esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints
## 5 Conclusions  
Aiming to satisfy the requirement of comprehensive performance optimization and control input reliability in ship sailings, a \(H_{\infty}\)-SFMPC approach is proposed in this paper. The FMRLS strategy based on AR motion is used to predict the roll angles in advance for compensating the delay dynamic, which facilitates the practical applications; the MPC strategy is introduced to the MIMO rudder-fin joint roll/yaw control system, making a convex optimization problem that can be solved online. The \(H_{\infty}\)-type cost function is employed for the performance optimization, which considers a prediction model based on the practical system with wave disturbances instead of the nominal one. As a result, the closed-loop stability and anti-interference capability are enhanced. The state feedback control gain for the rudder-fin joint system is obtained by solving the optimization problem subject to matrix inequalities, which can deal with the saturation limitation of the rudder and fin stabilizers explicitly. Simulations yield that the proposed approach renders outstanding control performance for both the system with and without parameter uncertainties. In other words, the method model has a certain level of robustness. Our future work will be improving the control method by shifting some of the computations offline and facilitating practical implementations.  
Conceptualization, Z.J. and S.L.; methodology, Z.J. and W.Y.; software, L.J.; validation, S.L. and W.C.; investigation, Z.J. and W.Y.; resources, writing-original draft preparation, Z.J.; writing-review and editing, Z.J. and L.J.; visualization, Z.J.; supervision, W.C. and W.Y. All authors have read and agreed to the published version of the manuscript  
This work was partially supported by the National Natural Science Foundation of China (61903158, 61973140) and the National Science Foundation of Jiangsu Province of China (BK20180595).  
All authors declare no conflict of interest.  
List of Acronyms  
**Acronym**  
**Full Name**  
\(H_{\infty}\)-SFMPC  
\(H_{\infty}\)-type state feedback model predictive control  
LQG  
linear quadratic gaussian  
MIMO  
Multiple Input Multiple Output  
MPC  
model predictive control  
RRD  
rudder reduction damping  
LMI  
linear matrix inequality  
SISO  
single-input single-output  
RLS  
recursive least square  
FMRLS  
fading memory recursive least squares  
AR  
Auto-Regression  
LQR  
linear quadratic regulator
## 4 Case Study--Numerical Experiments
### Inter-Facility Material Transfer Process
#### 4.1.2 Optimal Buffer Allocation
These findings accentuate the importance of considering the interplay between truck allocations, the composition of product types, and their corresponding buffer allocations when optimising material transfer operations. A holistic approach is essential to comprehensively assess and determine each unique scenario's most effective and efficient buffer allocation strategy.
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 4 Preliminary Design of an IoT-Aided Building Fire Evacuation Control System
### Information Needs  
Generally, two aspects of safety issues are the major concerns during the fire evacuation. At first, there is the need to ensure that occupants stay away from the thermal hazard caused by the fire. Enclosure building fires create a large volume of heat and toxic smoke inside the building, and the spread of these hazards causes a major threat to evacuees [54]. Thus, it is essential to monitor the indoor fire environment and know where the hazardous areas are in a fire. In addition, fire safety equipment, such as firewater systems and fire doors, is the active design to stop the fire spread. The condition of these facilities in fires plays an important role in building evacuation. Therefore, information of the state of various fire safety equipment should also be monitored.  
Furthermore, in terms of occupants' situations during the evacuation process, chaos and disorder are also a concern [55]. Tall buildings encompass a large number of occupants working and living together. In fire emergencies, the convergence of the crowd in the stairwells may cause mass blockage and panic, so it is rather challenging to evacuate all these occupants simultaneously. Phased evacuation control has been proven more suitable for tall building fire evacuation [56]. The phased evacuation strategy conducts a controlled, area-by-area evacuation to decrease the queuing time and density of people. Still, its effectiveness must be strongly built on the timely information feedback of the overall evacuation progress [57]. Therefore, real-time occupant count data of different building areas should be collected to optimize the evacuation strategy and assist the building evacuation.  
Finally, evacuation signals and the implemented evacuation strategy need to be delivered to occupants in time in a fire emergency. The existing fire emergency systems, such as fire alarm systems, fire emergency lighting systems, and PA systems serve this purpose. Therefore, it is necessary to monitor the state of these systems to avoid a malfunction in emergencies. Moreover, due to the importance of providing evacuation guidance in tall building fire evacuation, smart devices that can promptly guide the occupants to escape should be used. The most direct way to deliver clear evacuation guidance in tall buildings is through voice and visual indicators. Therefore, remote control and operation of the PA system and the \"smart exit signs\" need to be built.  
According to the analysis above, the overall information needed for building fire evacuation control is listed in Table 2.
## 5 Conclusions and Future Work
* We assumed high-quality V2I/V2V communication with 5G networks, but the communication load, delay, and packet loss cannot be ignored under actual traffic conditions. Thus, we need to conduct a comprehensive simulation under the Veins framework, which combines the event-based network simulator OMNeT++ and the microscopic road traffic simulator SUMO (Simulation of Urban Mobility), to validate the proposed method in the future. Conceptualization, Z.D. ; formal analysis, Q.H. ; funding acquisition, Y.S. ; investigation, L.L. ; methodology, Z.D. and Y.S. ; project administration, W.S. ; resources, W.S. ; software, Z.D. ; supervision, W.S. ; validation, Y.S. ; visualization, Q.H. ; writing--original draft, Z.D. ; writing--review and editing, Y.S., Q.H., L.L. and W.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (2018 YFE0197700). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The authors declare no conflict of interest.
## 3 Performance Optimization Using Genetic Algorithm
### Definition of the Optimization Problem  
In vibration isolation, minimizing the total vibrational energy of the control object in a working frequency band is the optimization goal. By selecting optimal design parameters, the total vibrational energy will reach the minimum, and the optimal distribution of the vibration transmissibility \(T\) in a given frequency band can be realized. In this paper, the motion of the control object \(m_{u}\) is described by three generalized coordinates \(z_{u},a,\beta\). Thus, the total vibration energy from frequency \(f_{s}\) to \(f_{e}\) can be defined as  
\[G=\int_{f_{s}}^{f_{e}}\bigg{(}\big{|}\tilde{z}_{u}\big{|}^{2}+\big{|}\tilde{ \alpha}\big{|}^{2}+\big{|}\tilde{\beta}\big{|}^{2}\bigg{)}df \tag{13}\]  
where \(f_{s}=2\) Hz is the sweep start frequency and \(f_{e}=12\) Hz is the stop frequency; the envelopes are used as the amplitude of the vibration acceleration \(\tilde{z}_{u},\tilde{\alpha},\tilde{\beta}\). A lower \(G\) indicates better isolation performance. To simulate the actual complex base excitation, three typical basic excitation types are selected for the optimization research:Excitation 1: \(\phi_{01}=\phi_{02}=\phi_{03}=\phi_{04}=0\) (translation excitation along the \(z\) axis);  
Excitation 2: \(\phi_{01}=\phi_{02}=0,\;\phi_{03}=\phi_{04}=\pi\) (rotation excitation around the \(y\) axis);  
Excitation 3: \(\phi_{01}=\phi_{04}=0,\;\phi_{02}=\phi_{03}=\pi\) (rotation excitation around the \(x\) axis).  
The design parameters contain three structural parameters of the bio-inspired isolator: the connecting rod length \(l_{d}\), the transverse spring stiffness \(k_{d}\), the initial assembly angle \(\varphi_{d0}\), and two placement position parameters \(a,b\). Other structural parameters are shown in Table 1.  
Based on the above description, consider the fitness function \(F(\mathbf{p})=1/G(\mathbf{p})\), which represents the inverse of the total vibration energy generated by given parameter vector \(\mathbf{p}\). The vector \(\mathbf{p}\) contains all the design parameters, i.e., \(\mathbf{p}=\begin{bmatrix}l_{d}&k_{d}&\varphi_{d0}&a&b\end{bmatrix}\). Thus, an objective function is defined as  
\[\max F(\mathbf{p})\text{ when }\mathbf{p_{l}}<\mathbf{p}<\mathbf{p_{u}} \tag{14}\]  
where \(\mathbf{p_{l}}\) and \(\mathbf{p_{u}}\) are the upper and lower limits of the design parameter vector. The optimization goal is to find the maximum \(F\) subjected to \(\mathbf{p_{l}}<\mathbf{p}<\mathbf{p_{u}}\) under the above three base excitations, respectively.
## 3 Experimental Results
### Xrd  
The XRD test results for Day 1 are shown in Figure 7a. We can see that the main products of the reaction are ettringite (Aft), calcium hydroxide (CH), hemicapoluminate  
Figure 4: (**a**) Compressive strength results for Qu0, Qu7.5, and Qu15 at different ages; (**b**) compressive strength and heat relation.  
Figure 5: (**a**) Ultrasonic pulse velocity of Qu0, Qu7.5, and Qu15; (**b**) the relationship between compressive strength and UPV.  
(Hc), and monocarboaluminate (Mc). We can also see calcium carbonate peaks [33]; this is because the cement we used contains a small amount of calcium carbonate. In other words, what we used was not pure Portland cement in the traditional sense. In addition, Hc and Mc formed because calcium carbonate reacts with the aluminum phase in cement to generate Hc and Mc [38]. It also contains unreacted C3S, which mainly comes from cement that has not undergone a hydration reaction. In XRD testing, one crystal may correspond to multiple peaks. The peaks of alite and calcite overlap at about 30 degrees, so one peak corresponds to two substances.  
The 28-day XRD test results are shown in Figure 7b. By comparing Figure 7a,b, we can see that the types of reaction products are similar, but from 1 day to 28 days, the AFt content becomes less obvious, which is due to the transformation of AFt into AFm. In addition, from 1 day to 28 days, the Mc content becomes obvious because, compared with Hc, Mc has a better chemical stability. Over time, part of Hc is transformed into Mc [20; 36]. In addition, we can see that the strength of the C3S peak decreases from 1 day to 28 days. This is because the  
Figure 6: (**a**) Resistivity test results for Qu0, Qu7.5, and Qu15; (**b**) resistivity and strength relationship.  
Figure 7: XRD patterns forQu0, Qu7.5, and Qu15: (**a**) 1 day; (**b**) 28 days.  
hydration reaction of C\({}_{3}\)S occurs, and the C\({}_{3}\)S content in the solid product decreases. From 1 day to 28 days, the strength of the quartz peaks basically does not change. This shows that quartz is a chemically inert filler and does not chemically react with cement components.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 2 Overall UAV Design
### Fixed-Wing UAV Dynamic Modeling
\begin{cases}f_{y}\approx\frac{1}{2}\rho V_{a}^{2}S\bigg{[}C_{Y_{O}}+C_{Y_{g}} \varphi+C_{Y_{p}}\frac{b}{2V_{a}}R+C_{Y_{q}}\frac{b}{2V_{a}}r+C_{Y_{q}}\delta_ {e}\bigg{]}\\ k\approx\frac{1}{2}\rho V_{a}^{2}Sb\bigg{[}C_{lo}+C_{Ie}\varphi+C_{Ie}\frac{b} {2V_{a}}R+C_{Ie}\frac{b}{2V_{a}}r+C_{Ie}\delta_{e}\bigg{]}\\ n\approx\frac{1}{2}\rho V_{a}^{2}Sb\bigg{[}C_{no}+C_{n_{B}}\varphi+C_{n_{B}} \varphi+C_{n_{B}}\frac{b}{2V_{a}}R+C_{n_{B}}\frac{b}{2V_{a}}r+C_{n_{Hg}}\delta_ {e}\bigg{]}\end{cases}  \tag{11}\]\[ \begin{cases}f_{y}\approx\frac{1}{2}\rho V_{a}^{2}S\Big{[}C_{Y_{O}}+C_{Y_{g}}\varphi+ C_{Y_{g}}\frac{b}{2V_{a}}\gamma+C_{Y_{g}}\frac{b}{2V_{a}}r+C_{Y_{g}}\delta_{r} \Big{]}\\ k\approx\frac{1}{2}\rho V_{a}^{2}Sb\Big{[}C_{Io}+C_{I_{g}}\varphi+C_{I_{g}}\frac{b }{2V_{a}}\gamma+C_{I_{s}}\frac{b}{2V_{a}}r+C_{I_{g}}\delta_{r}\Big{]}\\ n\approx\frac{1}{2}\rho V_{a}^{2}Sb\Big{[}C_{no}+C_{n_{g}}\varphi+C_{n_{g}} \frac{b}{2V_{a}}\gamma+C_{n_{g}}\frac{b}{2V_{a}}r+C_{n_{g}}\delta_{r}\Big{]} \end{cases}  \tag{12}\]  
where \(\varphi\) is the side slip angle, \(R\) is the roll angle, \(r\) is the yaw rate, \(\delta_{r}\) is the rud-ders, \(f_{y}\) is the lateral force, \(k\) is the rolling moment, \(n\) is the yaw moment, and \(C_{*}\) is the aerodynamic coefficient. The thrust produced by the propeller is  
\[f_{p}=\frac{1}{2}\rho S_{\text{prop}}\ C_{\text{prop}} \begin{pmatrix}\left(p_{ \text{motor}}\ \delta_{t}\right)^{2}-V_{a}^{2}\\ 0\\ 0\end{pmatrix}  \tag{13}\]  
where \(p_{motor}\) is the transmission efficiency coefficient of the motor, and the propeller sweep area is \(S_{\text{prop}}\). The atmospheric disturbance can be expressed as follows:  
\[V_{w}= \begin{pmatrix}w_{o}\\ w_{e}\\ w_{c}\end{pmatrix} V_{w_{s}}^{i}= \begin{pmatrix}\ w_{o_{s}}\\ w_{e_{s}}\\ w_{e_{s}}\end{pmatrix} V_{w_{s}}^{b}= \begin{pmatrix}\ y_{w_{g}}\\ \ v_{w_{g}}\\ w_{w_{g}}\end{pmatrix}  \tag{14}\]  
where \(V_{w_{s}}\) is the value of the airflow velocity in a stable environment; \(v_{w_{g}}\) is the airflow disturbance value; \(w_{o_{s}}\), \(w_{e_{s}}\) are the steady-state airflow velocity values pointing north, east, and to the center of the Earth, respectively; and \(y_{w_{g}},v_{w_{g}},w_{w_{g}}\) are the velocity components of the turbulent airflow pointing north, east, and geocentric. According to the kinetic equation in this study [45], a robust controller is designed, and the control rate formula is as follows:  
\[u=\ddot{y}_{d}-h_{1}e_{1}-h_{0}e_{0}-\hat{d}-k_{d}\dot{x}_{2} \tag{15}\]  
In this formula, the robust control rate is mainly composed of three parts: \(\left(\ddot{y}_{d}-h_{1}e_{1}-h_{0}e_{0}\right)\), a proportional controller; \(\hat{d}\), the observers; and \(k_{d}\dot{x}_{2}\), the forecasts. The parameters of the control rate depend on the order of the system; for example, the roll control system is a second-order system, and parameters \(h_{1},h_{0}\) guarantee that the spectrum of the polynomial, \(h(s)=h_{0}=h_{1}s\), is distributed in the left half-plane; \(k_{d}\) is nonnegative constant, \(e_{0}=y-y_{d}\).
## 2 Prerequisites and Problem Statement  
While our considerations are quite general, meaning that the planar paths planned with our approach can be applied to various systems and planning tasks, let us consider a rear-driven carlike-vehicle kinematics as an illustratory example used throughout this paper. This is shown in Figure 1. Using results from Reference [4], one can model this system by decomposition into unicycle vehicle-body kinematics \(\dot{\mathbf{q}}=\mathbf{G}(\mathbf{\theta})\mathbf{v}\) and steering dynamics \(\dot{\beta}=u_{1}\) as follows:  
\[\dot{\mathbf{q}} =\begin{bmatrix}1&0\\ 0&\cos\theta\\ 0&\sin\theta\end{bmatrix}\begin{bmatrix}v_{1}\\ v_{2}\end{bmatrix}=\mathbf{G}(\mathbf{\tilde{q}})\mathbf{v}, \tag{1}\] \[\dot{\beta} =u_{1},\quad\mathbf{v}=[v_{1}\,v_{2}]\triangleq\begin{bmatrix}\frac {u_{2}}{L}\tan\beta&u_{2}\end{bmatrix}^{\top}\in\mathbb{R}^{2},\] (2) \[\mathbf{u} =[u_{1}\,u_{2}]^{\top}\in\mathbb{R}^{2},\]  
where \(\mathbf{q}=[\beta\;\theta\;x\;y]^{\top}=[\beta\;\mathbf{\tilde{q}}^{\top}]^{\top}=[ \beta\;\theta\;\mathbf{\tilde{q}}^{\top}]^{\top}\in\mathcal{Q}=[-\beta_{m},\beta _{m}]\times\mathbb{R}\times\mathcal{P}\) denotes a configuration vector with \(\beta_{m}<\pi/2\) being a steering angle limit, \(\mathcal{P}\subseteq\mathbb{R}^{2}\) denotes a position space, \(\mathbf{v}\) corresponds to vehicle-body kinematics-control input, with \(v_{1}\) being vehicle-body angular velocity, and \(v_{2}\) corresponding to longitudinal velocity of the guidance point \(\mathbf{\tilde{q}}=[x\;y]^{\top}\) illustrated in Figure 1, while \(\mathbf{u}\) is the control input of carlike kinematics comprising steering angle rate \(u_{1}\) and longitudinal velocity \(u_{2}\equiv v_{2}\). As a result of limited steering angle \(\beta\in[-\beta_{m},\beta_{m}]\), the following constraint is present:  
\[|\kappa|\leq\frac{1}{L}\tan\beta_{m}=:\kappa_{B}, \tag{3}\]  
where  
\[\kappa\triangleq\frac{v_{1}}{v_{2}}=\frac{1}{L}\tan\beta \tag{4}\]  
is the motion curvature of the robot, whereas \(L\) denotes the distance between the rear and the front axle (see Figure 1).  
Let us assume that control input \(\mathbf{u}\) is continuous, which is often desirable in practical applications. According to Equation (2), this implies that \(\dot{\beta}\) is continuous. Since \(\dot{\beta}\) is related to curvature \(\kappa(t)\) by Equation (4), one concludes that admissible trajectories of the carlike kinematics must have curvature \(\kappa(t)\) of class \(\mathbb{C}^{1}\) (with a continuous time derivative), which satisfies Equation (3). As a consequence of such requirements, admissible paths for the carlike kinematics must be \(\mathbb{G}^{3}\)-continuous, that is, for a path \(\dot{\mathbf{q}}_{d}(s)=[x_{d}(s)\,y_{d}(s)]^{\top}\), its curvature \(\kappa_{d}(s)\) must be at least of class \(\mathbb{C}^{1}\). In this paper we
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 4 Results and Discussion
### Theoretical Impact of Buoy Movements on the FLS Horizontal Velocity Estimates
#### 4.2.2 Tilt Motion
In the case of an isolated tilt \(T\), the most influential effect on the horizontal wind measurement is the variation of the angle \(\varphi\) on the beams perpendicular to the rotation axis (beams A-C).
# An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation
## 2 Data and Methodology
### Data Augmentation  
The GAN architecture, first proposed in [34], is a framework for generative models through adversarial training, as illustrated in Figure 2. It consists of a pair of models known as the generator (G) and the discriminator (D). Both networks are fully connected neural networks. The generator creates fake images (which do not exist in the original training set) using noise input, while the discriminator tries to distinguish these fake images from real images in the original training set. The entire framework resembles a two-player minimax game, where the generator aims to minimize its objective function, and the discriminator seeks to maximize its objective function. Consequently, G learns to generate images that D evaluates as real, while D learns to accurately distinguish between fake and real images. By alternating these processes, G generates images that closely resemble real ones.
###### Abstract  
In this work, a novel rotation approach for the antenna elements of a linear phased array is presented. The proposed method improves by up to 14 dB the cross-polarization level within the main beam by performing a sequential 90 deg rotation of the identical array elements, and achieving measured cross-polarization suppressions of 40 dB. This configuration is validated by means of simulation and measurements of a manufactured linear array of five dual-polarized cavity-box aperture coupled stacked patch antennas operating in L-Band, and considering both uniform amplitude and phase distribution and beamforming with amplitude tapering. The analysis is further extended by applying and comparing the proposed design with the 180 deg rotation and non-rotation topologies. This technique is expected to be used for the next generation L-Band Airborne Synthetic Aperture Radar Sensor of the German Aerospace Center (DLR).  
cross-polarization improvement; dual polarized aperture coupled stacked patch antenna; sequential element rotation; linear array; airborne SAR system Article  
Sequential 90 deg Rotation of Dual-Polarized Antenna Elements in Linear Phased Arrays with Improved Cross-Polarization Level for Airborne Synthetic Aperture Radar Applications  
17 March 2021  
Accepted: 2 April 2021  
Submitted: 8 April 2021
## 3 Results and Discussion
### Deficit Irrigation Effect on the Morphological and Physico-Chemical Characteristics of Tomato  
The primary factors that have an impact in the quality of tomatoes are the morphology, color, total soluble solids (TSS) and titratable acidity (TA) [23]. On the other hand, the functional quality is mainly given by the lycopene, carotenoids, polyphenols, and antioxidant activity, among others. While the sensory quality it is predominantly given by the color, sugars, organic acids, and the volatile compounds [24]. All these parameters are influenced by the cultivar, development stage, agronomic and environmental conditions [25]. Figure 1: Climatic conditions in the greenhouse. (**a**): Maximum (brown) and minimum (yellow) temperature; (**b**): Evapotranspiration (ETo) (blue) and vapor pressure deficit (VPD) (grey). Even though cumulated yield was not different, the individual tomatoes weight was significantly higher in RDI treatments for 1 st and 2 nd week of deficit irrigation and became similar to the control in the following weeks (Table 2). The same phenomenon was also observed in size for the second and third week, while similar values between treatments were registered for the other weeks. Authors working with DI in tomatoes concluded that fresh weight of larger fruits tended to be more negatively affected by the water deficit compared to smaller fruits [26; 27]. Apparently, due to the lower osmotic and water potential of smaller fruits could compete more effectively for water in response to a reduced water supply. Other authors reported that weight was most likely to be influenced by grafted technique than water supply [28]. As observed, the cultivar \"Summerbrix\" was characterized by a high variability of weight from an initial mean value of 18.3 g (week 1) down to 8.3 g in the last week 6. This tendency was also reported by other authors in the same cultivar but also for others such as \"Lazarino\" in which weight variability depending on the harvest position of the cluster part was detected [29]. For instance, tomatoes with higher weights were found in the proximal part of the cluster, and a decrease was reported when the tomatoes were harvested from the medium and distal part of the cluster. The position factor also could influence the size and weight although in the present study all fruits, both from control and RDI treatments, were harvested from the whole spotted plants.