esa-sceva/satcom-chunk-collection huggingface dataset:
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 3 The Hybrid EMD-SARIMA Forecasting Framework  
A hybrid method of EMD-SARIMA for air traffic forecasting is proposed here. As discussed in Section 1, in this methodology, the EMD method is used first to decompose the original air cargo and passenger series into independent components called IMFs. The main purpose of decomposition is to differentiate the modes based on different characters and improve the prediction accuracy. Utilizing the EMD method, the original air cargo and passenger series that is nonlinear and non-stationary are decomposed into a finite number of IMFs. After the decomposition step, each IMF is modeled by a SARIMA model and can be forecasted more accurately. Finally, the predictions from the SARIMA model are aggregated togenerate a forecasting result from the hybrid EMD-SARIMA model. A detailed procedure of the EMD-SARIMA modeling framework is shown in Figure 2.  
Figure 2: The hybrid EMD-SARIMA modeling framework. IMF: intrinsic mode function; SARIMA: seasonal autoregressive integrated moving average.
## 4 Simulations and Results
### Simulation Settings and Results Format
Commonly used settings are adopted for MAC and PHY in the two technologies. The MCS in NR-V2X is chosen as the lowest possible (most reliable) for the given channel. Furthermore, blind retransmissions are not enabled in NR-V2X since this would strongly and unfairly affect the impact of coexistence and methods; investigating the impact when blind retransmissions are enabled is left for future work. When used, the superframe duration is set to 25 ms, which is the middle value between those indicated in [4] (10, 25, or 50 ms). When a static configuration is assumed, the slot time is fixed to \(T_{\text{NR}}=13\) ms and \(T_{\text{11p/bd}}=12\) ms, regardless of the balanced or unbalanced vehicle distributions; in real scenarios, in fact, the technology distribution would likely change in time and space and such variability could not be caught in the static configuration. It is therefore relevant to take such inaccuracy into account. Since the investigated mechanisms impact the channel occupation measurement (as shown in [12]) and therefore alter the way congestion control mechanisms work in either technology, congestion control is not used in this paper; the impact of congestion control and its improvement in the coexistence scenarios is left for future work. Three metrics are used to compare the performance of each mitigation method:  
1. _Transmission range (TR)_: The maximum distance to have an average \(PRR\) higher than 0.9, where the PRR at a given distance (with granularity 50 m) is defined as the ratio between the number of vehicles receiving the packet correctly at the given distance from the receiver and the total number of target vehicles at the same distance;
2. _End-to-end delay (EED)_: The time interval between the generation time of a packet at the transmitter and its reception time at the receiver. As shown in Figure 9a, only correct reception contributes to this metric;
3. _Data age (DA)_: The time interval between the generation of a packet that is correctly received and the time when the next correct reception occurs (from the same source to the same receiver); this time is also equal to the time between two consecutive received packets generated from the same transmitter plus the EED of the first packet. As shown in Figure 9b, errors increase the DA, which therefore takes into account the correlation between errors. In all the plots, the gray lines provide benchmarks where no methods are used. In particular, the dotted gray lines correspond to results with all vehicles equipped with the same technology, called the _Only-1-tech_ scenario in the following. The dashed gray lines correspond to the _No-method_ scenario, where the two technologies coexist with no mitigation methods. The colored lines correspond to the performance when either of the mitigation methods listed in Table 1 are used (the corresponding colors are used to frame Figures 2-6).
## 4 Results of Experiments
### Implementation Details  
We implemented our model in Python 3.9.13, using TensorFlow version 2.17.0. The dataset was divided into 70% training, 15% validation, and 15% test sets, as shown in Table 1. The hyperparameters for each dataset in our models are detailed in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Sentiment** & **Training 70\%** & **Validation 15\%** & **Testing 15\%** & **Total** \\ \hline \multirow{3}{*}{ACRD} & Positive & 11,970 & 2565 & 2565 & \multirow{3}{*}{34,200} \\  & Negative & 11,970 & 2565 & 2565 & \\ \multirow{3}{*}{HARD} & Positive & 32,795 & 7027 & 7027 & \\  & Negative & 32,795 & 7027 & 7027 & \\ \multirow{3}{*}{LABR} & Positive & 5511 & 1188 & 1188 & \\  & Negative & 5511 & 1188 & 1188 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of Sentiment Analysis Data Across Training, Validation, and Testing Sets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **ACRD** & **HARD** \\ \hline \multirow{2}{*}{MNB} & \{‘alpha’: 0.1, ‘fit\_prior’: False\} & \{‘alpha’: 0.1, ‘fit\_prior’: False\} \\  & \{‘estimator\_C’: 1, ‘estimator\_max\_iter’: 1000\} & \{‘estimator\_C’: 0.1, ‘estimator\_max\_iter’: 1000\} \\ \multirow{2}{*}{PAC} & \{‘estimator\_C’: 0.01, ‘estimator\_max\_iter’: 1000, ‘estimator\_tol’: 0.0001\} & \{‘estimator\_tol’: 0.001\} \\  & \{‘C’: 10, ‘penalty’: 12, ‘solver’: ‘saga’\} & \{‘C’: 1, ‘penalty’: 12, ‘solver’: ‘liblinear’\} \\ \multirow{2}{*}{DT} & \{‘criterion’:‘entropy’,‘max\_depth’:None,‘min\_samples\_leaf’:1, ‘min\_samples\_split’: 2, ‘min\_samples\_split’: 5, ‘splitter’: ‘best’\} \\ \multirow{2}{*}{KNN} & \{‘metric’:‘euclidean’,‘n\_neighbors’: 3,’weights’: ‘uniform’\} \\  & \{‘bootstrap’: False, ‘max\_depth’: None, ‘min\_samples\_leaf’: 1, ‘min\_samples\_split’: 2, ‘n\_estimators’: 100\} & \{‘metric’: ‘euclidean’, ‘n\_neighbors’: 3,’weights’: ‘uniform’\} \\ \multirow{2}{*}{RF} & \{‘bootstrap’: False, ‘max\_depth’: None, ‘min\_samples\_leaf’: 1, ‘min\_samples\_split’: 2, ‘min\_estimators’: 2, ‘min\_estimators’: 100\} & \{‘bootstrap’: False, ‘max\_depth’: None, ‘min\_samples\_leaf’: 2, ‘min\_samples\_split’: 2, ‘n\_estimators’: 100\} \\  & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Best hyper-parameters and cross-validation (CV) for conventional ML models.
# An EV Charging Scheduling Mechanism Based on Price Negotiation
## 3 Price Negotiation Model
### Initial Price Model for Charging Station Operators and EV User  
#### 3.1.1 Initial Price of EV User  
When a charging request is submitted, EV users will form an initial price that meets their own best expectation. To form this initial price, the following factors should be mainly considered.  
(1) The travelling requirement of EV user \(R_{i}\) in the next time period  
Obviously, the psychological expectation of the charging price of the EV user will increase if the travelling requirement is very urgent. \(R_{i}\) is quantified by the formulation (1).  
\[R_{i}=X_{i,n}\times(\lambda_{1}\times M_{i,n}+\frac{\lambda_{2}}{SOC_{i}}) \times\frac{\lambda_{3}}{n} \tag{1}\]  
\[X_{i,n}=\left\{\begin{array}{ll}1,\ \text{plan to travel in the next $n$ -- th time slot}\\ 0,\ \text{not plan to travel in the next $n$ -- th time slot}\end{array}\right.,n\geq 1 \tag{2}\]  
where \(M_{i,n}\) is the mileage requirement that is planned in the next \(n\)-th time slot, \(SOC_{i}\) is the State of Charge of \(ev_{i}\). \(\lambda_{1},\lambda_{2},\lambda_{3}\) indicate the influences of mileage requirement, current remaining battery energy, and urgency of travelling requirement, respectively.  
(2) The current load of the power grid \(L(t)\)  
Using a mobile device, the EV user will share information of the real-time load of the power grid. When \(L(t)\) is higher, the psychological expectation of the charging price of the EV user will increase. Otherwise, the price will increase.  
(3) The distance \(d_{i,j}\) between EV \(ev_{i}\) and charging station \(CS_{j}\)  
The charging convenience increases as \(d_{i,j}\) decreases, which means that charging requirement will be fulfilled in a relatively short time. Thus, the price that an EV user can bear will increase.  
Thus, the initial charging price of an EV user is formed as follows.  
\[p_{i,0}^{n}=p_{0}\times(R_{i}+\eta_{1}\times L(t)+\frac{\eta_{2}}{d_{i,j}}) \tag{3}\]where \(p_{0}\) is the baseline of price, \(\eta_{1},\eta_{2}\) indicate the influences of current load of the power grid and distance \(d_{i,j}\) between \(ev_{i}\) and \(CS_{j}\), respectively.
## 2 Numerical Simulation Modeling and Boundary Conditions
### Parameterization  
After establishing the mathematical model for coal seam water injection and permeability enhancement, the seepage patterns around the borehole under different water pressures and stress conditions were analyzed. The relationship between coal sample permeability and water pressure is illustrated in Figure 2. Subsequently, a comparative analysis was conducted to examine the impact of injection pressure on the radial seepage pressure and velocity around the borehole, assuming constant porosity. Based on the relationship between coal and rock permeability and pore water pressure under in situ stress conditions, a User-Defined Function (UDF) was implemented to incorporate permeability variations. This allowed us to analyze the effects of injection pressure on radial seepage pressure, seepage velocity, and water content around the borehole under varying permeability conditions. This analysis provides valuable insights into the relationship between injection pressure, flow rate, and the uniformity and extent of wetting, offering a reference for optimizing field process technologies.  
Figure 2: Variation curve of permeability of coal samples with hydraulic pressure.  
After the grid is divided, the relevant parameters are set: the laminar flow model is set as a viscous model, and the convergence accuracy of the calculation adopts the default value of 0.001. The fluid material is added and the physical properties of the fluid are set; this paper researches the seepage movement of water injection in the coal seam and determines the density of its physical properties as 1000 kg/m\({}^{3}\); the boundary properties and conditions are set; the upper and lower boundaries of the injection boreholes and the left and right boundaries are set to be the constant-pressure permeability boundaries, and the total pressures of inlet and outlet are calculated according to the standard atmospheric pressure. The total pressure is injected in amounts of 4, 10, 15, 20, and 25 Mpa. The total pressure at the exit is calculated according to the standard atmospheric pressure, the boundary conditions of the wall are kept unchanged at the default value, the fluid is set to be a porous medium model, and the parameters such as porosity are set as shown in Table 1.  
A numerical simulation analysis of water injection was conducted using five water pressure measurements of 4, 10, 15, 20, and 25 Mpa. The ground stress condition was set as the original rock stress, which assumed that the coal body did not undergo volume deformation and that the initial porosity was maintained. The pressure, velocity, and water distribution law within the coal body around the borehole were analyzed following 12, 36, and 72 h of water injection.
## 2 Review: Regional-Scale Assessment of Buildings' Seismic Vulnerability
### Machine Learning in Disaster-Risk Assessment  
DM has been seen as a powerful alternative to conventional statistical methods when dealing with multi-dimensional big data problems, insofar as they require no assumptions to be made about data distributions [19]. An urban natural-hazard risk assessment can be conceived of as an immensely complex multi-dimensional problem whose hazard, exposure, and vulnerability modules require masses of quantitative and qualitative data. As such, SVM--a DM technique derived from statistical-learning theory--has been widely and effectively used to solve natural-hazard risk problems involving landslides [20], typhoon-related floods [21], droughs [22], and climate change [23], by taking into account thousands and millions of multi-dimensional datasets relating to the natural, social, and economic aspects of an affected urban area. In contrast to many other natural hazards, which are connected to build environments via mechanisms that remain largely unknown, seismic risk has been assessed using CRM with relatively high degrees of success, thanks in part to advancementsin structural computational modeling. Accordingly, several studies have utilized SVM to map the underlying relationships between buildings' vulnerability and a range of their characteristics, such as number of stories, building length, and total ground areas [18; 24; 25; 26]. The results have demonstrated SVM's powerful ability to estimate buildings' seismic vulnerability efficiently on a large geographic scale, as well as its merit as an accurate, convenient tool for both the computational and semi-empirical methods described above.  
However, the outputs of the aforementioned SVM models of buildings' seismic vulnerability have been devoted to one particular type of seismic-resistant capacity, which reflects the capability of a building to withstand collapse during an earthquake [21; 22], such as maximum story drift ratio, as if that type alone applied to an entire building. In addition, such maximum story drift ratio, for example, defined as the maximum ratio of the difference in lateral displacements in between two consecutive floors to the story height under a fixed seismic intensity [27], cannot be applied to the estimation for the same building's response in different seismic demand. For this reason, micro-level outputs--like building-capacity curves, which can be used to evaluate different levels of damage to individual components, as well as building's different response coupled with different seismic-demand curves--should be estimated to facilitate more detailed and flexible assessment of buildings' seismic vulnerability.
## 3 Methodology
### Relationship between the Satellite-Observed Parameters and the Maximum Wind Speed
#### 3.1.1 TB Parameters
\begin{table}
\begin{tabular}{c c c} \hline \hline
**TB Parameter** & \
\begin{tabular}{c} **Correlation** \\ **Coefficient** \\ \end{tabular} &
\begin{tabular}{c} **Root Mean** \\ **Square Error (m/s)** \\ \end{tabular} \\ \hline TB19H\_MIN\_C100 & 0.84 & 6.67 \\ TB37H\_MIN\_C125 & 0.84 & 6.72 \\ TB19H\_MIN\_C125 & 0.83 & 6.98 \\ TB37H\_MIN\_C100 & 0.83 & 7.00 \\ TB19H\_MIN\_C075 & 0.82 & 7.01 \\ TB22V\_RAPT270\_C100 & 0.82 & 7.04 \\ TB22V\_RAPT270\_C125 & 0.82 & 7.14 \\ TB19H\_MIN\_A075100 & 0.82 & 7.17 \\ TB22V\_RAPT270\_C075 & 0.82 & 7.19 \\ TB19V\_MIN\_C100 & 0.81 & 7.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The highest correlated brightness temperature (TB) parameters.
## 4 Robust Distributed Rendezvous Control
### Handling of Dynamic Scenarios, including Faulty UUVs  
One may have a situation where a UUV is broken or the interaction link between two neighboring UUVs in \(T\) is broken by moving obstacles. Moreover, we may have a situation where an invisible UUV blocked by unknown obstacles appears suddenly. In these cases, every UUV stops moving, and we re-build a tree \(T\) utilizing Algorithm 1 in Section 4.1, so that network connectivity among UUVs can be re-established. Then, Algorithm 2 re-runs to achieve rendezvous at \(u_{N}\). In this way, we can handle dynamic scenarios which may happen due to unknown obstacles.  
As we re-build a tree \(T\) utilizing Algorithm 1 in Section 4.1, we may have a case where a tree \(T\) cannot be generated. This implies that a UUV may not be connected to the root, even after running Algorithm 1 in Section 4.1. For handling this case, we need to increase the number of neighbors for each UUV.  
For handling the case where a UUV may not be connected to the root, we increase \(P_{0}\) in Equation (5) for each UUV. As \(P_{0}\) increases, the number of neighbors can increase, since a UUV \(u_{i}\) is a neighbor to \(u_{i}\) if \(P(u_{i})>P_{thres}\) and \(P(u_{j})>P_{thres}\). By increasing the signal emission strength, \(u_{i}\) can detect a UUV that is located far from \(u_{i}\). This emission power increase was also used in [41], for improving the network connectivity in multi-agent systems. We acknowledge that by increasing the signal emission strength, \(u_{i}\) consumes more power.
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 3 System Model
Note that the reception is contaminated by the noise vector \(\mathbf{w}[n]\in\mathbb{C}^{N_{\text{R}}\times 1}\). To model the channel we have assumed that terminals are in LoS conditions. Hence, upon neglecting the multipath effect, we end up with:  
\[\mathbf{h}_{k}=\sqrt{\frac{G_{\text{R}}G_{R}(\theta_{k})}{L_{k}K_{B}Tb_{W}}} \mathbf{s}_{k} \tag{7}\]  
\[\left[\mathbf{s}_{k}\right]_{n+N_{x}m}=e^{j\frac{2\pi d}{\lambda}(n\sin(\theta _{k})\cos(\phi_{k})+m\sin(\theta_{k})\sin(\phi_{k}))}=e^{j\frac{2\pi d}{ \lambda}(nu_{k}+mv_{k})}, \tag{8}\]  
for \(0\leq n\leq N_{x}-1\) and \(0\leq m\leq N_{y}-1\). Thus, \(N_{x}N_{y}=N_{\text{R}}\). We use \([\mathbf{a}]_{i}\) to refer to the \(i\)-th element of the vector \(\mathbf{a}\). The phase term is formulated as a function of the carrier wavelength \(\lambda\) and the direction of the \(k\)-th signal that is located at elevation angle \(\theta_{k}\) and azimuth angle \(\phi_{k}\). The angles are measured with respect to the satellite antenna boresight direction. Let \(u_{k}=\sin(\theta_{k})\cos(\phi_{k})\) and \(v_{k}=\sin(\theta_{k})\sin(\phi_{k})\) denote the (u,v)-coordinates of the \(k\)-th user. The magnitude of the channel depends on the antenna gain of the \(k\)-th terminal \(G_{k}\), the antenna gain of the radiating element \(G_{R}(\theta_{k})\), the free space loss \(L_{k}\), the Boltzmann constant \(K_{B}\), the receiver noise temperature \(T\), and the carrier bandwidth \(B_{W}\). The radiation pattern of antenna elements can be approximated by \(G_{R}(\theta_{k})\approx\sqrt{G_{R}}\cos^{\theta}(\theta_{k})\), with no azimuth dependence [33]. Furthermore, the \(q\)-factor can  
\[x_{k}[n]=\sum_{m=0}^{N_{\text{S}}-1}x_{k}^{m}[n-Nm], \tag{1}\]0 Sequence structure for the RSBA scheme. be formulated as a function of the gain as \(q=\frac{1}{4}G_{R}-\frac{1}{2}\)[27]. The propagation losses are defined in dB as:  
\[x_{k}[n]=\sum_{m=0}^{N_{\text{S}}-1}x_{k}^{m}[n-Nm], \tag{1}\]1  
where \(f_{c}\) is the carrier frequency in GHz and \(d_{k}\) denotes the slant range in meters along the direction of the \(k\)-th user. According to [27], we can relate the slant range to the off-nadir angle \(\theta_{k}\) as follows:  
\[ \begin{array}{ll}d_{k}=&\left[R_{E}^{2}+(R_{E}+h)^{2}-2R_{E}(R_{E}+h)\sin \left(\cos^{-1}\left(\frac{R_{E}+h}{R_{E}}\sin\theta_{k}\right)\right)\right.\\ &+\sin^{-1}\left(\frac{R_{E}}{R_{E}+h}\right)\left(\frac{R_{E}+h}{R_{E}}\sin \theta_{k}\right)\right]^{1/2},\end{array}  \tag{10}\]  
where \(h\) is the altitude of the satellite and \(R_{E}\) is the Earth's radius. As [4] shows, the slant range can also be expressed in terms of the elevation angle of the terminal \(\psi_{k}\), namely,  
\[d_{k}= R_{E}\Bigg{[}\sqrt{\left(\frac{h+R_{E}}{R_{E}}\right)^{2}-\cos^{2} \psi_{k}}-\sin\psi_{k}\Bigg{]}. \tag{11}\]  
In Figure 3 we have depicted the satellite geometry to have a more clear understanding of the expressions. Notice that \(\psi_{k}\) is measured in the horizon plane.
## 6 Analysis of Orbit Determination Performance  
In this section, we present simulation results of the orbit determination method detailed in Section 3 for a Galileo-like constellation of 24 MEO satellites. The \(15\times 24\) broadcast parameters are fitted to the ISR and GSR measurements collected over a time interval varying from 1 to 3 h. We analyze two different scenarios, with different numbers and distribution of ground stations. The first three stations, GS1-GS3 (Tromse, Nemea and Tenerife), are located within the European geographical area. A fourth station GS4 (Papeete, French Polynesia) is added to analyze the impact of placing a station on the opposite side of the Earth.  
Two scenarios are considered: (1) a regional European network of three ground stations GS1-GS3 and (2) a global network of stations GS1-GS4. Two figures of merit are used to assess the performance of the OD method. The first is the RMSE of the estimated orbit in radial, along- and cross-track directions with respect to the simulated true orbit (Section 4). The second is the orbit-only Signal-in-Space Range Error (SiSRE\({}_{\text{orb}}\)), which provides a metric to express the range error introduced by the space segment. The SiSRE\({}_{\text{orb}}\) combines errors in all three spatial components [10]:  
\[\text{SiSRE}_{\text{orb}}=\sqrt{\left(W_{\text{R}}\Delta R\right)^{2}+W_{\text {AC}}^{2}(\Delta A^{2}+\Delta C^{2})}, \tag{7}\]  
where \(\Delta R\), \(\Delta A\), \(\Delta C\) are the average absolute radial, along- and cross-track errors, respectively, over all 24 satellites in a given time interval. Weights \(W_{\text{R}}\) and \(W_{\text{AC}}\) are chosen as for the Galileo system (\(W_{\text{R}}=0.98\) and \(W_{\text{AC}}^{2}=1/61\)) [10].
# GPS/BDS Medium/Long-Range RTK Constrained with Topospheric Delay Parameters from NWP Model
## 2 Materials and Methods
### GPS/BDS Medium/Long-Range RTK Algorithm Constrained with NWP Model
For the standard RTK processing, the unknown parameter vector \(\mathsf{X}\) can be described as  
\[\mathsf{X}=\left(\mathsf{B}\Delta_{\text{ZWD}}\Delta\mathsf{N}_{\text{Gi}} \Delta\mathsf{N}_{\text{Bj}}\right)^{T} \tag{5}\]  
As with the standard RTK, we remove the \(\Delta_{\text{ZHD}}\) firstly in the NWP-constrained RTK (both GPS alone and GPS/BDS RTK). In a different way from the standard RTK, the priori \(\Delta_{\text{ZWD}}\) predicted by the NWP model with a prior variance of the wet troposphere parameters is introduced to the troposphere vector in this algorithm. At the same time, a wet delay residual \(\text{Resi}_{\Delta_{\text{ZWD}}}\) is estimated as an unknown parameter during the processing in order to account for possible imperfections inherent in the NWP. The constraints of the residual \(\Delta_{\text{ZWD}}\) are referred to as the prior variance, and the definition of this prior variance will be proposed in the next section. The unknown parameter vector \(\mathsf{X}\) in the NWM-constrained RTK can be expressed as  
\[\mathsf{X}=\left(\mathsf{B}\text{Resi}_{\Delta_{\text{ZWD}}}\Delta\mathsf{ N}_{\text{Gi}}\Delta\mathsf{N}_{\text{Bj}}\right)^{T} \tag{6}\]  
where \(\text{Resi}_{\Delta_{\text{ZWD}}}\) is the wet delay residual. Comparing the RTK algorithms, they have the same number of unknown troposphere parameters. However, the NWP-constrained RTK applies the external meteorological information and the prior variance information. The RTK performance of this algorithm would be better in theory, if the priori \(\Delta_{\text{ZWD}}\) predicted by NWP model has high accuracy.