esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 3 Hardware-Based Energy Sustainability in WSNs
### Hardware-Based Methods for Energy Sustainability
#### 3.2.1 Energy Saving Methods Applied in Submodules
Subsequently, they consume lower amounts of power comparatively to omnidirectional antennas that transmit towards many and probably undesired directions and consequently cause higher energy consumption [21]; Moreover, energy conservation depends on the way the nodes are deployed, the distance between them and the power needed for data transmission. In fact, in networks with dense deployment, nodes can communicate with nearby allocated nodes by using small communication links. This way, the transferred data reach their final destination by exploiting multi-hop paths, which results in the consumption of low power levels of each node. Contrariwise, in networks with sparse deployment in which single-hop communication applies, the transmission power and consequently the overall energy dissipation is greater [21]. * Regarding the power supply unit of sensor nodes, small batteries with restricted capacity [22] are typically used as power sources. The amount of the stored energy while a battery is fully charged is characterized as its capacity. There are different types of batteries used in WSNs, and some of the most commonly used are the Alkaline, the Lithium-Ion (Li-ion) and the Nickel Metal Hydride (NiMH) batteries. Of course, all types of batteries have an extremely limited lifetime. For this reason, the use of rechargeable batteries or supercapacitors is a better alternative. * In WSNs where the recharge of the batteries of the nodes is feasible, the usage of rechargeable batteries can considerably prolong the operational lifespan of the nodes and the overall network. Additionally, due to their high energy density, rechargeable batteries are suitable for WSNs utilizing energy harvesting implementations. Specifically, the density of NiMH batteries is 60-80 Wh/kg and that of lithium batteries is 120-140 Wh/kg, while their lifetime varies between 300-500 and 500-1000 recharge cycles, respectively [19]. In the cases
where battery recharge is difficult to perform, techniques that aim at either estimating [30] or prolonging [31] the remaining battery lifetime may be used; Supercapacitors are capacitors having higher capacitance with lower voltage limits when compared to typical capacitors. They have grown into practical alternatives of power sources in WSNs nodes due to their energy density levels that range between 1-10 \(\mathrm{Wh/kg}\), and their smaller size in comparison with batteries. Thus, an even long-lasting lifespan of the sensor nodes could be achieved by replacing the non-rechargeable batteries of sensor nodes used in harvesting systems with supercapacitors as means of energy storage [19].
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 3 Theoretical Background  
This chapter presents the theoretical background of all terminologies and an explanation of the proposed model. Deep learning techniques, such as RNN, RNN-LSTM, and RNN-GRU, are utilized to forecast future short-term electricity demand. Forecasting electrical load presents distinct challenges, including seasonality, periodicity, non-linearity, and sequential interdependence within consumption data sequence, which deep learning architectures are especially well-suited to address. By using non-linear modifications and extracting high-level abstractions, deep learning models, as opposed to shallow ANN architectures, can automatically learn complicated temporal patterns [43].
# A Robust Fixed-Time Sliding Mode Control for Quadrotor UAV
## 2 Quadrotor Aircraft Modelling  
The Quadrotor aircraft shown in Figure 1, is an unmanned aerial vehicle that has become increasingly popular in recent years due to its versatility and ability to perform a wide variety of tasks. This type of drone uses four electric motors to generate the power needed to stay in the air and control its movement in different directions.  
Each motor in the Quadrotor aircraft is responsible for generating a force known as Thrust, which allows the drone to stay in the air and move in different directions. The four motors work together to control the Yaw (vertical axis turn), Roll (horizontal axis turn), and Pitch (lateral axis turn) movements of the drone, allowing it to perform precise maneuvers and respond to instructions from the pilot or the automatic control system.  
The control of the four motors in a Quadrotor aircraft is essential to achieve balance and stability in the air since any change in the speed or force of one of the motors directly affects the drone's movement in all directions. Therefore, precision and careful control of all four motors are critical to achieving stable and safe flight.  
Currently, one of these types of aerial vehicles is the IMU, which uses GPS, lidar, etc.; this allows knowledge of the position and attitude. However, for UAV navigation to be accurate and efficient, it is necessary to have a geodetic frame of reference that allows the device to be in a three-dimensional coordinate system.  
The Quadrotor aircraft shows the simplified model of [32], which satisfies the following perturbed dynamic equations:  
\[\begin{split}\ddot{x}&=-u\sin\theta+\Gamma_{x}\\ \ddot{y}&=u\cos\theta\sin\phi+\Gamma_{y}\\ \ddot{z}&=u\cos\theta\cos\phi-mg+\Gamma_{z}\\ \ddot{\theta}&=\tau_{\theta}+\Gamma_{\theta}\\ \ddot{\phi}&=\tau_{\phi}+\Gamma_{\phi}\\ \ddot{\psi}&=\tau_{\psi}+\Gamma_{\psi}\end{split} \tag{1}\]  
where \(u\) is the total thrust, \(\tau_{\theta}\), \(\tau_{\phi}\) and \(\tau_{\psi}\) are the torques of rotational movements, \(m\) is the mass, \(I\) is the inertia moment, \(x\) is the horizontal displacement, \(y\) is the vertical displacement, \(z\) is the altitude displacement and \(g\) is the gravitational acceleration. \(\Gamma_{x}\), \(\Gamma_{y}\) and \(\Gamma_{z}\) represent the perturbations of the translational motions of the \(x\), \(y\) and \(z\) axes; now in \(\Gamma_{\theta}\), \(\Gamma_{\phi}\) and \(\Gamma_{\psi}\) represent the perturbations of the rotational motions of the angles \(\theta\), \(\phi\) and \(\psi\). All perturbations are external. In this model, we do not contemplate perturbations in the translational dynamics \((y-x)\) due to this being the main focus of analysis, since we propose to use visual information to obtain references \((x,y)\); for this, we will have a value of \(\Gamma_{z}\approx 0\), and later we will use GPS information to stabilize the \(z\)-axis.
## 2 Gravity Calculation Method  
The spherical harmonic gravity field is widely used with coefficients associated with Legendre functions of degree \(n\) and order \(m\)[18] and its application to asteroid [2; 19].  
\[U(r,\phi,\lambda)=\frac{GM}{r}\Bigg{[}1+\sum_{n=1}^{\infty}\Big{(}\frac{r_{0} }{r}\Big{)}^{n}\sum_{m=0}^{n}P_{nm}(\sin\phi)\times[C_{nm}\cos(m\lambda)+S_{ nm}\sin(m\lambda)]\Bigg{]}. \tag{1}\]  
where \(r\) is the radial distance from a given point to the centroid of the asteroid. \(\phi\) is its latitude, and \(\lambda\) is the longitude. \(G\) is the gravitational constant, \(M\) is the total mass of the asteroid, \(r_{0}\) is the reference radius, \(P_{nm}\) is the normalized associated Legendre function of degree \(n\) and order \(m\), and \(C_{nm}\) and \(S_{nm}\) are the normalized spherical harmonic coefficients:  
\[C_{nm} = \sqrt{\frac{2^{\delta}(l-m)!}{(2l+1)(l+m)!}}\frac{1}{Mr_{0}^{n}} \iiint_{\rm Vol}r^{m}P_{nm}(\sin(\phi^{\prime}))\cos(m\lambda^{\prime}){\rm d }M, \tag{2}\] \[S_{nm} = \sqrt{\frac{2^{\delta}(l-m)!}{(2l+1)(l+m)!}}\frac{1}{Mr_{0}^{n}} \iiint_{\rm Vol}r^{m}P_{nm}(\sin(\phi^{\prime}))\sin(m\lambda^{\prime}){\rm d }M. \tag{3}\]  
where \(r^{\prime},\phi^{\prime},\lambda^{\prime}\) are radius, latitude, longitude, respectively of a distributed mass inside an asteroid. And \(\delta=0\) if \(m=0\); \(\delta=1\) if \(m\
eq 0\). The \(r_{0}\) is the same reference radius in Equation (1). By comparison, the reference radius \(r_{0}\) can apparently be chosen arbitrarily, but it is normally chosen as the mean radius of the asteroid, i.e., the radius of a corresponding sphere with the same mass as the asteroid. The mean radius is an important parameter of the asteroid. Similar to the Earth, at positons where \(r=r_{0}\), it can be regard as the \"sea level\"; for \(r>r_{0}\), it is the \"land area\"; and for, \(r<r_{0}\), it is the \"ocean floor\". This classification is helpful to discuss the convergence and divergence regions of the harmonic expansion examples of an asteroid's gravity, which will be discussed comprehensively in the following sections.
## 1 Introduction
A methodology for discovering earthquake precursors by using clustering, grouping, constructing a precursor tree, pattern extraction, and pattern selection has been applied to seven different datasets from three different regions [29]. Results show a remarkable improvement in terms of all evaluated quality measures compared to the former version. The authors suggest that this approach could be further developed and applied to other regions with different geophysical properties to improve earthquake prediction. In [30], the authors use machine learning techniques to detect signals in a correlation time series corresponding to future large earthquakes. The overall quality is measured by decision thresholds and receiver operating characteristic (ROC) methods together with Shannon information entropy. They hope that the deep learning approach will be more general than previous methods and not require prior guesswork as to what patterns are important. The seismic activity parameters constructed based on seismic catalogs are typically used as the input data set for earthquake prediction. However, it is still debatable whether the information about small earthquakes (typically with a magnitude smaller than 4.0), such as foreshocks and aftershocks, contained in these seismic features can predict large earthquakes (typically with a magnitude larger than 6.0). In fact, the ability to successfully predict earthquakes has been the subject of controversy [1; 3] among researchers. Some studies have shown that information about small earthquakes can indicate the occurrence of large earthquakes, while others have drawn opposite conclusions [31; 32]. To clarify this debate, a study was conducted by using seismic features based on seismic catalogs containing small earthquakes to test whether this information can help predict large earthquakes. Additionally, the possibility of using machine learning in earthquake prediction was explored. The study focused on two important questions:  
1. Will there be a strong earthquake (M \(\geq\) 6.0, 7.0, or 8.0) in the next year? 2. What will be the maximum magnitude of the earthquake in the next year? The Sichuan-Yunnan region, as shown in Figure 1,was chosen as the research area and seismic features were extracted from the seismic catalog to predict earthquakes. The traditional machine learning methods were used to classify whether there would be strong earthquakes in the next year, and the LSTM network was used to estimate the maximum magnitude of the earthquake in the next year. In this way, we explored the potential applications of machine learning methods in earthquake prediction and provided a possible solution for seismic hazard evaluation.
## 5 Numerical Simulations
### Simulation Results
#### 5.2.3 Independence and Robustness  
We then investigated the relationship between the interdependence and the ADN robustness under the ER/BA topological structure, which is shown in Figure 8, where the proportion of distributed generators was 0.2 and the value of the tolerance parameters of the nodes was 1.5. One-to-one interdependence represented \(<\)K\({}_{2}\)\(>\) = 1, and \(<\)O\({}_{1}\)\(>\) = 1; three-to-three Interdependence represented \(<\)K\({}_{2}\)\(>\) = 3, and \(<\)O\({}_{1}\)\(>\) = 3.  
Of the nodes in the PN, 26.01% survived for \(\Theta_{P}\) = 0.022 with one-to-one interdependence under the ER topological structure and 62.49% of nodes in the PN survived for \(\Theta_{P}\) = 0.022 with three-to-three interdependence under the ER topological structure. Of the nodes in the CN, 26.01% survived for \(\Theta_{P}\) = 0.022 with one-to-one interdependence under the ER topological structure and 64.13% of nodes in the CN survived for \(\Theta_{P}\) = 0.022 with three-to-three interdependence under the ER topological structure. Accordingly, 21.02% of nodes in the PN survived for \(\Theta_{P}\) = 0.004 with one-to-one interdependence under the BA topological structure. Of the nodes in the CN, 21.02% survived for \(\Theta_{P}\) = 0.004 with one-to-one interdependence under the BA topological structure and 25.6% of nodes in the CN survived for \(\Theta_{P}\) = 0.004 with three-to-three interdependence under the BA topological structure.  
As shown in Figure 8, we observed that \(R_{IF}(\infty)\) and \(R_{PF}(\infty)\) of three-to-three interdependence were greater than that of one-to-one interdependence both in the ER topology and in the BA topology structure. This agreed with our third conclusion. That is, the robustness of the ADN was improved  
Figure 8: \(R_{PF}(\infty)\)/\(R_{IF}(\infty)\) of different independence between the PN and CN.  
when the value of average control fan in \(<\)\(K_{2}\)\(>\) of a node in the PN and the value of average power fan in \(<\)\(O_{1}\)\(>\) of a node in the CN were greater.
## 4 Dynamic Response of Unsupported Track  
Numerical modeling described in Section 2 is applied to investigate the dynamic response of the unsupported track. Referring to the architectural drawings of a high-speed trackway bridge that is a simply supported box girder in China, the structural parameters of the bridge are as follows: span \(L_{b}\) = 32 m; Young's modulus \(E_{b}\) = 34.5 GPa; Poisson ratio 0.2; vertical moment of inertia \(I_{by}\) = 11.1 m\({}^{4}\); lateral moment of inertia \(I_{bz}\) = 93.8 m\({}^{4}\); twisting moment of inertia \(I_{bx}\) = 22.7 m\({}^{4}\); mass per unit length \(m_{b}\) = 43,630 kg/m; Rayleigh damping ratio \(\zeta\) = 0.02; cross-sectional area \(A_{b}\) = 8.98 m\({}^{4}\); vertical distance between the bridge deck and the torsional center of girder cross section \(h_{b}\) = 1.05 m. The fundamental  
Figure 4: Comparative results of the displacement for the track: (**a**) vertical direction and (**b**) lateral direction.  
frequency of the bridge is 4.6 Hz, and the theoretical resonance speed is 108 m/s. A train consisting of 5 vehicles moves over the bridge at a speed of 100 m/s, which is close to the resonance speed.  
The track is comprised of spatial beam elements, wheel-rail connection elements, and bridge-rail connection elements. The detailed properties of the track materials are as follows: element length \(L_{r}\) = 1 m; [PERSON]'s modulus \(E_{r}\) = 206 GPa; Poisson ratio 0.2; vertical moment of inertia \(I_{by}\) = 5.24 \(\times\) 10\({}^{-6}\) m\({}^{4}\); lateral moment of inertia \(I_{bx}\) = 3.217 \(\times\) 10\({}^{-6}\) m\({}^{4}\); mass per unit length \(m_{r}\) = 60.64 kg/m; eccentric distance of the left-side and right-side rail \(f\) = 0.757 m; vertical connected stiffness coefficient between rail and bridge \(k_{rb}^{v}\) = 6 \(\times\) 10\({}^{7}\) N/m; vertical connected damping coefficient between rail and bridge \(c_{rb}^{v}\) = 7.5 \(\times\) 10\({}^{4}\) N\(\cdot\)s/m; lateral connected stiffness coefficient between rail and bridge \(k_{rb}^{w}\) = 1.5 \(\times\) 10\({}^{7}\) N/m; lateral connected damping coefficient between rail and bridge \(c_{rb}^{w}\) = 6 \(\times\) 10\({}^{4}\) N\(\cdot\)s/m. The length of the subgrade in the transition section at both ends of the bridge is taken as 12 m, and the total length of the track is 56 m.  
The parameters of the vehicle with 27 DOFs are the same as those in Ref. [30]. A Hertz spring is used to simulate wheel-rail contact, and both vertical stiffness coefficient \(k_{wr}^{v}\) and lateral stiffness coefficient \(k_{wr}^{w}\) are 1.4 \(\times\) 10\({}^{8}\) N/m.
## 3 Experiments and Analysis of Results
### Analysis of Prediction Results
#### 3.2.3 Regression Analysis of Prediction Results  
The relationship between _PMV_ and the thermal dissatisfaction rate prediction result is shown in Figure 9. The _PMV-PPD_ thermal comfort prediction model predicted the lowest value of thermal dissatisfaction rate to be 5% and the comfort interval to be (\(-\)0.5,0.5) at a thermal dissatisfaction rate of 10%. The lowest value of the thermal dissatisfaction rate predicted based on Bayesian theory is 16.28%, corresponding to a _PMV_ value of 0.25.
## 2 Materials and Methods
### Brain Machine Interface
#### 2.4.3 Feature Extraction  
The following step of the BMI has the objective of computing the characteristics of the EEG during each brain task that could be discriminating.  
For the MI paradigm, common spatial patterns (CSP) [22] are computed for each frequency band. CSP estimate a spatial transformation that maximizes the discriminability between two brain patterns. If \(X\) is the EEG that has \(N*T\) dimensions, which are the number of channels and number of samples, respectively, the CSP algorithm estimates a matrix of spatial filters \(W\) that discriminates between two classes: (\(X_{1}\)) and (\(X_{2}\)). Firstly, the normalized covariance matrices are computed for each class as in  
\[C_{1}=\frac{X_{1}X_{1}^{T}}{trace(X_{1}X_{1}^{T})},C_{2}=\frac{X_{2}X_{2}^{T}}{ trace(X_{2}X_{2}^{T})}. \tag{2}\]  
These matrices are computed for each trial and \(\overline{C_{1}}\) and \(\overline{C_{2}}\) are calculated by averaging over all trials of the same class. The averaged covariance matrices are combined to result in the composite spatial covariance matrix that can be factorized as  
\[C=\overline{C_{1}}+\overline{C_{2}}=U_{0}\Sigma U_{0}^{T}. \tag{3}\]  
\(U_{0}\) is a matrix of eigenvectors and \(\Sigma\) is the diagonal matrix of eigenvalues. The averaged covariance matrices are transformed as  
\[P=\Sigma^{1/2}U_{0}^{T}, \tag{4}\]  
\[S_{1}=P\overline{C_{1}}P^{T},S_{2}=P\overline{C_{2}}P^{T}. \tag{5}\]  
\(S_{1}\) and \(S_{2}\) have common eigenvectors, and the sum of both matrices of eigenvalues is the identity matrix.  
\[S_{1}=U\Sigma_{1}U^{T},S_{1}=U\Sigma_{2}U^{T}and\Sigma_{1}+\Sigma_{2}=I \tag{6}\]  
The projection matrix is obtained as  
\[W=U^{T}P. \tag{7}\]  
\(Z\) is the projection of the original EEG signal \(S\) into another space. Columns of \(W^{-1}\) are the spatial patterns.  
\[Z=WX \tag{8}\]  
Although \(Z\) has \(N*T\) dimensions, the first and last rows are the components that can be better discriminated in terms of their variance. Therefore, for feature extraction, only the \(m\) first and last components of \(Z\) are considered. \(Z_{p}\) is the subset of \(Z\) and the variances of each component are computed and normalized with the logarithm as  
\[f_{p}=log\frac{var(Z_{p})}{\sum_{i=1}^{2m}Z_{p}}. \tag{9}\]  
\(f_{p}\) is the vector of features and has \((fbands*2*m)*T\) dimension. \(m\) was set to 4, and in the pre-processing phase, 4 band-pass filters were employed so the dimension is \(32*T\).  
For the attention paradigm, power spectral estimation by Maximum Entropy Method (MEM) was used to obtain features associated with each task. The signal of each electrode was estimated as an autoregressive model in which the known autocorrelation coefficients were calculated and the unknown coefficients were estimated by maximizing the spectral entropy [23]. Afterwards, the autocorrelation cofficients were used to compute the power spectrum that was compatible with the fragment of the signal analyzed, but it was also evasive regarding unseen data. Afterwards, only the power of the frequencies in the gamma band was considered [15].
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 4 Towards Software-Defined Heterogeneous Vehicular Networks
### Architectural Challenges
#### 4.2.3 Broadcast Storm Mitigation and Network Slicing  
The sheer increase in the availability of onboard sensors and next-generation communication platforms facilitate vehicles in disseminating safety-critical messages to other vehicles and vulnerable road pedestrians in their immediate vicinity. Such safety messages are extremely critical in nature and typically include, but are not limited to, emergency vehicle warnings, forward collision warnings, blind intersection warnings, vulnerable pedestrian warnings, blind curve warnings and queue warnings. However, disseminating of these packets in a concurrent manner or to vehicles not requiring the same results in packet collision, choking of the entire network and hence the packet delivery timelines could be considerably delayed, which within the context of vehicular networks, may lead to a number of grave consequences. This phenomenon is referred to as broadcast storming where a sheer amount of traffic is broadcast, in turn consuming the precious network resources and leaving behind fewer resources to transport normal traffic. Broadcast storm could also be triggered by a malicious entity, and the security of the network is also crucial in order to ensure that such entities are not allowed to penetrate the network in the first instance, but if they do so, appropriate recovery mechanisms should be in place to track and subsequently eradicate them from the network.  
However, a broadcast storm could be resolved by appropriately slicing the network. Network slicing is a technique whereby multiple tenants are isolated into distinct groups to improve the network efficiency significantly. This is illustrated via a general scenario depicted in Figure 6. The police vehicle \(P\) and ambulance \(A\) can broadcast messages to vehicles on both sides of the road. However, it is unnecessary for the vehicle \(P\) to broadcast messages to vehicles \(V_{3}\), \(V_{4}\) and \(A\), and the same is true for vehicle \(A\), which does not need to broadcast messages to vehicles \(P\), \(V_{1}\) and \(V_{2}\). The localized SDN controllers could therefore slice the network with respect to the driving directions of vehicles in the network. Thus, the vehicles would only broadcast packets to other vehicles travelling in their respective direction or their anticipated travelling trajectory. SDN-based network slicing is perhaps easy to configure, primarily owing to the software nature of the SDN controllers, is extremely beneficial within a dynamic vehicular context and ensures ultra-reliable and low-latency communication.