esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
###### Abstract
To address the above challenges, by carefully analyzing the launching process of UAVs with large wingspans, we observed that it is similar to the launch and control of small unpowered parachute gliders. For example, the \"parachute-dive-horizontal flight\" process of small unpowered parachute gliders takes parachute cut-off times as the start time of the automatic control and defined the working process as four initial states: parachute release, wing deployment, initial horizontal flight, and cruise flight [4]. After the parachute glider falls vertically, the pull-up control is applied to change its trajectory into horizontal flight [4; 5]. Based on these observations and the analysis, our key idea is to build a complex control model based on UAVs with large wingspans and to apply the launching strategy of an unpowered parachute glider to balloon-borne-based UAVs with large wingspans. To this end, in this paper, we designed an improved adaptive controller for UAVs with large wingspans to tackle challenges that exist in the launch flight test of balloon-borne-based solar-powered UAVs. More specifically, first, we analyze the flight control of the pull-up process for the large-scale balloon-borne-based aircraft. Then, we establish a flight dynamic model considering elastic deformation. This helps the design of the launching controller. Finally, we propose an adaptive aircraft pull-up control algorithm and design an improved adaptive controller dedicated for the launch control of balloon-borne-based solar-powered UAVs. We perform a simulation test to evaluate the effectiveness of the proposed algorithm and a physical test to assess the pull-up control design for balloon-borne-based aircraft with large wingspan. To conclude, the main contributions can be summarized as follows:  
* This paper propose an adaptive control model to tackle the pull-up control of balloon-borne-based solar-powered UAVs with large wingspans, which is a tough problem faced by these UAVs. * This paper introduces a series of factors that influence the elastic deformation of fuselages in solar-powered UAVs to approximate their real-world flight dynamic model and proposes a stage-based pull-up control strategy. * This paper verifies the effectiveness of the adaptive control model on pull-up processes with the simulation experiment and demonstrates its superiority over the latest control model utilized in physical experiments. The rest of the paper is organized as follows. Section 2 illustrates background information on solar-powered UAVs and related studies. Section 3 analyzes important factors in pull-up control processes. In Section 4, a flight dynamic model of balloon-borne unmanned aerial vehicles considering elastic deformations and the influence of a low Reynolds number is established. Section 5 presents the UAV launch and pull-up control algorithm. Section 6 evaluates the proposed control algorithm by conduciting a simulation experiment and comparison. In Section 7, we discuss limitations and future work. Finally, the conclusion is presented in Section 8.
## 2 Material and Methods
### Failure Modes Assessment  
Each specimen was visualized with a digital microscope (KH-7700, Hirox, Tokyo, Japan) under 50\(\times\) magnification, and photographs were obtained after dislodgement of the root canal filling material. The photographs were separately evaluated by two blinded operators, and the mode of failure was recorded. Failure modes were classified into one of the following three categories according to [PERSON] et al. [22]: Class I: adhesive failure, characterized by less than 25% of the dentin surface being covered by sealer; Class II: cohesive failure, indicated when more than 75% of the dentin surface was covered by sealer; Class III: mixed failure, when the dentin surface covered by sealer was between 25% and 75%.
# Gamification in IT Service Management: A Systematic Mapping Study
## 4 Results
### RQ3. How Has Gamification Been Implemented in ITSM Processes?
#### 4.4.1 RQ3.1. Which Methods or Frameworks Have Been Used to Gamify ITSM Processes?
The authors suggest interviewing support group agents, and to observe and measure their actual behaviors to identify their needs and motivations, and to determine their behavior in the process. After that, engagement loops will be defined to conduct the actions of the support group agents through the process. The main objective of this step is that support group agents manage adequately the incidents to fulfill the agreed service level agreements. Finally, to make the job of support group agents more enjoyable, the most adequate game elements will be selected and implemented in an incident management tool. To illustrate the utility and applicability of the gamification method proposed, [P08] presented an example of use to gamify the IT service incident management process in a hypothetical company. As can be seen, the results of our study show that most studies implemented gamification in the area of ITSM processes by adopting some gamification ideas learned from literature or experimenting with game elements. Only three studies presented structured methods to design and implement adequate gamification strategies. This indicates the absence of a standardized method that details the concrete actions and steps to be carried out for gamifying ITSM processes.
# Interaction of Segmental Tunnel Liniings and Dip-Slip Faults--Tabriz Subway Tunnels  
[PERSON]  
1 Department of Mining, Faculty of Engineering, Urmia University, Urmia 5756151818, Iran; [EMAIL_ADDRESS] (A.R.); [EMAIL_ADDRESS] (M.H.)  
[PERSON]  
2 Department of Civil Engineering, University of Minho, Campus de Azurem, 4800-058 Guimaraes, Portugal; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Mining, Faculty of Engineering, Urmia University, Urmia 5756151818, Iran; [EMAIL_ADDRESS] (A.R.); [EMAIL_ADDRESS] (M.H.)  
[PERSON]  
4 Laboratory 3 SR, Department of Civil Engineering, Polytech Grenoble, Grenoble Alpes University, 38400 Grenoble, France 44 University of Sheffield  
Active faults are the most important geological features when designs in seismic regions must be done, as there is always a relationship between the main region faults and the earthquakes. When structures are built near active faults, procedures are recommended [10; 11]. Despite this, many cities, such as Tabriz in Iran, are situated within bedrock fault zones. Consequently, it may not always be possible to prevent construction near these faults. This phenomenon causes serious problems during tunnel construction. It is therefore necessary to conduct reliable design studies before constructing a tunnel intersection with an active fault. It is also recommended that suitable tunnel lining be used in order to minimize damage [2]. When dealing with mechanized shield tunneling, a segmental structure is installed by a tunnel boring machine (TBM). The segmental structure is composed of several reinforced concrete segments and joints (Figure 1). Using this structure, there is an increase in the degree of freedom of the tunnel lining both longitudinally and transversely. Earthquake-induced damage will then be concentrated in the flexible joints or limited to a portion of the segments. The result is that the damage to the entire lining can be reduced by this segmental structure. The basic question is whether the resistive capacity of the segmented lining is stable against the relative motion of the fault and what its difference is compared to the continuous lining.  
This paper examines the performance of segmental tunnel structures affected by a dip-slip bedrock fault rupture using advanced three-dimensional numerical simulations. The Tabriz Subway Line 2 was used as a reference case. The tunnel route crosses a bedrock fault known as the Baghmisheh Fault. The developed numerical codes were validated with centrifuge tests performed by one of the co-authors [2; 12]. In the next step, we investigated the interaction between the segmental tunnel structure and the capable fault rupture in the Tabriz Subway. Compared to similar studies, this research is distinguished by the advanced modeling of segment joints (oblique and straight joints) in conjunction with their interactions with soil mass. Furthermore, an advanced constitutive model was assigned to sandy soils, which had not previously been applied.
## 2 Theory of UWA Communication Using CCVTRM
### Principles of CCVTRM
The final data stream is the convolution of \(s_{r}(t)\) with the time-reversed composite channel estimation \(h^{\prime}(-t)\), which can be expressed as:  
\[r(t) =s_{r}(t)*h^{\prime}(-t) \tag{8}\] \[=s(t)*h(t)*h^{\prime}(-t)+\sum\limits_{i=1}^{M}n_{si}(t)*h^{\prime }(-t)\] \[=s(t)*\hat{h}(t)+\sum\limits_{i=1}^{M}n_{si}(t)*h^{\prime}(-t)\]  
where:  
\[s_{r}(t) =\sum\limits_{i=1}^{M}s_{i}(t) \tag{5}\]0  
\(\hat{h}(t)\) called virtual channel, considered to be the exclusive channel the data stream propagates through, is the correlation of the composite channel \(h(t)\) and its estimation \(h^{\prime}(t)\). If the estimation \(h^{\prime}(t)\) is approximated as the composite channel \(h(t)\), the virtual channel \(\hat{h}(t)\) will tend to be a delta function with a high correlation peak. Moreover, as the complexity of \(h(t)\) increases, the virtual channel \(\hat{h}(t)\) is closer to a delta function. Ideally, the virtual channel \(\hat{h}(t)\) can be thought of as a simple channel only with a single path. That is, CCVTRM can refocus the energy of the dispersed signal caused by multipath. To calculate the output SNR gain of CCVTRM, given that the SNR of each path signal is equal and noises are uncorrelated in the array elements, we can obtain:  
\[s_{r}(t) =\sum\limits_{i=1}^{M}s_{i}(t) \tag{5}\]1 \[s_{r}(t) =\sum\limits_{i=1}^{M}s_{i}(t) \tag{5}\]2\[s_{r}(t) =\sum\limits_{i=1}^{M}s_{i}(t) \tag{5}\]3  
\[s_{r}(t) =\sum\limits_{i=1}^{M}s_{i}(t) \tag{5}\]4  
where \(n_{k}(t)\) denotes the noise of the number \(k\) path and \(\sigma_{k}^{2}\) denotes the noise variance, and '\(E[\cdot]\)' denotes expected value. After going through CCVTRM, the final data stream can be rewritten as:  
\[s_{r}(t) =\sum\limits_{i=1}^{M}s_{i}(t) \tag{5}\]5  
The output SNR is:  
\[s_{r}(t) =\sum\limits_{i=1}^{M}s_{i}(t) \tag{5}\]6  
Combining Equations (12) and (14), we can obtain:  
\[\text{SNR}_{2}=\frac{A_{2}^{2}}{\sigma_{1}^{2}}\left[1+\left(\sum\limits_{k= 1}^{N}\sum\limits_{ \begin{array}{c}l=1\\ l\
eq k\end{array} }^{N}A_{k}A_{l}\right)/\left(\sum\limits_{k=1}^{N}A_{k}^{2} \right)\right] \tag{15}\]  
Thus, the SNR gain of CCVTRM is:  
\[G_{VTRM}=10\log_{10}\left[1+\left(\sum\limits_{k=1}^{N}\sum\limits_{ \begin{array}{c}l=1\\ l\
eq k\end{array} }^{N}A_{k}A_{l}\right)/\left(\sum\limits_{k=1}^{N}A_{k}^{2} \right)\right] \tag{16}\]  
As we can see in Equation (16), the more complex the multipath, the higher SNR gain of CCVTRM we can obtain. In a word, CCVTRM for the VSA processing not only has a simple calculation, but also gets the spatial gain from the VSA and the temporal focusing gain from TRM, moreover, it can mitigate the ISI caused by multipath.
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 2 Wind Energy Conversion System Description  
### Wind Turbine Model  
The mechanical power generated by a wind turbine is [20]:  
\[P_{w}=\rho\pi R^{2}C_{p}(\lambda,\beta)v^{3}/2, \tag{1}\]  
where \(\rho\) is air density, \(R\) is turbine blade radius, \(v\) is wind speed and \(C_{p}\) is power coefficient which is a function of the tip speed ratio (TSR) \(\lambda\) and blade pitch angle \(\beta\). \(\lambda\) is defined as:  
\[\lambda=\omega_{o}R/v, \tag{2}\]  
where \(\omega_{o}\) indicates the rotational speed in rad/s.  
The output torque of the wind turbine is:  
\[T=\rho\pi R^{3}C_{p}(\lambda,\beta)v^{2}/2\lambda \tag{3}\]  
According to Equation (2), the rotor speed can be adjusted to keep \(\lambda\) at the optimum value \(\lambda_{opt}\) and then maximize the power coefficient as \(C_{pmax}\). The wind turbine aerodynamic efficiency \(C_{p}\) (\(\lambda\), \(\beta\)) is given by the following Equation [21]:  
\[\left\{\begin{array}{l}C_{p}(\lambda,\beta)=0.5176(\frac{116}{\alpha}-0.4 \beta-5)e^{-21/\alpha}+0.0068\lambda\\ \frac{1}{\alpha}=\frac{1}{\lambda+0.08\beta}-\frac{0.035}{\beta^{3}+1}\end{array}\right. \tag{4}\]  
where \(\alpha\) is the intermediate variable to obtain the \(C_{p}\) by using \(\lambda\) and \(\beta\). The curves of the power coefficient \(C_{p}\) and the TSR \(\lambda\) with different blade pitch angles are shown in Figure 1. It can be seen that the maximum power coefficient is decreasing by increasing the pitch angle. Therefore, the output power of the wind turbine can be limited to the rated value through the variable pitch angle control.  
According to (1) and (2), the maximum power output of the wind turbine is obtained as:  
\[P_{wopt}=\rho\pi R^{5}C_{pmax}\omega^{3}/2\lambda_{opt}^{3}=k_{opt}\omega^{3}. \tag{5}\]  
Then the optimum torque is expressed as  
\[T_{opt}=\rho\pi R^{5}C_{pmax}\omega^{2}/2\lambda_{opt}^{3}=k_{opt}\omega^{2}, \tag{6}\]  
where \(k_{opt}\) is the coefficient associated with the wind turbine characteristics.
# Scale-Model Experiment on the Delayed Failure Mechanism of Deep Tunnels in Brittle Rock
## 4 Scale-Model Testing of Deep Tunnel
### Deep Rock Scale-Model Testing Apparatus  
DRSTA is designed particularly for deep engineering research. It is composed of a reaction frame, hydraulic unit, control unit, and pressure chamber (Figure 2a). The hydraulic unit is powered by three engines, where each of them control the hydraulic cylinders on one side (lateral, horizontal, and axial, respectively). Thus, triaxial pressure could be applied to the chamber. The insides of the pressure chamber are shown in Figure 2b. Multiple technical details in DRSTA were considered for simulating the deep underground environment:  
1. To obtain the high initial triaxial stress status of the model, we limited the size of the model chamber to 40 cm \(\times\) 40 cm \(\times\) 88 cm (lateral \(\times\) horizontal \(\times\) axial right), with this size each direction could apply 2 MPa of stress;
2. Disc springs (Figure 2b) were installed between the hydraulic cylinder and the loading plate to simulate the infinite boundary of the surrounding rocks;
3. Polyvinyl chloride layer was pasted onto the scaled model to reduce the friction between the model and chamber.  
The stiffness of disc springs should be of proper value according to the following equation:  
\[K=\frac{E}{(1+\
u)r} \tag{1}\]  
where \(E\) is the elastic model of the similar material, \(\
u\) is Poisson's ratio of the similar material, and \(r\) is the distance to the outline of the tunnel.  
An excavation of the tunnel in DRSTA was performed with a self-designed drilling machine; the schematic and picture of the drilling machine are shown in Figure 3. The cutter of the drilling machine is driven by a horizontal motor. The cutter rotated at a very slow speed, which made little disturbance during the excavation.  
Figure 2: Deep rock scale-model testing apparatus: (**a**) apparatus; (**b**) pressure chamber.
## 2 Structure Design and Numerical Method
### Numerical Model  
In this manuscript, a comparison between the numerical and experimental results is performed. The CFD solver used is StarCCM+ 2020 and the mesh is also plotted by StarCCM+ software as given in Figure 6. The lift calculation in this study is fulfilled by  
Figure 4: Kinematics of the FWR (not including the terms caused by the wings’ deformation).  
Figure 5: Lift-testing system for the FWR.  
solving the unsteady Reynolds average equation built in StarCCM+ software. To ensure the numerical stability of the calculation, implicit Euler time discretisation method is applied. It is noted that the deformation of these wings is not included in this model. The height of the first mesh cell off the wall is set as 0.1 mm, and 10-layer boundary layer mesh is plotted. The total number of the meshes is about 2 million, and polyhedral meshes are used in the CFD calculation. The whole fluid field can be mainly partitioned into two regions: the background mesh region and the overset mesh region. A user-defined function is programmed in Python language to make the overset mesh region fulfill the rotation motions of the wings. Moreover, the \(k-\omega\) SST turbulence model is applied as that used in previous study [28].  
For validating the CFD model in this paper, the lift and moment results by our CFD simulation are compared with those obtained by [PERSON] et al. [24]. The variation function of twisting angle (\(\theta\)), stroke angle (\(\phi\)), and rotary angle (\(\psi\)) are set the same as those in the literature. According to Figure 7, it is observed that, by using our CFD model, the \(\overline{C_{L}}\) equals to 0.88, and \(\overline{C_{m}}\) equals to 0.145, which is quite close to those given by [PERSON] et al. (\(\overline{C_{L}}\)= 0.92 and \(\overline{C_{m}}\)= 0.13). The difference should partly lie in the different mesh type and solver used. Generally, the above comparison illustrates that the CFD model in this paper is appropriate for the aerodynamic calculation of the FWR.  
Figure 6: CFD model of the flapping-wing rotor.  
Figure 7: Comparison of lift coefficients and rotary moment coefficients between the CFD calculation by StarCCM+ software and the previous computational results.  
To facilitate the understanding of the inertial force results tested by the experiment, an ADAMS model is also built up as shown in Figure 8. As depicted in Table 1 and Figure 3, the single wing assembly weighs 1.7 g and can be further decomposed into several parts, including the hot-melt adhesives (0.2 g), nylon stop blocks & angle limiter (0.2 g), main beam (0.5 g), wing membrane (0.4 g), and subsidiary beam (0.4 g). To keep consistency with the experiment, the wing membrane part should be removed from the ADAMS model upon calculating the inertial forces of the FWR encountered during its flapping process.
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 5 Summary of Results  
Based on the performed experiments, we can conclude the following:  
* Both approaches provide satisfying results as far as both high imperceptibility and data recovery accuracy are concerned.
* All presented types of transforms may be used to embed a watermark of high capacity.
* The DCT method works well only when using method 4 for data embedding, and then, it provides better visual quality and comparable accuracy of information recovery in comparison with the proposed transforms.
* The proposed orthogonal transforms provide satisfactory results for both PSNR and BER when using methods 2, 4, or 5 and for some cases also method 3.
* The DCT method cannot guarantee data recovery during the extraction process in case of methods 1, 2, 3, and 5.
* Spectrum analysis and adaptive watermark embedding guarantees that the proposed orthogonal transforms can provide promising results and may be found useful in many potential applications.
* Matrix parameterization is an interesting feature that might be used as a security mechanism for both data embedding and recovery.  
Since the proposed and DCT transforms have their unique nature, in order to compare these two approaches, the concrete combinations of watermark embedding methods and types of transforms have been selected.  
1. For proposed orthogonal transforms: method 3 and sparse exponential matrix of size 8 \(\times\) 8 pixels.
2. For DCT transform: method 4 and DCT matrix of size 8 \(\times\) 8 pixels. The comparison results are depicted in Figure 13.  
As can be seen, the proposed orthogonal transforms can even outperform the DCT approach when the visual perception is our main goal. Conversely, if the main target of the application is to guarantee full data recovery and some level of image distortion is acceptable (but still satisfying), one can apply a different type of the proposed transforms with another embedding scheme. This is especially noticeable in case of method 5 and all types of transforms. This means that depending on the task one wants to realize, an adequate procedure can be selected in order to provide expected results.  
Figure 12: Sample watermarked image obtained by using method 3 and sparse exponential matrix for the message length equal to 50,000 bits.
# Topology Optimisation in Structural Steel Design for Additive Manufacturing
## 4 Approaches, Methods, Criteria, and Software in Topology Optimisation  
### Approaches and Optimisation Methods  
#### On the Nomenclature Complexity  
An unexpected layer of complexity in TO lies in the terminology. Approaches, methods, methodologies, models, techniques, and algorithms, etc. are used in conflicting ways by an expanding and very diverse research and practice community.  
In this article, a choice has been made to refer to the comprehensive strategies for solving a well-defined TO problem as \"methods\". Such an option adheres to some of the most respected leaders and book-writers in disciplines, such as [PERSON], [PERSON], and [PERSON], to name a few, with some occasional exceptions. In this manner, the well-known SIMP, RAMP or ESO are considered methods, while others are grouped by similarity. That is the case of OMP and NOM, both nested under the \"Homogenisation\" methods family. Such an option is, evidently, arguable.  
Other options resided in considering six approaches, in which the defining criterion was the ability to group methods by its functional similarity. Thus, Density-Based, Level-Set, Topological Derivatives, Phase-Field, Heuristic and Hybrid approaches were accounted. As a result, other approach classifications, such as Material/Geometrical or Lagrangian/Eulerian, could not be simultaneously considered.  
The problem resulting from the possibility of employing different Optimality Criteria (OC) for one given method and possibly using the same Optimality Criterion for different methods could only be solved by creating a diverse group for OC.  
However, other problems arise from using different names for the same method or for different authors' applications of the same method. For example, one can consider the ESO method, also referred to as SERA by researchers who were not involved in its original proposition, regarding their views on the method shortcomings. Likewise, the Bubble Method is referred under different names, and recent developments in Level-Set Methods and Topological Derivatives have not yielded universally accepted names for its methods, due to several increments from many researchers and applications to many different problems.  
Contrariwise, several well-known methods have remarkable resemblances which could be better regarded as different applications of a single method. That can be found in the extended ESO family, in applying Genetic Algorithms (GA) and Swarm Methods, and in the different options for solving the Hamilton-Jacobi Equation in Level-Set Methods by only a handful of examples. An option was taken to leave all the methods that have some expression in the surveyed literature, regardless of its similitude, and group methods that differ only in using well-known algorithms, such as GA or Swarms.
## 1 Introduction
### Design Methods against Obsolescence
#### 1.3.1 Openness of a System  
Articles unanimously cite system openness or technology transparency as the most important factor in minimizing the risk for software obsolescence [10] (p. 29). Open systems are, according to [10] (p. 29), systems with interfaces, based on generally accepted standards. It is assumed that this hardware and software design methodology ensures that a module can be replaced if necessary. The technology including interfaces used inside the component are not relevant. Access to the source code is to be preferred [10] (p. 32).