esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 3 Real-Time ZTD Grid Product Generation
### Inverse Scale Height Model  
Figure 1 presents the vertical profiles of ZTD derived from ERA-Interim at the nearest grid points from station BJFS, GDZJ and LHAS. These three stations locate in different areas in China with significantly different latitudes and altitudes. The ZTD profiles show exponentially or near exponentially decreasing trends with altitudes.  
Similar to [8], the exponential function for ZTD vertical variations can be expressed as:  
\[ZTD(h)=ZTD_{0}\cdot\exp(-\frac{1}{H}\cdot h) \tag{8}\]  
where \(ZTD_{0}\) is zenith tropospheric delay at MSL, \(H\) is the scale height, and \(h\) denotes the location altitude above MSL.  
Equation (8) can also be modified to convert ZTD at a given height (\(h_{1}\)) to the required height (\(h\)) as:  
\[ZTD(h)=ZTD_{1}\cdot\exp(-\frac{1}{H}\cdot(h-h_{1})) \tag{9}\]  
where \(ZTD_{1}\) is zenith tropospheric delay at the given height \(h_{1}\).  
Considering the practical situations, we rarely do PPP for GPS receivers which are located vertically too far away from the ground surface. Therefore, we designed two ZTD fitting tests here. In the first test, the whole ZTD profiles (from 1000 hPa to 0.1 hPa) at each grid point were fitted using (9), while in the second test only ZTD at altitudes within 3 km from the terrain surface were used. The bias and standard deviation (STD) as well as the maximum and minimum values of fitting residuals are summarized in Table 4. The comparisons show that in the terrain \(\pm 3\) km test the fitting residuals are much smaller with STD of 0.42 cm. Therefore, in the following sections, we will only fit ZTD within 3 km from the terrain surface (surface altitude are acquired from ERA-Interim products) to build the inverse scale height (\(1/H\)) model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fitting Layer** & **Bias** & **STD** & **Max** & **Min** \\ \hline All & \(-0.39\) & \(1.69\) & \(2.48\) & \(-3.26\) \\ Terrain \(\pm 3\) km & \(-0.001\) & \(0.42\) & \(0.59\) & \(-0.55\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of ZTD fitting residuals on 1 January 2014 (unit: cm).  
Figure 1: Profiles of ZTD at the nearest grid points of the GPS stations, BJFS, GDZJ and LHAS at UTC 0, 1 April 2014.
## 4 UAV Navigation and Control
### Path Planning and Trajectory Design  
Based on the existing papers and practical considerations, we have classified the literature that has considered UAV navigation into three categories:  
1. Two-dimensional path planning where UAV flies at a fixed altitude and the UAV waypoints are optimized through the path in a horizontal plane.
2. Two-dimensional path-plus-speed considering the UAV energy consumption for propulsion.
3. Three-dimensional trajectory optimization [125] while considering practical UAV navigation factors and motion constraints such as: 1. [label=()] 2. Obstacle-free trajectory planning for crash avoidance navigation [126]. 3. Considering kinematic motion constraints such as speed, acceleration and nonholonomic constraints [127,128]. 4. Dynamic-aware motion primitives or input jerks of the trajectory to eliminate path tracking controller [129].  
**Remark 8**.: _From the practical point of view, a path-tracking controller is required for path-planning optimization methods that determine waypoints of the UAV path rather than UAV motion inputs._  
**Remark 9**.: _The difference in path planning (in the first group of works) with trajectory design is that, in the latter, time is an extra factor that is considered in the optimization problem. Therefore, in addition to the waypoint, the UAV speed along the path should be optimally determined (second group). Nevertheless, when it comes to obstacle-free UAV navigation in dense environments (e.g., for IoVs support in smart cities) kinodynamic-aware motion constraints and motion primitives should be determined to make the trajectory practically feasible (third group)._  
**Remark 10**.: _In the majority of relevant works in the wireless communication community, obstacle-free navigation considering motion constraints has been overlooked [36,40,130], which makes it impractical from the robotic navigation perspective in practice. Additionally, in some works in the wireless communication community, the path planning is called trajectory, which is misleading from the robotic and UAV motion perspectives._
## Abstract  
This paper presents a robust proportional derivative adaptive nonsingular finite-time synergetic tracking control (PDAFS) for a parallel Delta robot system. First, a finite-time synergetic controller combined with a proportional derivative (PD) control is constructed based on an object-oriented model to fulfill the robust tracking control of the robot. Then, an adaptive radial basis function approximation neural network (RBF) is designed to compensate for the effects of uncertainty parameters and external disturbances. Second, a second-order sliding mode (SOSM) differentiator is implemented to reduce the chattering noises due to the low-resolution encoders. Third, the stability theorems of the proposed control scheme are provided, where the Lyapunov stability theory is used to prove the theorems. Then, simulations of the helix trajectory tracking and the pick-and-place task are demonstrated on the Delta robot to validate the advantages of the proposed control scheme. Based on the advances, an implementing control system of the proposed controller is performed to improve the Delta robot's performance in the experiments.  
Keywords:adaptive synergetic control; dynamics delta robot; radial basis function approximation; second-order sliding mode observer +
Footnote †: journal: Applied Sciences
## 3 Results  
### Selection of the Polymers  
The AABB polymers are a relatively new family of biodegradable biomaterials that are obtained by the interaction of basic bis-nucleophilic monomers, amino-acid-based diamine-diesters [23; 30], with various bis-electrophiles using either IP (when using various dichlorides as bis-electrophiles) or SAP (when using various activated diesters as bis-electrophiles) [22; 23; 30]. Three main classes of AABB polymers--PEAs, PEURs, and PEUs--Were synthesized on the basis of diamine-diester monomers. For a systematic study of the NPs' fabrication process from the AABB polymers with the purpose of obtaining NPs of desired size, zeta-potential, and stability, two classes of AABB polymers--PEUs and PEAs--Were selected for the following reasons: The PEUs **1F6** and **1L6** and the PEA **8L6** are obtained using readily available purchasable and cheap bis-electrophilic monomers, triphosphate and sebacoyl chloride; the activated di-p-nitrophenyl ester of adipic acid used for synthesizing the PEA **4F4** was also obtained via the available alipoyl chloride. Among other reasons, apart from availability and cheapness, the selection of the PEUs and PEAs was stipulated by the glass transition temperatures (\(T_{g}\)) of the polymers and a low pliability within the temperature range 20-40 \({}^{\circ}\)C (the temperature range within which the NPs are prepared and can be applied as drug carriers) of the polymers in either dry or wet state, which is important for preventing the NPs' aggregation. The third class of AABB polymers, PEURs, are synthesized using diol-bis-chloroformates [20], which are not purchasable monomers and should be custom-made. In addition, the AABB PEURs show low \(T_{g}\) and are highly pliable, especially in wet state, which increases the chances of their aggregation under the conditions of the nanoprecipitation applied for fabricating the NPs.  
As was emphasized above, after the biodegradation of the AABB polymers very little or no local acidity causing inflammation is built up [39; 40]. Therefore, these polymers are especially promising for constructing biodegradable micro and nanoscaled devices used as containers for drug delivery to tissues that are highly sensitive to acidic media.  
The neutral (having no charge) AABB polymers composed of hydrophobic \(\alpha\)-amino acids L-phenylalanine (**F**) and L-leucine (**L**), selected for a systematic study of the NPs fabrication using nanoprecipitation, were: Two PEURs, **1F6** and **1L6**, and two PEARs, **4F4** and **8L6**. The structures of the selected AABB polymers are depicted in Figure 1; their MW characteristics are given in Table 1.  
One L-arginine (**R**)-based cationic AABB polymer, PEA **8R6** was also used in the present study for imparting the positive charge to the NPs via blending with the neutral polymers above. The **8R6** is rather hydrophobic and dissolves in water upon heating to 60-70 \({}^{\circ}\)C and precipitates when cooled to r.t. so it will not easily be washed out from the NPs in the water phase. The structure of the PEA **8R6** is depicted in Figure 2; its molecular weight characteristics are given in Table 1.
# The Energy Consumption and Robust Case Torque Control of a Rehabilitation Hip Exoskeleton
## 2 Theory
### Sit-to-Stand and Walking Activity  
Rising from a seated position is a primary step towards pursuing gait mobility for daily activities and improving the quality of life. The STS is a crucial link between a static position and a dynamic activity, which is still a challenging aspect after a stroke [39]. In addition, the STS stimulates the circulatory and respiratory systems, reduces spasticity and increases bone density [40].  
Therefore, for rehabilitation purposes, repetitive STS activities at various torque levels representing five angular speed movements were performed and analysed, followed by an analysis of walking movements at five torque levels equivalent to walking on a treadmill at various linear speeds (0.5-2.6 m/s). To control the rehabilitation hip exoskeleton, an admittance control strategy was used in which the hip torque of a healthy person, \(\mathcal{T}_{\text{HH}}\), at various torque levels (\(TL1\)_to_\(TL5\)) was used for reference control signals. The torque signal of a healthy hip performing STS was used to control the STS activity, \(\mathcal{T}_{\text{HH-STS}}\), whereas the torque signal of a healthy hip performing walking movements, \(\mathcal{T}_{\text{HH-W}}\), was applied to the walking control strategy. The torque control signals for both STS and walking are further discussed in [8,41,42] and are also shown in Figures 1 and 2.  
Because higher movement speeds require a higher torque, a lower torque level was selected for low-speed movement or when the patient's joint required a small amount of torque [43]. In addition, people have different disorders and rehabilitative needs; therefore, five torque levels were used in the control strategy to fulfil the various rehabilitation sessions. Figures 1 and 2 show that, if the exoskeleton was designed to train patients at a faster speed, the maximum hip torque signal of a healthy person was set at the fifth level as a reference control signal. On the other hand, the torque was set to the first level if the minimum walking speed (0.5 m/s) was needed.  
Lastly, the rehabilitation hip exoskeleton aimed to restore the impaired hip torque signal to that of a healthy one by the end of the treatment program. Healthy torque signals can vary with speed, which can be assigned and modified by a physiotherapist to tackle different conditions and needs. For this reason, the hip torque signal at various speeds was controlled using an LQR. Then, the control behaviour was analysed for the robustness; RMSEs, which show the differences between the desired and measured torque signals; and the energy needed to perform repetitive sessions, which will be illustrated later.
# Improved Anticancer Activity of the Malloapelta B-Nanoliposomal Complex against Lung Carcinoma
## 3 Results  
### Preparation and Morphology of malB-Encapsulated Nanoliposomes  
The main components used to produce drug-encapsulating nanoliposomes included malB compound at different concentrations, DPPC, mPEG-cholesterol, with or without cholesterol. The physicochemical characteristics of the nanoliposomes thus formed were assessed by determining the mean diameter (nm), polydispersity index (PDI), zeta potential, and encapsulation efficiency (EE).  
Based on the results shown in Table 2, all of the nanoliposomal formulations had not only a mean diameter \(<\)200 nm, but also PDI \(<\)0.3, and carried a low negative charge. However, the formulations without cholesterol as a component showed significantly higher packing efficiency (\(p<0.01\)). The EE of formulations A and B containing cholesterol were only about 10%, whereas the EEs of the cholesterol-free structures were \(>\)50%. The EE was also dependent on the malB concentration; a lower malB concentration of 0.25 mg was associated with higher EE (78.73%), whereas a higher concentration of 0.5 mg showed a lower EE (51.33%). Due to the highest efficient effect, structure C was selected for further studies.  
The morphology of the malB-nanoliposomal formulation C was examined by transmission electron microscopy (TEM) (Figure 1). The malB-nanoliposomes were small unilamellar vesicles, likely spherical in shape, and fairly homogeneous in size with an average diameter of 76.98 \(\pm\) 9.57 nm (Table 2). Based on morphological assessment, the mal B-nanoliposomal formulation C was suitable to manipulate in the further experiments for anticancer activities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types** & **Size (nm)** & **PDI** & **Zeta Potential (mV)** & **EE (\%)** \\ \hline Formulation A & 130.53 \(\pm\) 4.38 & 0.26 \(\pm\) 0.04 & \(-\)2.02 \(\pm\) 0.95 & 12.80 \(\pm\) 1.55 \\ Formulation B & 101.53 \(\pm\) 3.89 & 0.28 \(\pm\) 0.03 & \(-\)0.52 \(\pm\) 0.04 & 9.85 \(\pm\) 0.21 \\ Formulation C & 76.98 \(\pm\) 13.18 & 0.28 \(\pm\) 0.04 & \(-\)5.53 \(\pm\) 0.21 & 78.73 \({}^{**}\) \(\pm\) 9.52 \\ Formulation D & 124.41 \(\pm\) 35.41 & 0.30 \(\pm\) 0.03 & \(-\)2.70 \(\pm\) 0.13 & 51.33 \({}^{**}\) \(\pm\) 6.53 \\ Blank & 119.70 \(\pm\) 22.17 & 0.22 \(\pm\) 0.02 & \(-\)2.80 \(\pm\) 0.24 & - \\ \hline \hline \end{tabular} Note: \({}^{**}\)\(p<0.01\) compared to the formulation without cholesterol; blank liposome including DPPC, cholesterol, and mPEG-cholesterol.  
\end{table}
Table 2: Characteristics of obtained malB-loaded nanoliposomes.
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger
### Safety and Interactions
#### 2.2.3. Ginger May Lower Bioavailability of Some Substances  
The interaction of ginger with the immunosuppressiveant drug cyclosporine was studied in mice under different conditions of administration, leading to distinct results. The administration of ginger juice (5 mL/kg), both at the same time and two hours before oral administration of cyclosporine led to reduction in the bioavailability of this API, with reductions of 63% and 40% in the respective AUC [33]. Note that these results are in contrast with the claims in the patent of [PERSON] et al. [31], in which 30-35 mg/kg ginger extract in co-administration with cyclosporine is said to afford a bioavailability increase of 116% (measurements done in plasma over 24 h).  
The variability of the reported results allows postulating that high doses of ginger are required for it to feature an enhancing action on the absorption of APIs. In turn, an antagonistic action with other substances may occur when ginger is taken concomitantly in low doses, such as those arising from juices and infusions.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Coordination of Transportation Fleets of Autonomous Drivers
* From the driver or vehicle side, for a driver \(v\), the earnings depend on the price the client pays minus the cost of the vehicle for travelling the distance from the current position of \(v\) towards the position \(p_{o}(s)\) and later to the destination \(d(s)\): \[Earn(v,s)=Price(s)-\textit{vcost}\cdot(d(v,s)+d(s))\] where \(d(v,s)\) denotes the distance from the current position of the vehicle in the moment of assigning a service \(s\) to the origin point of that service, and _vcost_ is the actual cost rate of moving the vehicle on a per distance basis. _vcost_ will implicitly include petrol, maintenance, depreciation of the vehicle, etc. For simplicity, here we assumed that _fcost_, _fare_, and _vcost_ are the same for all services and vehicles, that is, in the price structure we do not distinguish between different vehicle costs nor between different requests. If the use of different cost factors is important, the proposed model could be adapted accordingly. Furthermore, part or all of the amount of _fcost_ could be retained by the mediator service as income. In this case, the earnings of the driver would be reduced by this amount. Like in the emergency medical case, a typical approach for assigning services to vehicles in such a system is the first-call/first-served (FCFS) rule, where each incoming request is assigned to the closest available driver in that moment and no reassignments are done. As shown in [59], if there are more unassigned requests than available vehicles, it is better to assign vehicles to service requests. We call this strategy nearest-vehicle/nearest-request (NVNR). Dynamic assignment strategies can improve the overall efficiency of a fleet. We assumed that drivers, once they are available, are obliged to accept a transportation service assigned to them. However, in contrast to the emergency medical scenario presented in the previous section, drivers do not have to accept changes in the assignments. That means they are free to accept or to decline proposed reassignments from the system, considering their own objectives and benefit. So, the dynamic reassignment approach, as proposed in Section 3.3.1, cannot be applied directly, as a driver would not be willing to accept a reassignment that reduces his/her net income. In order to still take advantage of cost reduction through reassignment, we developed an incentive schema so as to convince drivers to accept reassignments that are economically efficient from the global perspective of the system. The approach is detailed in the next subsection.
## 2 System Model
### Communication Model  
As shown in Figure 2, this study employs a communication model based on a MIMO system utilizing hybrid beamforming. On the transmission side, signals are weighted through baseband precoding and dynamically adjusted in direction using digital signal processing, followed by digital-to-analog conversion (DAC) to convert the signals into analog form. Subsequently, further directional adjustments are made via radio frequency (RF) precoding, and the beamformed signals are transmitted into space through an antenna array. On the reception side, the received signals from each antenna are combined using RF combined and converted into digital signals through analog-to-digital conversion (ADC). Subsequently, baseband combining is performed to eliminate noise and equalize the channel, thereby improving communication quality. Furthermore, by applying the Multiple Signal Classification (MUSIC) algorithm for direction of arrival (DOA) estimation, the arrival direction of received signals can be efficiently calculated, achieving high-precision beamforming. This approach, which integrates both digital and analog layers, enables the construction of a communication system that excels in flexibility and efficiency [12; 17].
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 3 Methodology
### Decoupling of Dynamics
#### 3.1.3 Neural Networks Training  
The D-class sedan model of CarSim is utilized to obtain the vehicle responses \(\left\{a_{x},a_{y},\ddot{\varphi},\dot{x},\dot{y},\dot{\varphi}\right\}\) under stochastic inputs \(\left\{F_{t},\delta,M_{z}\right\}\). To simulate the 4 WID vehicle, the total longitudinal force and yaw moment are converted to driven torques of each wheel based on Equations (2) and (4).  
\[T_{fl,fr}=\frac{(1-k_{r})F_{t}\mp M_{z}/d(1+\cos\delta)}{r}+I_{r}\frac{dw_{fl, fr}}{dt}, \tag{25}\]  
\[T_{rl,rr}=\frac{k_{r}F_{t}\mp M_{z}/d(1+\cos\delta)}{r}+I_{r}\frac{dw_{fl,fr}}{ dt}, \tag{26}\]  
where \(T_{fl}\), \(T_{fl}\), \(T_{fl}\), \(T_{fl}\) are the driven torques of the front-left, front-right, rear-left and rear-right tire, respectively. \(r\) is the radius of each wheel. \(I_{r}\) is the wheel inertia moment. \(w_{fl,fr}\) and \(w_{fl,fr}\) are the rotation velocities of each wheel.  
The longitudinal and lateral acceleration were constrained considering the ride comfort and anti-sideslip [33].  
\[-\eta_{d}g\leq a_{x}\leq\eta g, \tag{27}\]  
\[-\mu_{g}g\leq a_{y}\leq\mu_{g}g, \tag{28}\]where \(\eta_{d}\), \(\eta\) and \(\mu_{s}\) respectively represent the maximum coefficient of longitudinal deceleration, longitudinal acceleration and lateral acceleration. According to the vehicle dynamics and the kinematic model:  
\[a_{x}=\frac{F_{t}}{m}, \tag{29}\]  
\[R=\frac{L}{\sin\lvert\delta\rvert}\approx\frac{L}{\lvert\delta\rvert}, \tag{30}\]  
where \(R\) is the turning radius of the vehicle, and \(L\) is the distance between the front and rear axles. Then, substituting Equations (29) and (30) into Equations (27) and (28) yields:  
\[\lvert\delta\rvert\leq\frac{\mu_{s}\delta L}{u^{2}}, \tag{31}\]  
\[-\mu mg\leq F_{t}\leq\mu mg, \tag{32}\]  
The interval between two sampling points of longitudinal force, steering angle and additional yaw moment is 2 s. The values of sampling points are stochastically generated, obeying the uniform distribution in the range of \((-5000,5000)N\), \((-0.1,0.1)rad\) and \((-500,500)Nm\), respectively. The sampling data are interpolated by the Hermite method for smoothness, as shown in Figure 3.  
By setting different initial speeds, multiple groups of input signals act on the vehicle model, then the inputs and the responses of the vehicle are recorded as the training dataset. To ensure the validity of the collected data set, the following two constraints need to be guaranteed.  
* The equivalent steering angle is constrained according to the real-time speed.
* The vehicle speed is always positive.
## 4 Results and Analysis  
In order to validate the mathematical model, simulation results were compared to CFD computation, experiments, and results carried out by other authors. The following parameters are defined:  
\[\mathrm{C_{P}}\ =\ \frac{\mathrm{P}}{\frac{\mathrm{1}}{\mathrm{2}}\rho\mathrm{w}^{ 2}} \tag{84}\]  
\[\mathrm{c_{fy}}\ =\ \frac{\mathrm{f_{y}}}{\frac{\mathrm{1}}{\mathrm{2}}\rho \mathrm{w}^{2}} \tag{85}\]  
\[\mathrm{c_{fz}}\ =\ \frac{\mathrm{f_{z}}}{\frac{\mathrm{1}}{\mathrm{2}}\rho \mathrm{w}^{2}} \tag{86}\]  
\[\mathrm{c_{mx}}\ =\ \frac{\mathrm{m_{x}}}{\frac{\mathrm{1}}{\mathrm{4}}\mathrm{b}^{ 2}\rho\mathrm{w}^{2}} \tag{87}\]  
\[\tau\ =\ \frac{\mathrm{wt}}{\frac{\mathrm{1}}{\mathrm{2}}\mathrm{b}} \tag{88}\]  
where \(\mathrm{c_{P}}\) is the pressure coefficient, \(\mathrm{c_{fy}}\) is the horizontal force coefficient, \(\mathrm{c_{fz}}\) is the vertical force coefficient, \(\mathrm{c_{mx}}\) is the roll moment coefficient, and \(\tau\) is the non-dimensional time.