) for the receiver operating characteristics (ROC). We used three scale parameters for the geons model to evaluate the impact of the scale parameter on the performance of LSM. The results were similar for the three scale parameters. Applying the Dempster-Shafer theory could significantly improve the results of the object-based geons model. The accuracy of the DST-derived LSM for Austria improved and the respective AUC value increased from 0.84 to 0.93. The resulting LSMs from the geons model provide meaningful units independent of administrative boundaries, which can be beneficial to planners and policymakers.  
**Keywords:** landslide susceptibility mapping; geons; Dempster-Shafer theory; object-based; natural hazards
## 4 Mathematical Model for Optimal Design
### Constraint Creation  
To better ensure the comprehensive performance of the 6-UPS parallel mechanism in the optimization process, in addition to the objective function, the displacement of the prismatic pair of the drive branch chain, the rotation angle of the spherical pair and the universal pair, and the interference of the drive branch chain are used as constraints in the optimization process.  
The telescopic length of the drive branch chain determines the stroke range of the prismatic pair, and the displacement constraint of the prismatic pair is expressed as  
\[P_{\min}\leq P_{i}\leq P_{\max} \tag{8}\]  
where \(P_{\min}\) is the minimum displacements allowed by the prismatic pair, \(P_{\min}=-300\) mm; \(P_{\max}\) is the maximum displacements allowed by the prismatic pair, \(P_{\max}=300\) mm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Variable** & **Symbol** & **Unit** & **Initial Value** & **Lower Value** & **Upper Value** \\ \hline \(x_{1}\) & \(r\) & mm & 600 & 500 & 700 \\ \(x_{2}\) & \(R\) & mm & 850 & 800 & 1000 \\ \(x_{3}\) & \(\alpha\) & (\({}^{\circ}\)) & 90 & 80 & 95 \\ \(x_{4}\) & \(\beta\) & (\({}^{\circ}\)) & 25 & 20 & 35 \\ \(x_{5}\) & \(H\) &The rotation angle \(\theta_{i}\) of the spherical pair and the universal pair cannot exceed its maximum allowable rotation angle \(\theta_{\rm max}\), whose constraint is expressed as  
\[\theta_{\rm i}=\arccos\frac{\mathbf{L}_{i}\cdot(\sigma\mathbf{n}_{i})}{|\mathbf{L}_{i}|}\leq \theta_{\rm max} \tag{9}\]  
where \(\mathbf{L}_{i}\) is the length vector of the drive branch chain; \(\sigma\) is the attitude of the hinge point relative to the ICS; \(\mathbf{n}_{i}\) is the \(Z\) axis vector of the \(i\)th hinge point; \(\theta_{\rm max}\) is the maximum allowable rotation angle, \(\theta_{\rm max}=30^{\circ}\).  
Due to the physical dimensions of the drive branch chains, interference may occur between the branch chains when the mechanism is in motion. Assuming that each drive branch chain is cylindrical, and its diameter is \(D\), the constraint that the drive branch chain does not interfere is expressed as  
\[D\leq D_{i} \tag{10}\]  
where \(D_{i}\) is the shortest distance between two adjacent centerlines.
# A New Understanding and Modelling of TSP and BP Indices Compared to Safety IMO Ship Requirements
## 5 Conclusions  
With the genesis of escorting operations, it was soon shown that BP was not the only parameter that allows us to analyse the full performance of tugs, nor can it be used for comparative purposes between different tugs. Furthermore, as it is vital to know the TSP developed by an escort tug at different speeds (unknown many times), and given the lack of research works in the TSP subject, a case study with a number representative of an escort tug fleet was conducted. With this information, one mathematical model was proposed to ascertain BP; in the strange event that this parameter is not available or in case of doubts, a mathematical model was proposed to calculate TSP exerted by tugs based on their main characteristics, and another model was developed to ascertain the TSP of tugs using only two variables. One of these variables is BP, so this will allow us to relate TSP and BP. The last model proposed, employing a real case study, was used to obtain the TSP demanded by assisted vessels.  
Considering that all the proposed mathematical models had a very good precision, afterwards, we matched the TSP equation demanded by tanker vessels with the TSP equation provided by tugs. In this way, the requirements of tugs expressed in BP can be obtained as a function of rudder angle and speed. Subsequently, using the third proposed model, IMO TSP is expressed in BP, i.e., the best-known parameter of a tug fleet. Finally, we carried out a comparison between the TSP IMO guidelines and the results of the proposed models, concluding that IMO guidelines should be over-reviewed in excess and/or considering the rudder angle maximum to be applied by the assisted ship.  
Conceptualisation, J.M.P.-C., J.A.O. and E.A.P.; methodology, J.M.P.-C., J.A.O. and E.A.P.; validation, J.M.P.-C., J.A.O. and E.A.P.; formal analysis, J.M.P.-C., J.A.O. and E.A.P.; investigation, J.M.P.-C., J.A.O. and E.A.P.; data curation, J.M.P.-C., J.A.O. and E.A.P.; writing--original draft preparation, J.M.P.-C., J.A.O. and E.A.P.; writing--review and editing, J.M.P.-C., J.A.O. and E.A.P. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Surface graph for IMO TSP upper limit.  
Figure 9: Surface graph for IMO TSP lower limit.  
**Funding:** This research was funded by the Spanish Ministry of Education and the University of A Coruna (Spain) in their research project to reduce energy consumption in ships (Grant No. 64900).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors wish to express their deepest gratitude to the Sustainability Specialisation Campus of the University of A Coruna for the administrative and technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Literature Review
### SIP-Based VoIP over TCP
Figure 2: Diagram showing the retransmission mechanism used by TCP when a packet is lost or delayed. Recreation from [PERSON], [PERSON] and [PERSON] (2014). (**a**) No lost packets; (**b**) Packet loss occurred. [PERSON] and [PERSON] [5] propose a \"disorder TCP transmission strategy\" to avoid the delay caused by TCP's retransmission mechanism following a dropped packet. Their strategy aims to counter the same issue as [PERSON], [PERSON], and [PERSON], pictured in Figure 2, while avoiding the setbacks that arise when using multiple connections. By adding the socket option \"SO_UNORDERED\", the application layer disregards the consecutiveness of the packets, instead reading data as it arrives. This more intuitive approach means lost packets are skipped over, avoiding the delay of retransmission. To identify the order of the packets, [PERSON] and [PERSON] [5] make use of a strategy that encodes the data of a packet with boundary identifiers. Firstly, the identifier byte \"0x00 B\" is inserted into both the start and the end of each complete packet before arriving at the application layer of the receiver. Here, the packets must be decoded to restore the complete original voice data. From the evaluation conducted by [PERSON] and [PERSON], over 90% of the voice data transmitted using disorder-TCP could be heard normally up to a packet loss rate of 2%. This is significantly greater than that of basic TCP, which drops below 90% coherency at just 0.7% packet loss rate, and only slightly below that of UDP. However, the mechanisms involved in disorder-TCP (data coding and boundary identification) require a significantly larger portion of CPU power and processing time than both standard TCP and UDP. When selecting a VoTCP mechanism for the design in this paper, the above techniques, and many others, were considered. While the design from [PERSON] and [PERSON] does have its advantages, [PERSON], [PERSON] and [PERSON]'s VoMTCP system would be more appropriate due to the application for the design. As the VoTCP mechanism would only be activated when the network was currently suffering a DoS attack, it is expected that the rate of packet loss would be consistently high. As a result, the flaw of [PERSON], [PERSON] and [PERSON]'s design (that it becomes less effective at low packet loss rates) becomes redundant.
# Determination of Kinematic and Dynamic Characteristics of Oscillating Conveyor Mechanism
## 3 Methodology
For each time step, we iteratively calculate the angular velocity \(\omega_{k}\) and the angular position \(\varphi_{k}\). The total simulation time is divided into \(N\) steps, and the time step \(h\) determines the accuracy of the approximation. Initial conditions are specified at t = 0: \(\omega_{0}=0\) and \(\varphi_{0}=0\). The equation of motion is discretized using the Euler method, leading to the iterative formulas for \(\omega_{k+1}\) and \(\varphi_{k+1}\). Stability of the solution depends on the choice of step size \(h\), with smaller values yielding more accurate results at the cost of increased computational time. The accuracy of the method is first-order, meaning the error decreases quadratically with the step size \(h\). The computational efficiency is an important consideration, especially if the system is large or requires many time steps.
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 3 Robustness of M-estimator
### Finite Breakdown Point  
The above asymptotic breakdown point is defined by the asymptotic behavior of the M-estimator. To evaluate this quantity for finite sample size data, there are several variants known in the literature. For our study, we focused on the finite breakdown point (FBP) based on replacement of the true data [36, 37].  
Let \(\hat{\theta}_{n}\) be an M-estimator and consider a datum \(\mathbf{x}=\{x_{1},x_{2}\ldots,x_{n}\}\) of the sample size \(n\). Denote by \(\mathbf{x};\mathbf{y}_{m}\) one of possible data obtained by replacing \(n-m\) elements of \(\mathbf{x}\) by \(\mathbf{y}_{m}=\{y_{1},y_{2},\ldots,y_{m}\}\). Intuitively, the FBP of the estimator \(\hat{\theta}_{n}\) for \(\mathbf{x}\) is defined by the maximum ratio \(\frac{m}{n}\), in which \(\hat{\theta}_{n}(\mathbf{x};\mathbf{y}_{m})\) behaves normally. This FBP is denoted by \(\varepsilon_{n}^{*}\left(\hat{\theta}_{n},\mathbf{x}\right)\). Typically, \(\varepsilon_{n}^{*}(\hat{\theta}_{n},\mathbf{x})\) is independent of the datum \(\mathbf{x}\). It can be proven that the FBP converges to the asymptotic BP in the limit \(n\to\infty\)[8].  
The formal definition of the FBP is as follows. Let \(\mathcal{X}_{\mathbf{x}}^{m}\) be a set of all possible data whose intersection with \(\mathbf{x}\) is \(n-m\), i.e.,  
\[\mathcal{X}_{\mathbf{x}}^{m}=\{\mathbf{y}:\#(\mathbf{y})=n,\#(\mathbf{x}\cap \mathbf{y})=n-m\}.\]  
The FBP for \(\mathbf{x}\) is given by:  
\[\varepsilon_{n}^{*}\left(\hat{\theta}_{n},\mathbf{x}\right)= \frac{m*}{n}, \tag{14}\] \[m^{*}=\max\left\{m\geq 0:\forall\mathbf{y}\in\mathcal{X}_{ \mathbf{x}}^{m},\ \hat{\theta}_{n}(\mathbf{y})\
otin\partial\Theta\cup\{\pm\infty\}\right\}.\]  
In our simulation for the FBP, we randomly generated outliers obeying the normal distribution whose expectation value has a different order from that of the true distribution. See Section 5.3 for details. By definition, the concept of the FBP relies on replacement of the actual data by artificially created outliers.  
Before closing this section, we will briefly discuss the evaluation of robustness. The FBP seems to be the most common choice of robustness in the literature. In statistics, one compares FBPs to show their robustness when one proposes a new M-estimator. However, there were severe critiques to relying on it as a measure of robustness [4]. One of the major objections is that this quantity does not concern the actual estimate at all. In other words, a robust estimator in the sense of high FBP could be a very poor estimator. To see this point, we will evaluate robustness based on the FBP together with a newly proposed figure of merit, called an \(\varepsilon\)-curve. This is defied by the behavior of M-estimators as a function of the contamination parameter \(\varepsilon\). This \(\varepsilon\)-curve is conceptually simple, and it concerns the actual estimate. In this paper, we find it more natural to measure robustness based on the \(\varepsilon\)-curve. See Section 5.3 for a detailed comparison.
## 2 Rain Attenuation and Phase Delay Due to Rain in Zenith Propagation Paths
Because our purpose is to study transfer functions, we assume the channel is still working as required; in other words, outages are not considered as they are not directly related to transfer functions. An example illustrates how the SST transforms \(R(t)\) into \(A(t)\) and \(T(t)\). Figure 3 shows \(R(t)\) recorded in Tampa and Figures 4 and 5 show the corresponding \(A(t)\) and the time delay \(T(t)\) at 80 GHz and the relative values at the extremes of the 10 GHz bandwidth centered at 80 GHz. Notice that since \(A(t)\) and \(T(t)\) increase with frequency non-linearly in decibels and in picoseconds, the relative differences are not odd functions. From these time series, we can calculate the passband and equivalent baseband transfer functions, according to the theory discussed in [4]. Figure 1:1 SST simulated event at Tampa, 27 September 1997, started at 7:34:27 AM local time. (**Upper panel**): rain attenuation \(A(t)\) (dB) at 80 GHz (circular polarization). (**Lower panel**): relative attenuation at the extremes of a 10 GHz bandwidth. Sampling time is 1 min. Figure 1:2 Rain rate time series \(R(t)\) recorded at Tampa on 27 September 1997. According to the rain gauge collecting rainfall, the rain event started at 7:34:27 AM, local time. Samples are averaged in 1 min.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Healthcare  
Drones are being explored for transporting medical supplies, especially to remote or hard-to-reach areas. They can deliver medicines, vaccines, blood samples, and other medical supplies quickly and efficiently [27].
## 3 Principles of Operation
### Fuel Cells
#### 3.3.1 Open-Circuit Potential  
The open circuit potential (OCP) is the theoretical or equilibrium potential of an electrode in the absence of an external current flowing to or from the electrode, which can be calculated based on the knowledge of thermodynamics. The portion of the reaction enthalpy that can be converted into electricity in a fuel cell corresponds to the Gibbs free energy.  
\[\Delta G=\Delta H-T\cdot\Delta S \tag{11}\]  
In this equation, the Gibbs free energy \(\Delta G\) is not equal to the total reaction enthalpy \(\Delta H\) because there are some irreversible losses in energy conversion due to creation of entropy \(\Delta S\).  
In general, the electrical work is a product of charge and potential:  
\[W_{el}=q\cdot E \tag{12}\]  
where \(W_{el}\) is the electrical work, \(q\) is the charge and \(E\) is the potential.  
The total electrical charge transferred in a fuel cell reaction per mole of H\({}_{2}\) consumed is  
\[q=n\cdot N\cdot q_{d} \tag{13}\]  
where \(n\) is the number of electrons per molecule of hydrogen (\(n=2\)), \(N\) is the Avogadro's number (\(N=6.022\times 10^{23}\) molecules per mol), \(q_{el}\) is the charge per electron (\(q_{el}=1.602\times 10^{-19}\) C per electron). Because \(q_{el}N=9.65\times 10^{4}\) C mol\({}^{-1}\) is the Faraday's constant \(F\), the electrical work is therefore:  
\[W_{el}=n\cdot F\cdot E \tag{14}\]  
Therefore:  
\[W_{el}=\ -\Delta G \tag{15}\]  
The theoretical potential of a fuel cell is then given by  
\[E=-\frac{\Delta G}{n\cdot F} \tag{16}\]  
The temperature-dependent theoretical cell potential can be calculated by substituting Equation (11) into Equation (16).  
\[E=-\frac{\Delta H-T\cdot\Delta S}{n\cdot F} \tag{17}\]  
The theoretical cell potential also varies with reactant pressure. Assuming the reactants and product behave as an ideal gas, the change in the Gibbs free energy in an isothermal condition is expressed by  
\[dG=R\cdot T\cdot\frac{dp}{p} \tag{18}\]  
Carrying out an integration process:  
\[G=G^{0}+R\cdot T\cdot\ln\left(\frac{p}{p^{0}}\right) \tag{19}\]where \(G^{0}\) is the Gibbs free energy at the standard condition (25 \({}^{\circ}\)C and 1.0 atm), and \(p^{0}\) (1.0 atm) is the reference or standard pressure.  
This is known as the Nernst equation. Assuming the water is produced in the gas phase. The OCP can be calculated as  
\[E^{0}=-\frac{(\Delta H-T\cdot\Delta S)}{n\cdot F}+R\cdot T\cdot\ln\left(\frac {p_{H_{2}}p_{O_{2}}^{0.5}}{p_{H_{2}O}}\right) \tag{20}\]  
As shown in Figure 3, the OCP decreases with the temperature in a linear manner. In addition, the OCP for liquid water production is higher than that for water vapour.