esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Throughout this work, the use of a new technology is proposed to give an innovative solution to the problem of indoor and outdoor positioning and a guidance system in areas where there is no coverage of Internet or global positioning systems. This novel solution is based on the Bluetooth protocol called FatBeacon, created by Google, which can be used in places such as subway stations located below ground, outdoor areas where even 2G coverage is unreachable or simply as an alternative to current technologies that may require an additional cost, such as the Internet in some countries. In particular, this work describes a new solution for supporting tourism called Smart Tourism for which an interactive and non-intrusive guidance application based on the FatBeacon protocol is presented. The developed application informs the users about the way to reach their destination without getting lost and can be used to obtain user data tracking anonymously. In this way, the cooperation between the different systems and components of the scheme creates a distributed ecosystem that is independent of an Internet connection. Since no FatBeacon implementations can be found yet, an experimental implementation was developed to test the proposal, and the obtained results are promising.  
Keywords: indoor positioning; Bluetooth Low Energy; user tracking; guidance; beacon; outdoor positioning; cyber physical systems +
Footnote †: journal: Applied Sciences
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 2 PRACH Architecture and Random Access Procedure  
The Physical Random Access Channel (PRACH) allows a UE to establish a connection with the base station (eNodeB) by sending a request to which the eNodeB will respond by scheduling appropriate resources for communications to and/or from the UE. Available bandwidth resources for LTE cell are divided in a time and frequency domain matrix. Time access is organized in frames that last 10 ms and that consist of 10 subframes with a duration of 1-ms each. Subframes can be divided into two 0.5-ms slots. In the frequency domain, resources are grouped in units of 12 OFDM subcarriers  
Figure 1: M2M communications for smart city scenarios through LTE.  
with a total bandwidth of 180 kHz. The basic access unit for either random or scheduled access is a Resource Block (RB), which consists of 12 subcarriers over one subframe. Cell bandwidth can be configured in frequency- or time-division duplex (i.e., FDD or TDD operation mode.  
PRACH is carved out of the time and frequency domain matrix by dedicating a number of resource blocks in a number of consecutive LTE frames. The basic PRACH resource is composed of six resource blocks in frequency with a bandwidth of 1.080 MHz, for the duration of one subframe; higher traffic volume can be accommodated by allocating more resources, for a total of 16 configurations shown in Figure 2. For low traffic intensity and small system bandwidth, one PRACH resource per two frames may be sufficient (TDMA Configurations 0, 1, 2 and 15). As traffic increases, PRACH resources may be configured to occur once per frame (TDMA Configurations 3, 4 and 5), twice per frame (TDMA Configurations 6, 7 and 8) or even once every three subframes (TDMA Configurations 9, 10 and 11). These configurations avoid interference at a granularity of three neighbouring cells. However, higher traffic may require even more dense PRACH allocations, which brings the possibility of interference since the PRACH resource occurs on every second subframe (Configurations 12 and 13) or on every subframe in a frame (Configuration 14). In the discussions that follow, we will denote the number of PRACH subframes within the frame as configuration index \(c_{f}\), where \(c_{f}=k\) means that there are \(k\) PRACH subframes in the frame.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 5 Simulation Results
### Nlt  
3D-SM balances the load of nodes and results in longer NLT. Exponential node depletion shows that energy consumption of the nodes is balanced, and after the death of the first node, nodes start depleting their energy. This scheme avoids multi-hop transmissions in order to save the energy of nodes. The MS and CNs receive data from the nodes, and then, the CN further transmits the received data to the MS. In terms of NLT, 3D-SM > DDRP > AUV-PN. This analysis shows that AUV-PN consumes more energy in the formation of clusters and in the selection of CHs, which results in a shorter NLT.  
However, DDRP and AUV-PN both have imbalanced energy consumption, as depicted in Figure 8. Nodes that are selected as CHs have a shorter lifetime because these relay the data of the MN. The selection criteria of the CH is based on the low-energy adaptive clustering hierarchy (LEACH) protocol, which depends on the probability. This is the major flaw of the LEACH protocol; there is no check on the residual energy. In the stable election protocol (SEP) [22], the authors consider a two-level heterogeneous system, where nodes have two different levels of energy: normal and advanced. Advanced nodes are 10% of the total number of nodes in the network, and they have double the initial energy as compared to the normal nodes. In the distributed energy-efficient  
\begin{table}
\begin{tabular}{c c} \hline
**Operations** & **Values** \\ \hline Initial energy of node & 70 J \\ Transmitter electronics & 6.3 mJ/bit \\ Data rate & 1 Kbps \\ Transmission range & 70 m \\ Network depth & 500 m \\ Network breadth & 1000 m \\ Network width & 500 m \\ Total number of nodes & 300 \\ AUV & 1 \\ CNs & 3 \\ \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
clustering algorithm (DEEC) [23], the authors consider a multi-level heterogeneous network with normal, advanced, super and ultra-super nodes. In both schemes, CHs are selected on the basis of the maximum residual energy; while in DDRP, there is no set pattern of the MS trajectory such that it will cover the whole network. Its mobility is random, and it may visit the same region twice in its data collection tour; this leads to imbalanced transmissions, as shown in Figure 8.
## 2 Materials and Methods
### Trend Analysis and Partial Correlation between EVI and Climate  
We calculated annual and maximum EVI (EVI\({}_{a}\) and EVI\({}_{m}\)) for each patch during May-September for each year as this period is considered as typical growing season in Northern China. Then, we derived the temporal trends in EVI\({}_{a}\) and EVI\({}_{m}\) based on the time series regression with the first order of autocorrelation using R nlme package [39]. Trend of the greenness is quantified with the regression slope describing the rate of change. Wet-year climate was also extracted for each patch and precipitation, \(P\), annual mean maximum and minimum temperature, \(T_{\text{max}}\) and \(\underline{T}_{\text{min}}\), were computed for each wet-year, each growing season (May-September), and each nongrowing season (prior October-April). The temporal trends of these climate variables were then derived using the time series analysis abovementioned.  
To explore the EVI responses to climate, we calculated the partial correlations between the EVI and the climate variables (annual, growing-season, and nongrowing-season precipitation, maximum and minimum temperatures) using the R ppcor package [40]. The advantage to use partial correlation is to eliminate the effects of other covariables when partial correlation coefficient is computed between two variables. Specifically, when calculating the partial correlations of EVI with annual precipitation, we set the annual minimum and maximum temperatures as covariables. When calculating the partial correlations between EVI and growing-season precipitation, we set the growing-season \(T_{\text{min}}\) and \(T_{\text{max}}\) and the nongrowing-season \(P\), \(T_{\text{min}}\), and \(T_{\text{max}}\), as covariables. The covariables for the partial correlation of EVI with nongrowing-season precipitation are the nongrowing-season \(T_{\text{min}}\) and \(T_{\text{max}}\) and the growing-season \(P\), \(T_{\text{min}}\), and \(T_{\text{max}}\). Similar settings are applied to the partial correlations with \(T_{\text{min}}\) and \(T_{\text{max}}\).
###### Abstract  
Modified blind bolts (Hollo-Bolt) and a locally strengthened steel tube column in the panel zone were created to overcome the moment-resisting problem for the bolted connections between concrete-filled hollow section columns and open section beams and to enhance the performance of connections. The cyclic loading was conducted on a total of six modified anchored blind bolted flush end-plate connections to concrete-filled steel tube (CFST) columns. The key parameters investigated were the tube wall thickness, end-plate thickness, blind bolt anchorage method, and beam section. The failure mode, hysteretic behavior, strength, stiffness, ductility, and energy dissipation capacity of the connections were analyzed and evaluated with all details. The results indicated that connections with modified anchored blind bolts and locally strengthened steel tubes could avoid the premature failure of CFST column and exhibit an improved behavior with a favorable strength, stiffness, and stiffness degradation. The test observations reveal two representative failure modes, and the tube wall thickness and blind bolt anchorage method have a significant effect on the resultant failure mode. Moreover, the use of thin endplate and weak beam can effectively enhance the hysteretic behavior of joints, ductility, and energy dissipation capacity; and the change in anchoring method has little effect on the stiffness. Finite element (FE) analysis models were established for the aforementioned connections. The numerical models were validated against the experimental results and exhibited good agreement. Finally, based on the component method, an initial stiffness calculation method was established for the connections.  
flush end-plate connections; blind bolts; seismic behavior; concrete-filled steel tube (CFST); finite element (FE) modeling; initial stiffness calculation +
Footnote †: journal: Applied Sciences
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 3 Multi-Objective Optimization Deployment Model
### Determining the Multi-Objective Deployment Model
#### 3.2.4 Description of Multi-Objective Problem  
The ultimate optimization goal of this paper is to ensure coverage that meets the requirements of the monitoring area while using as few sensor nodes as possible and extending the overall network lifespan. The lower the energy consumption, the longer the network's lifespan. The multi-objective sensor network deployment problem described in this paper can be characterized as  
\[\left\{\begin{array}{rl}&f_{1}(O)=Max(Cov(O))\\ &f_{2}(O)=Min(U_{\eta}(O))\\ &f_{1}(O)=Min(E_{a}(O))\\ \text{s.t}&s=\{s_{1},s_{2},\cdots,s_{l},\cdots s_{N}\}\in H\end{array}\right. \tag{9}\]  
where the constraint \(A(s_{i})\in H\) that each node's position (\(s_{i}\)) must be within the monitoring area (\(H\)). Here, \(O\) represents a matrix containing the coordinates, remaining energy, sensing radius, and communication radius of a set of sensor nodes.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 3 System Modeling and Problem Statement
### Motion Equations of the Multirotor UAVs  
The study of UAV motion has two main concepts: rotational and translational motions. According to the aforementioned references and to [3], the six degrees of freedom model of the system (position and attitude) can be separated into translational and rotational motions, represented, respectively, by \(\Sigma_{T}\) and \(\Sigma_{R}\) in Equations (7) and (8):  
\[\Sigma_{T} : \begin{cases}\dot{\mathbf{p}}=\mathbf{v}\\ \dot{\mathbf{v}}=g_{3}^{\mathbf{e}}\dot{f}_{-\frac{1}{m}}\mathbf{R}_{b}^{f} \mathbf{e}_{3}^{b}\mathbf{T}+\mathbf{F}_{d}\end{cases} \tag{7}\] \[\Sigma_{R} : \begin{cases}\dot{\mathbf{q}}=\frac{1}{2}\Xi(\mathbf{q})\mathbf{\omega} _{i}\\ \mathbf{J}\dot{\mathbf{\omega}}=-[\mathbf{\omega}^{\times}]\mathbf{J}\mathbf{\omega}+\mathbf{ \Gamma}+\mathbf{\Gamma}_{d}\end{cases} \tag{8}\]  
where \(m\) is the mass of the VTOL-UAV and \(\mathbf{J}\) is the inertial matrix expressed in \(\mathbf{E}^{b}\). The gravity acceleration is \(g\) and \(\mathbf{e}_{3}^{f}=\mathbf{e}_{3}^{b}=(0\:0\:0\:1)^{T}\). \(\mathbf{p}\in\mathbb{R}^{3}\) is the position of the center of gravity of the aircraft, which point coincides with the origin of frame \(\mathbf{E}^{b}\) with respect to frame \(\mathbf{E}^{f}\), \(\mathbf{v}\in\mathbb{R}^{3}\) is its linear velocity in \(\mathbf{E}^{f}\), and \(\mathbf{\omega}\in\mathbb{R}^{3}\) denotes the angular velocity vector of the body coordinate frame \(\mathbf{E}^{b}\) relative to the inertial coordinate frame \(\mathbf{E}^{f}\), expressed in \(\mathbf{E}^{b}\). \(\mathbf{e}_{3}^{b}\mathbf{T}\) is the total thrust, expressed in \(\mathbf{E}^{b}\). \(\mathbf{\Gamma}\in\mathbb{R}^{3}\) depends on the couples generated by the actuators and as a consequence, it represents the control signal. Furthermore, several disturbance forces and torques acting on the aircraft are being considered, which are grouped together in vectors \(\mathbf{F}_{d}\) and \(\mathbf{\Gamma}_{d}\), respectively. Note that the models (7) and (8) give rise to two cascaded subsystems: rotational and translational ones. The longitudinal and lateral movements cannot be performed without a coupling to the rotational degrees of freedom. Therefore, efficient attitude control is crucial to maintaining the desired attitude in order to reach a desired position despite external disturbances.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 4 Electricity Price Probabilistic Forecasting  
In electricity markets, electricity prices are affected by the total system's supply and demand. However, each participant playing in these markets needs to schedule their resources according to the predicted values of the market prices, in short-term horizons. Regarding future power systems, renewable-based generation will be the main source of electricity. Their intermittent nature and real-time fluctuations increase the electricity price fluctuations [68]. Accordingly, electricity price forecasting models need to be improved to actively follow the prices' fluctuations. In addition, it can be argued that probabilistic forecasting models are attracting increasing attention since they are able to consider various uncertainties and possibilities [10].  
Regarding studies that proposed parametric models, the authors of [69] proposed a first-order vector autoregressive (VAR) model considering exogenous effects and using skew t distribution in a Bayesian framework. The model was then sent to the Markov chain Monte Carlo for uncertainty analysis. Reference [70] adopted the GAMLSS method and also estimated the PIs to be time-varying quantiles of the acquired density forecasts. In [71], the authors developed generalized autoregressive conditional heteroskedasticity (GARCH)-based time-varying models to estimate the density function of the variable. As a semi-parametric model, ref. [72] introduced a semiparametric model that is combined with a time-adaptive quantile regression [34] in order to predict day-ahead market price densities. The proposed model was then compared to four well-known GARCH models considering a three-year time span. They finally proved that their proposed model is more reliable in terms of generating quantile estimates.  
Recently, non-parametric data-driven probabilistic forecasting models are more often employed due to their flexibility. For instance, ref. [73] introduced a deep neural network model-based method for the probabilistic prediction of electricity prices. In this model, they first made the price distributions using its historical data. Afterward, they employed a deep convolutional neural network (DCNN) for extracting high-level features. The obtained high-level features were sent to label distribution learning forests (LDLF) in order to construct price probabilistic forecasts. In another work, ref. [74] developed a two-step model to probabilistically forecast German-Austrian day-ahead prices. It first proposed estimating the mean of correlated time series prices using ordinary least squares and the elastic net method. In the second step, they estimated the residuals using the maximum likelihood method.
## 2 Experimental Section
F tends to pile up at the HfO\({}_{2}\)/In\({}_{0.53}\)Ga\({}_{0.47}\)As interface passivating interface traps, resulting in a better interface quality (discussed later in this paper). The composition and stoichiometry of HfO\({}_{2}\) were determined by XPS analysis (data not shown). For the control sample, HfO\({}_{2}\) was in good stoichiometry (Hf:O = 1:2) and it became oxygen-rich (HfO\({}_{2.25}\)) after CF\({}_{4}\)/O\({}_{2}\) plasma treatment. One possibility of improvements could be due to the oxygen vacancies passivation during the plasma treatment. The effect of O\({}_{2}\) plasma treatment is under investigation. One concern of doping F into gate oxide is that dielectric constant of the gate oxide would decrease with heavy F incorporation. From XPS, the F concentration in our fluorinated HfO\({}_{2}\) is estimated to be 2.7 at.%, which is lower than that used in the low-k technology [12]. Therefore, the dielectric constant of HfO\({}_{2}\) remains similar (\(\sim\)17) after F incorporation, as shown in Figure 5. The inset of Figure 5 compares the gate leakage current of samples with and without F incorporation. The gate leakage current is slightly reduced with F incorporation. Figure 4: Secondary ion mass spectrometry (SIMS) profile of elements in the HfO\({}_{2}\)/In\({}_{0.53}\)Ga\({}_{0.47}\)As gate stack with and without CF\({}_{4}\)/O\({}_{2}\) plasma treatment. The CF\({}_{4}\)/O\({}_{2}\) plasma condition is 30 W for 3 min. One objective of this work is to investigate the optimum condition of CF\({}_{4}\)/O\({}_{2}\) plasma. The RF power and the plasma treatment time are two critical factors that affect the electrical characteristics significantly. Insufficient plasma treatment might not improve the gate dielectric quality, whereas excessive plasma treatment possibly causes plasma damage and corrodes the improvement. To study the effects of RF power on HfO\({}_{2}\) gate dielectrics, some samples were treated in CF\({}_{4}\)/O\({}_{2}\) plasma for 3 min at different RF power in the range from 20 to 40 W. It was found that the samples treated by the power of 30 W improved most in terms of G\({}_{\rm m}\), I\({}_{\rm d}\) and SS. With the fixed RF power of 30 W, we continued to study the effect of CF\({}_{4}\)/O\({}_{2}\) plasma treatment time. Some samples were treated in CF\({}_{4}\)/O\({}_{2}\) plasma at different treatment times ranging from 1 to 7 min with a fixed RF power of 30 W. It was found that 5 min plasma treatment further improved SS.
# Stability Analysis of GNSS Stations Affected by Samos Earthquake
## 3 Results and Discussion
It should be noted that these approaches do not provide displacement magnitudes, but rather determine significant displacements in GNSS stations. The S-transformation method demonstrates effectiveness in detecting unstable reference points and GNSS stations significantly affected by the earthquake, making it suitable for deformation analysis. Figure 4: Map of GNSS coseismic displacements. Red arrows are the (**a**) horizontal and (**b**) vertical displacements with 95 percent confidence ellipses relative to Eurasia, and black lines are active faults [34]. The coseismic slip distribution model (**a**) is taken from Chousianitis and Konca [21].
## 4 LiCSAR Applications for Measuring Tectonic and Volcanic Deformations
### LiCSAR for Tectonic Applications
#### 4.1.1 Coseismic Interferometric Products  
As an example, Figure 9 shows three coseismic interferograms from continental earthquakes in different tectonic settings generated in both ascending (left) and descending (right) orbits. They correspond to the 4 July 2019, \(M_{w}\) 7.1 right-lateral strike-slip earthquake in Ridgecrest, California (Figure 9a), the 12 November 2017, \(M_{\mathrm{w}}\) 7.3 dip-slip earthquake at the Iran-Iraq border (Figure 9b), and 16 September 2015, \(M_{\mathrm{w}}\) 8.3 dip-slip earthquake in Illapel, Chile (Figure 9c). Each colour cycle can be interpreted as representing 2.8 cm of relative ground displacement in the LOS direction (i.e., towards or away from the satellite). Because the LiCSAR metadata folder includes grids of the unit vector in the satellite LOS at each pixel, the data can easily be used for earthquake source modelling, for example using tools such as Pyrocko [65], which automatically can ingest LiCSAR products.  
As an example of other generated InSAR products, Figure 10a,b show the unwrapped phase and coherence images corresponding to the Ridgecrest interferogram in Figure 9b, respectively. It should be noted that the phases in Figure 10a are rewrapped to \(6\pi\) and therefore the colour cycle here is equal to a \(6\pi\) phase variation (8.3 cm if it is caused by movement in the LOS). The white areas in the unwrapped image show the areas that were masked based on a coherence threshold to avoid unwrapping errors. These low coherence regions (visible as dark spots in Figure 10b) can be due to large changes in the ground surface due to surface rupture, ground shaking and high displacement gradients. Figure 10c is the intensity image of the same frame corresponding to the post-seismic Sentinel-1 image, acquired on 21 August 2019.  
Figure 9: Coseismic interferograms generated in both ascending (**left**) and descending (**right**) orbital tracks of Sentinel-1 by the LiCSAR processor for: \(M_{w}\) 7.1 right-lateral, strike-slip earthquake in Ridgecrest, California (4 July 2019) from (**a**) frame 064A_05410_131313 and (**b**) frame 071D_05377_131313; \(M_{w}\) 7.3 dip-slip earthquake at the Iran–Iraq border (12 November 2017) from (**c**) frame 072A_05489_131313 and \(M_{w}\) 8.3 dip-slip earthquake in Illapel, Chile (16 September 2015) from (**e**) frame 018A_12301_061311 and (**f**) frame 156D_12184_101305. Note that the deformation from the Illapel earthquake covers too large an area to be completely captured in a single Sentinel-1 frame.