esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Future Research  
The mode detection accuracies may be further improved by incorporating more complex ANNs with more hidden layers and varying the number of neurons. There is a need for a consistent guideline for optimizing the complexity of the designs and layout of the ANNs for the mode detection.  
The MA layers can be implemented as an online database and be available for real-time access in the case of certain sections being unavailable due to accidents or temporarily paralyzed by grid-lock situations in the CBD.  
Routes of certain modes of transportation are only available at particular ranges of elevations. For example, a boat mode usually operates at sea level, while elevated rails for the metro is always at higher elevations from the ground. Different transit bus routes may operate within different ranges of elevations. When combined with digital elevation models (DEM) it is possible to identify the mode of transportation more easily by incorporating the operating height information of each mode.  
Currently in Dubai, where this study has been conducted, the metro trains operate above the ground. However, the majority of metro systems internationally operate underground. It is a challenge to enhance the proposed approach with underground applications without GPS data due to the blocked line of sight from GPS satellites to the metro users. It would be interesting to see how other sensors in smartphones, combined with mode availability layers, perform together for mode detection.  
This paper briefly develops universal mode detection ANNs, followed by focusing on a particular ITS application of traffic monitoring. In the future, unique and specific ITS applications can be built for each mode. For example, for bike mode-related applications, the ANNs can further be optimized for detecting the bike mode followed by conducting slope analyses of the ground by processing the DEM with slope-determining GIS operations in 3D for the flattest route instead of the shortest route computations for the bike mode.
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
###### Abstract  
In the context of rapid urbanization, the refined management of cities is facing higher requirements. In improving urban population management levels and the scientific allocation of resources, fine-scale population data plays an increasingly important role. The current population estimation studies mainly focus on low spatial resolution, such as city-scale and county scale, without considering differences in population distributions within cities. This paper mines and defines the spatial correlations of multi-source data, including urban building data, point of interest (_POI_) data, census data, and administrative division data. With populations mainly distributed in residential buildings, a population estimation model at a subdistrict scale is established based on building classifications. Composed of spatial information and attribute information, _POI_ data are spaced irregularly. Based on this characteristic, the text classification method, frequency-inverse document frequency (_TF-IDF_), is applied to obtain functional classifications of buildings. Then we screen out residential buildings, and quantify characteristic variables in subdistricts, including perimeter, area, and total number of floors in residential buildings. To assess the validity of the variables, the random forest method is selected for variable screening and correlation analysis, because this method has clear advantages when dealing with unbalanced data. Under the assumption of linearity, multiple regression analysis is conducted, to obtain a linear model of the number of buildings, their geometric characteristics, and the population in each administrative division. Experiments showed that the urban fine-scale population estimation model established in this study can estimate the population at a subdistrict scale with high accuracy. This method improves the precision and automation of urban population estimation. It allows the accurate estimation of the population at a subdistrict scale, thereby providing important data to support the overall planning of regional energy resource allocation, economic development, social governance, and environmental protection.  
building classification; multi-source data; population estimation model; fine-scale population estimation +
Footnote †: journal:  
1
Footnote 1: [[https://www.mdpi.com/journal/futureinternet](https://www.mdpi.com/journal/futureinternet)]([https://www.mdpi.com/journal/futureinternet](https://www.mdpi.com/journal/futureinternet))
# Enhanced Quasi Type-1 PLL-Based Multi-Functional Control of Single-Phase Dynamic Voltage Restorer
## 4 Super Twisting Sliding Mode Controller Design  
In this Section, tracking error dynamics (6) and (7) will be used for the control design. For this purpose, let us consider that the control signal is composed of \(u=u_{0}+u_{n}\), where \(u_{0}\) is the nominal control signal and \(u_{n}\) is the nonlinear part of the control signal. If we consider \(u_{0}=(1/V_{dc})\xi_{1}\) and \(u_{n}=u_{ST}/(\delta V_{dc})\), then the total control signal can be written as follows.  
\[u=\frac{1}{V_{dc}}\Bigl{(}\xi_{1}+\frac{u_{ST}}{\delta}\Bigr{)}. \tag{32}\]  
Then, the tracking error dynamics (6) and (7) can be rewritten as follows.  
\[\frac{d\xi_{1}}{dt} = \xi_{2}, \tag{33}\] \[\frac{d\xi_{2}}{dt} = u_{ST}+w. \tag{34}\]  
Tracking error dynamics (33) and (34) can be viewed as a perturbed second-order integrator. Numerous control techniques can be employed to stabilize the error under the presence of perturbation \(w(t)\). In this study, we consider a second-order SMC [31, 37] in the form of super-twisting SMC [38]. In order to design the super-twisting SMC, let us consider the following sliding surface.  
\[\sigma_{e}=\xi_{2}+\lambda_{1}\xi_{1},\lambda_{1}>0. \tag{35}\]  
Then, the controller \(u_{ST}\) in (34) can be designed as follows:  
\[u_{ST}=-\lambda_{1}\xi_{2}-\lambda_{2}|\sigma_{e}|^{\frac{1}{2}}\text{sgn}( \sigma_{e})-\lambda_{3}\int_  
Let us consider the following variables.  
\[\zeta_{1} = \sigma_{e}, \tag{39}\] \[\zeta_{2} = -\lambda_{3}\int_{0}^{t}\text{sgn}(\sigma_{e}(\tau))d\tau+w(t),\] (40) \[\frac{dw}{dt} = \eta(t). \tag{41}\]  
Then, the dynamics of the sliding surface (38) can be rewritten as follows.  
\[\frac{d\zeta_{1}}{dt} = -\lambda_{2}|\sigma_{e}|^{\frac{1}{2}}\text{sgn}(\sigma_{e})+ \zeta_{2}, \tag{42}\] \[\frac{d\zeta_{2}}{dt} = -\lambda_{3}\int_{0}^{t}\text{sgn}(\sigma_{e}(\tau))d\tau+\eta(t). \tag{43}\]  
Then, for the selected control gain (37), finite-time convergence of the variables \(\zeta_{1}\) and \(\zeta_{2}\) can easily be established by using the results presented in Levant [37]. In order to implement the super-twisting controller, (32), (35), and (36) are required. An implementation block diagram of the super-twisting sliding-mode controller is provided in Figure 7.
###### Abstract  
Remote sensing has become an essential tool for geological exploration, disaster monitoring, emergency rescue, and environmental supervision, while the limited number of remote sensing satellites and ground stations restricts the timeliness of remote sensing services. Satellite Internet has features of large bandwidth, low latency, and wide coverage, which can provide ubiquitous high-speed access for time-sensitive remote sensing users. This study proposes a near real-time remote sensing (NRRS) architecture, which allows satellites to transmit remote sensing data via inter-satellite links and offload to the Earth Stations from the satellite that moves overhead. The NRRS architecture has the advantages of instant response, ubiquitous access, and intelligent integration. Based on a test communication constellation, a vehicle-mounted Satcom on-the-move experiment was conducted to validate the presented NRRS architecture. The results show that the whole process from demand collection to image acquisition takes no more than 25 min, which provides an engineering reference for the subsequent implementation of near real-time remote sensing.  
a _aerospace_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Near Real-Time Remote Sensing Based on Satellite Internet: Architectures, Key Techniques, and Experimental Progress. _Aerospace_**2024, _11_, 167. [[https://doi.org/10.3390/aerospace11020167](https://doi.org/10.3390/aerospace11020167)]([https://doi.org/10.3390/aerospace11020167](https://doi.org/10.3390/aerospace11020167))  
Academic Editor [PERSON]  
Received: 13 October 2023  
Received: 7 February 2024  
Accepted: 8 February 2024  
Published: 19 February 2024
## 2 Two Degrees of Freedom (2-DOF) Model
### Period Elongation  
The natural frequencies of the system can be found by solving the eigenvalue problem:  
\[\Big{|}\mathbf{K}-\omega^{2}\mathbf{M}\Big{|}=0 \tag{6}\]  
\[\omega^{4}\cdot m_{s}\cdot m_{g}-\omega^{2}\cdot m_{g}\cdot k_{s}-\omega^{2} \cdot m_{s}\cdot(k_{g}+k_{s})+k_{s}\cdot k_{g}=0 \tag{7}\]  
The equation may be easily solved by putting:  
\[\omega^{2}=\lambda \tag{8}\]  
\[\alpha=\left(\begin{array}{c}\omega_{g}\\ \omega_{s}\end{array}\right)^{2} \tag{9}\]  
and:  
\[\beta=\frac{m_{s}}{m_{g}} \tag{10}\]  
and by dividing (7) with \(m_{s}\cdot m_{g}\):  
\[\lambda^{2}-\lambda\omega_{s}{}^{2}(1+\alpha+\beta)+\alpha\cdot\omega_{s}{}^{ 4}=0 \tag{11}\]  
The solution of (11) leads two roots, given by:  
\[\lambda=\frac{\omega_{s}{}^{2}(1+\alpha+\beta)\pm\sqrt{\left(\omega_{s}{}^{4} (1+\alpha+\beta)\right)^{2}-4\alpha\cdot\omega_{s}{}^{4}}}{2} \tag{12}\]  
where the term under the square root is the discriminant that may be rewritten as:  
\[\Delta=\omega_{s}{}^{4}\Big{[}(1-\alpha)^{2}+2\beta(1+\alpha)+\beta^{2}\Big{]} \tag{13}\]Since soil structure interaction effects are significant when the structural frequency is larger than that of the soil \((m_{g}>>m_{s}),\ \beta\ \sim 0\ (13)\) may be rewritten as:  
\[\Delta={\omega_{s}}^{4}\Big{[}{\left(1-\alpha\right)}^{2}\Big{]} \tag{14}\]  
Substituting (14) inside (12), leads to the two natural frequencies of the system:  
\[{\omega_{1}}^{2}={\omega_{s}}^{2}\frac{1+\alpha+\beta+1-\alpha}{2} \tag{15}\]  
\[{\omega_{2}}^{2}={\omega_{s}}^{2}\frac{1+\alpha+\beta-1+\alpha}{2} \tag{16}\]  
(15) and (16) may be rewritten as:  
\[\omega_{1}=\omega_{s}\sqrt{\frac{2+\beta}{2}} \tag{17}\]  
\[\omega_{2}=\omega_{s}\sqrt{\frac{2\alpha+\beta}{2}} \tag{18}\]  
and consequently, the fundamental periods of the system are:  
\[T_{1}=\frac{T_{S}\ \sqrt{2}}{\sqrt{2+\beta}}\ \sim T_{S} \tag{19}\]  
\[T_{2}=\frac{T_{S}\ \sqrt{2}}{\sqrt{2\alpha+\beta}} \tag{20}\]  
It is worth noting that, since \(\alpha\) and \(\beta\) are small values, \(T_{2}\) is bigger than \(T_{1}\) and the coefficient \(\frac{\sqrt{2}}{\sqrt{2\alpha+\beta}}\) describes the period elongation that is demonstrated to depend both on the ratio of the masses and on the ratio between the frequencies of the structure mass and of soils. \(T_{1}\) is the first natural period of the system and depends on the structural characteristics. For small \(\beta\)\(T_{1}\) can be approximated with \(T_{S}\) (Equation (19)).
# Fast Stepwise Inertial Control Scheme of a DFIG for Reducing Second Frequency Drop
## 3 Frequency Support Schemes of a DFIG  
When implementing the frequency support, the relationship between \(\Delta P_{DFIG}\) and rotor speed of the DFIG is represented as:  
\[\Delta P_{DFIG}=J_{DFIG}\omega_{r}\frac{d\omega_{r}}{dt} \tag{9}\]  
where \(\Delta P_{DFIG}\) and \(J_{DFIG}\) are the active power variation and moment of the DFIG, respectively.  
According to the definition of inertia constant by the synchronous generator, the inertia constant of the DFIG (\(H_{DFIG}\)) is expressed as:  
\[H_{DFIG}=\frac{J_{DFIG}\omega_{n}^{2}}{2S_{DFIG}} \tag{10}\]  
where \(\omega_{n}\) and \(S_{DFIG}\) are the rated rotor speed and rated capacity of the DFIG, respectively.  
Figure 1: Vector control of the rotor side converter.  
Rearranging (9), and converting to a per unit (p.u.) system:  
\[2H_{DFIG}\omega_{pu}\frac{d\omega_{pu}}{dt}=\Delta P_{DFIG\_pu} \tag{11}\]  
where \(\Delta P_{DFIG\_pu}\) and \(\omega_{pu}\) are \(\Delta P_{DFIG}\) and the rotor speed of the DFIG in p.u., respectively.  
As reported in [38], the system frequency in p.u. is the same as \(\omega_{pu}\); therefore, the expression of (11) can be modified as a function of the system frequency, as follows:  
\[2H_{DFIG}f_{pu}\frac{df_{pu}}{dt}=\Delta P_{DFIG\_pu} \tag{12}\]  
After integrating (12), the instantaneous frequency in p.u. is derived as:  
\[\Delta P_{DFIG\_pu}\times\Delta t=H_{DFIG}\times\left[f_{pu}^{2}(t+\Delta t)-f_ {pu}^{2}(t)\right] \tag{13}\]  
where \(f_{pu}(t)\) and \(f_{pu}(t+\Delta t)\) are the grid frequencies at \(t\) and \(t+\Delta t\), respectively. Then, (13) is rearranged as:  
\[f_{pu}(t+\Delta t)=\sqrt{\frac{(\Delta P_{DFIG\_pu}\times\Delta t)}{H_{DFIG}}+ f_{pu}^{2}(t)} \tag{14}\]  
Hence, (14) illustrates that the DFIG sustains the system frequency during a frequency disturbance. During the frequency support phase (FSP), the DFIG provides a higher \(\Delta P_{DFIG\_pu}\); therefore, the maximum frequency deviation reduces.
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 2 Materials and Methods  
### Super-Resolution Imaging Process  
The process of super-resolution ultrasound imaging is quite like that of the single-molecule localization microscopy (SMLM) in optics [4; 6], which is usually decomposed into four steps as shown in Figure 1.  
In the SMLM, the first step is imaging light-activated fluorescent molecules that act as tiny, randomly distributed pinpricks of light. The use of low light intensities and the fact that the molecules' activation is inherently random ensures that only a sparse subset is turned on at any one time. Thus, these point-like light sources are separated by more than half a wavelength, so the image of each one (a blurred spot called the PSF) does not overlap with that of its neighbors. In ultrasound super-resolution imaging, the flowing microbubbles excited by the acoustic waves are similar to those molecules excited by the fluorescence. By controlling the injection concentration and injection volume,the microbubbles can reach the appropriate concentration. In this case, the microbubbles (or its group) do not overlap in vessels. The echoes enhanced by a microbubble group can be recorded by the ultrasound plane wave imaging, which uses the low emission energy and keeps the microbubbles unbroken [18; 19; 20]. After comparing sequential images, the locations of the few, well-separated, flowing microbubbles would be pinpointed.  
The second step is to determine the exact position of each point-like source by finding the center of the PSF. This is possible for well-separated sources, because the shape of the PSF can be devised in advance. The resolution of the final imaging result depends largely on the design of the PSF. For this step, ultrasound imaging and fluorescence imaging are the same. We call this step microbubble localization in the following sections.  
The third step is to repeat the illumination and detection steps many times. A different set of separated point-like sources is detected each time, until a sufficient density of source points has been obtained. In acoustics, microbubbles vibrate under the excitation of scanning ultrasound, so we do not need an extra operation like the illumination in optics.  
In the final step, by marking the positions of all these point sources on a single meta-image, a super-resolved picture can be built up. The spatial resolution in this image can exceed the diffraction limit, because it is determined by the accuracy with which the position of each source can be estimated. The accuracy of the reconstructed super resolution image is closely related to the chosen visualization method. We call this step the trajectory tracking in the following sections.  
According to the above analysis, two steps of the microbubble localization and trajectory tracking are most closely related to the quality of reconstructed images. Therefore, we improve the corresponding algorithms for these steps respectively in order to get more precise super-resolution reconstruction images in this paper.
# Echocardiography in the Diagnosis and Management of Tricuspid Atresia
## 5 Echo-Doppler Studies Following Palliative/Corrective Interventions
### Summary of Palliative/Corrective Interventions  
Total surgical correction is not feasible for patients with TA. However, physiologic correction is feasible, which was described in the early 1970s by [PERSON] [32] and [PERSON] [33] and their associates, concurrently. Since its early description in the 1970s, the Fontan procedure has undergone a number of modifications, as reviewed elsewhere [34; 35; 36; 37; 38]. Currently, total cavopulmonary connection, advocated by [PERSON] and his colleagues [39], is routinely performed. Due to high PA pressure/resistance in the neonates, the procedure cannot be performed in neonates and young infants. Therefore, it is undertaken in three stages: Stage I. Palliation at the time of initial presentation, usually in the neonatal period (see below); Stage II. Bidirectional Glenn [40] between 3 and 12 months of age (along with the ligation of prior aorto-pulmonary shunt); and Stage III. Fontan completion one to two years later by redirecting the IVC blood flow into the PAs, either via a lateral tunnel [41] or via an extracardiac nonvalved conduit [41; 42]. Most of the Fontan procedures performed today include a fenestration [43; 44]. The fenestration may be closed at a later date by a transcatheter methodology [36; 37; 38; 43]. Staged, total cavopulmonary connection with an extracardiac nonvalved conduit and fenestration has become standard of care for all single ventricle lesions, including TA [36; 37; 38].  
Most of the patients with TA require symptomatic relief at initial presentation [22]. Babies with pulmonary oligemia are addressed with initial Prostaglandin E\({}_{1}\) infusion, followed by a modified Blalock-Taussig shunt operation [45] or other types of aorto-pulmonary Gore-Tex graft shunts. Infants with pulmonary plethora are treated with anticongestive measures (if they are in congestive heart failure (CHF)) followed by the banding of the PA [46]. In some Type II (transposition of the great arteries) patients, the VSD may be small and obstructive, and is usually treated with Damus-Kaye-Stansel (DKS) procedure. Some patients may develop inter-atrial obstruction requiring balloon atrial septostomy [47; 48; 49], and others may need the relief of aortic coarctation. A few patients may have adequate pulmonary blood flow and need no intervention in the neonatal period.
## 3 System Model
### Dynamic Path and Charging System Process  
The procedures of dynamic path-4 planning and charging selection system includes a real-time information-collection phase and a real-time information-access and decision-making phase, as shown in Figure 2.  
In the real-time information-collection stage, RSUs play a critical role in caching the information flow between charging infrastructures and AEVs. Procedure 1 in the Figure 2 updates the charging infrastructure service status and AEV travel information to nearby RSUs. However, due to the limited coverage of RSUs at intersections, AEVs within the intersection communication range can exchange information with the RSU through one-hop V2R communication. By contrast, AEVs outside the intersection communication range need to transmit information through multi-hop V2V and V2R communication. We assume that each charging infrastructure is deployed within the communication range of an RSU, and the service status information of charging infrastructures can be directly transmitted to RSUs through its built-in intelligent device. The intersection communication range is defined as the overlapping area of the AEV and RSU communication range, as shown in Figure 3. AEV1 and RSU are not within range for intersection communication, which means they cannot communicate directly. By contrast, AEV2 and RSU are within the range of intersection communication, allowing them to communicate through V2R.  
In the real-time information-access and decision-making phase, when the SoC level of the AEV is lower than the charging threshold, the AEV sends charging requests to the nearby RSU through one-hop V2R or multi-hop V2V, and the RSU receiving the charging request replies with real-time information to the AEV, as shown in procedures 2 and 3 in the Figure 2. The AEV then uses the information to complete path and charging optimization and send decision results to the RSU, as shown in procedure 4 in the Figure 2. When the RSU receives the traffic accident information, it can transmit that information to the corresponding AEV via one-hop V2R or multi-hop V2V communication, as shown in procedure 5 in the Figure 2. The AEV can use the updated information to replan the path and charging selection, and send updated decision results to RSU, as shown in procedure 6 in the Figure 2.
## 2 Numerical Methodology
### Turbulence Models
#### 2.3.2 Reynolds-Averaged Navier-Stokes (RANS)  
In the context of RANS simulations, the Spalart-Allmaras turbulence model is a one-equation model solving a transport equation for a variable called \(\widetilde{v}\), representing an approximation of viscosity [20; 21]. This variable is then used to adjust a turbulent viscosity \(v_{t}\). Within the lattice Boltzmann method (LBM), the turbulent viscosity can be employed to modify the relaxation time in the collision operator. In this investigation, a modified version of the model, presented in Equation (10), was implemented to enhancethe numerical behaviour near the wall within the laminar sublayer. The transport equation and the complete set of parameters associated with the model are provided below:  
\[\frac{\partial\overline{v}}{\partial t}+\overset{\rightarrow}{u}.\left( \overset{\rightarrow}{\
abla}\overline{v}\overline{v}\right)=\frac{1}{\sigma} \Bigg{[}\overset{\rightarrow}{\
abla}.\left((v+\overline{v})\overset{ \rightarrow}{\
abla}\overline{v}\right)+c_{b2}\bigg{(}\overset{\rightarrow} {\
abla}\overline{v}\bigg{)}^{2}\Bigg{]}+c_{b1}\widetilde{S}\overline{v}-c_{w1 }f_{w}\bigg{(}\frac{\overline{v}}{d}\bigg{)}^{2} \tag{10}\]  
\[v_{t}=\widetilde{v}f_{v1},\ \ \ \ \ f_{v1}=\frac{X^{3}}{X^{3}+c_{v1}^{3}},\ \ \ \ X= \frac{\widetilde{v}}{v},\]  
\[c_{b1}=0.1355,\ \ \sigma=\frac{2}{3},\ \ c_{b2}=0.622\,\ \ \kappa=0.41,\]  
\[c_{w1}=\frac{c_{b1}{}^{2}}{\kappa}+\frac{1+c_{b2}}{\sigma},\ \ \ \ c_{w2}=0.3,\ \ c_{w3}=2,\ \ c_{v1}=0.1\]