esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 5 Motion Planning
### Trajectory Planning  
Initial Trajectory  
To execute a smooth motion towards the desired waypoint, we use a suitable time-optimal path planning by reachability analysis (TOPP-RA) trajectory planner [31]. The TOPP-RA algorithm searches for the time-optimal trajectory and is based on a \"bang-bang\" principle on the generalized torque of each DoF. The planner is capable of receiving the input waypoints of an arbitrary dimension and output a smooth trajectory. Each DoF has to be provided with dynamical constraints in terms of velocity and acceleration, which are respected during the trajectory generation process.  
As mentioned, the input to the TOPP-RA trajectory is the path of a set of \(n\geq 2\) waypoints:  
\[\mathcal{P}=\Big{\{}\mathbf{w}_{i}\mid\mathbf{w}_{i}\in\mathbb{R}^{13},i\in(0, 1,\dots,n)\Big{\}}. \tag{19}\]  
Based on the dynamical constraints, the output of the TOPP-RA planner is a sampled trajectory:  
\[\mathcal{T}_{\mathrm{s}}=\Big{\{}\mathbf{t}(kT_{\mathrm{s}})\mid\mathbf{t}(kT _{\mathrm{s}})\in\mathbb{R}^{3\times 13},k\in(0,\dots,n_{t})\Big{\}}, \tag{20}\]  
where \(\mathbf{t}=\Big{[}(\mathbf{w})^{T}\quad(\mathbf{\dot{w}})^{T}\quad(\mathbf{ \dot{w}})^{T}\Big{]}^{T}\in\mathbb{R}^{3\times 13}\) is a single sampled trajectory point consisting of position, velocity and acceleration; \(T_{\mathrm{s}}\) is the sampling time; and \(n_{t}\) is the number of points in the sampled trajectory. Note that each trajectory point contains both roll and pitch angles. Although these angles can be planned through the TOPP-RA algorithm,they are omitted at this point because of the underactuated nature of the multirotor UAV. Nevertheless, they are used later in the paper when the model corrections are applied.  
The impedance controller expects a step change in the force and weighing parameter \(\beta\) referent values. To satisfy this requirement, large constraints for velocity and acceleration are imposed for these DoFs. However, because other DoFs have constraints below their physical limit, the overall force and \(\beta\) trajectory has a slower, dynamically smooth profile. These profiles also have overshoots and undershoots which are not acceptable because they are not within the hard constraints required for \(\beta\). To tackle this problem, a simple piecewise constant velocity interpolation was applied to the force and \(\beta\). This way, a large velocity constraint produces a step change which is a suitable input to the impedance controller. A visual example of the difference between the TOPP-RA and piecewise constant velocity interpolation is depicted in Figure 5.
## 2 Preliminaries
### Variable Limitations  
The values of the parameters considered are: \(J=450\) kg\(\cdot\)m\({}^{2}\), \(k_{f}=120\) kg\(\cdot\)m\({}^{2}\)/s, and \(T_{T\omega}=0.12\) s.  
In the dimensioning of the control system, the limitations imposed by the actuator must be taken into account, namely the maximum power \(P_{m}\) and the maximum torque \(M_{m}\) that it can develop in a permanent working regime. The maximum rotational speed \(\omega_{m}\), which cannot be exceeded by the moving body, must also be taken into account to ensure safe operation. These limitations are introduced in the structure of control system. It is considered that the rotation of the body can take place in both directions of rotation. The maximum limit of the position it can reach is \(\theta_{m}\), in one direction or another.  
\[P\in[0,P_{m}],\ M\in[-M_{m},M_{m}]\omega\in[-\omega_{m},\omega_{m}],\ \theta\in[- \theta_{m},\theta_{m}] \tag{3}\]  
In this case, the following maximum values were chosen: \(M_{m}=1000\) Nm, \(\omega_{m}=0.3\) rad/s, and \(\theta_{m}=180^{\circ}\).  
Below are analyzed some position control structures based on linear control systems taken as reference models for comparing the control efficiency [35]: linear control with error feedback; digital control; and state feedback control.
# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 2 Prerequisites for Using the UAVs in Some Spheres of Economy
### Monitoring of Technical and Engineering Structures (MoTES)  
The new industrial revolution, Industry 4.0 [91], is based on the paradigm of the industrial Internet of Things (IIoT). IIoT provides a new type of business model [92]. A key element of these models is collecting and processing the data throughout the entire life cycle of a product or structure.  
IIoT application areas are as follows: transportation systems and vehicles, machinery, construction, houses, firefighting equipment, agricultural systems, mining and related equipment, medical devices, and personal products, etc. [93].  
Figure 7: IUAVT in the disaster process control. Source: Generated by the authors.  
UAVs are used for monitoring, for 3D models construction, for the detection and assessment of damages, etc. [94]. Figure 8 based on [95; 96] illustrates usage of UAVs for tasks of such type.  
UAVs allow getting quickly the collections of 3D images, including ones from hard-to-reach places [97]. The obtained images are classified using deep learning, for example, to assess the condition of the wings of wind turbines [98]. The use of noise sensors makes it possible to detect the increasing destruction [99]. UAVs additionally equipped with an infrared camera can be used for monitoring of vast areas of photovoltaic stations [100]. Re-equipment with additional ultrasonic and laser scanners allows the use of UAVs for monitoring of the steel structures [101]. Along with the advantages of such use of UAVs, there are also disadvantages among which are flight restrictions, weather dependence, and weight restricting equipment.  
The assessment of the economic effect of the use of UAVs for air patrolling pipelines in Kazakhstan is given in [11], and its volume is about USD 7.9 million per year.  
The main prerequisites of using IUAVT for monitoring of technical and engineering structures are the necessity to collect information on the condition of machinery and facilities, especially in hard-to-reach places for the implementation of tasks [97; 98; 99; 1001].  
It is necessary to develop methods for monitoring engineering structures using computer vision systems and machine learning to identify and prevent violations in their work and to predict their condition. Scientific and technical problems, such as the development of software for machine vision, image classification, and indoor flight control applications, should be solved in this area. The evaluated economic effect of IUAVT employment is USD 10 million (see Appendix A Table A7).
## 4 Results and Discussions
According to the locations of data points on the map, it is clear to see that the SPR sensors (#1 and #2) have the high resolution down to \(\sim\)10\({}^{-6}\) and narrow linear angle range below 0.3\({}^{\circ}\). The phase-interrogation TIR (#3), Fabry-Perot resonator (#5), and intensity-interrogation TIR close to the critical angle (#4) have the middle resolution of \(\sim\)10\({}^{-4}\) and middle linear range of 0.3\({}^{\circ}\)\(\sim\)3\({}^{\circ}\). The white-light interferometry (#6) provides low resolution of \(\sim\)10\({}^{-1}\) but wide linear range of 40\({}^{\circ}\). In our results (#7), the proposed BR sensor has the similar high resolution level of \(\sim\)10\({}^{-6}\) (deg,) achieved in SPR. However, the linear range of 5\({}^{\circ}\) is much higher. Especially, the critical angle and SPR sensors always need fine initial angle setting [1; 6]. In the case of the BR sensor, the tolerance of the initial angle conditions will help the sensing module integrate easily with other elements in the instrument.
## 3 Experimental Results and Discussion
### Basic Physical Parameters
#### 3.2.3 Comparison of Static Compressive Stress-Strain Curves  
The stress-strain curves of concrete show three stages, including the linear rising stage, nonlinear stage of crack development, and decay stage after the arriving peak [26].  
The failure of concretes under static compression is mainly due to the initiation and development of cracks whose positions are concentrated on the joints of aggregates and cement additives. Inside concrete, original cracks are formed in the process of concrete pouring and solidifying. The concrete began to crack locally in order to release the stress and avoid stress concentration when it was subjected to slow-motion static stress. In this process, only some certain locations were destroyed; therefore, the energy released was less. In the compressive stage of concrete, the concrete was observed to be an elastic material and therefore the stress-strain curve is linear in this stage.  
With the further increase of the static load, the cracks inside concrete developed and intact cracks formed from the joints of the aggregates and mortar, and the concrete lost its stability gradually. In this stage, the cracks stop developing with the halt of the static compressive load and the strength of the concrete reached a maximum, and the stress-strain curve is nonlinear.  
During the stage of the stress-strain curve declining, as the bearing capacity of concrete is lost gradually, the gradient of the stress-strain curve is small. The stress-strain curves of the experimental results agree well with the above three stages.  
In Figure 14, the stress-strain curves of the two kinds of concretes in compression under static loading are plotted after water cooling and air cooling. The addition of the basalt fiber, maximum temperature, and cooling methods significantly affected the concrete behavior.  
Figure 12: The failure pattern of ordinary concrete in water cooling.  
Figure 13: The failure pattern of basalt fiber-reinforced concrete in water cooling.  
Under the same conditions, the compressive strength of basalt fiber-reinforced concrete is bigger than that of ordinary concrete. Besides, the higher the maximum temperature, the greater the reduction in the compressive strength. In addition, the static compressive strength of concrete after cooling in air is greater than that after cooling in water. In other words, water cooling is more detrimental to the compressive strength than air cooling.
## 3 Results and Discussion
### Mesogen Orientation of PAz Films
#### 3.1.4 Reversible Orientation Switching by Coating and Removal of the Top Layer  
As displayed in Figure 4c, the homotropic or planar alignment is changed by the absence and presence of polymer topcout. Here, an attempt was made to confirm the reversibility of the mesogen by repeated coating and removal of the polymer. To evaluate the Az mesogen orientation, UV-visible absorption spectra of the films on a quartz plate were taken in the transmission mode. The \(\pi\pi\)* band of Az around 320-350 nm region reflects the orientation of the long axis of Az mesogen.  
Figure 6 shows the changes in the UV-visible absorption spectrum in the course of the coating and removal procedures of the P4 VP top layer. A spin-coated PAA film (thickness: 100 nm) was first annealed (120 \({}^{\circ}\)C for 10 min), and then the absorbance of the \(\pi\pi\)* band was significantly reduced with a slight blue shift (black solid line to the red solid line). After annealing, the spectrum was somewhat broadened, probably due to the involvement of a J-type aggregate [45]. This shows the orientational change of Az to a more vertical orientation state. When an overcoat of P4 VP using methanol followed by annealing was achieved, the \(\pi\pi\)* band almost recovered to the spin-coated state with spectrum broadening (green solid line). Next, the topcout film of P4 VP was washed (removed) with methanol and annealed. The Az mesogen became vertically aligned again (red dotted line). By coating with P4 VP followed by annealing, the \(\pi\pi\)* band recovered (green dotted line). In this manner, the homeotropic and planar alignments of Az mesogen can be successfully altered repeatedly by topcout manipulation. The orientation changes of the mesogens during such processes are schematically displayed in Figure 6b.
## 5 Evaluation
### Evaluation Metrics
#### 5.4.1 Success Rate  
The attacking success rate \(\rho\) is defined as  
\[\rho=\frac{1}{N}\sum_{i\in V}\lambda_{V,V^{{}^{\prime}}}\times 100,\quad\lambda_{V _{i}V_{i}^{{}^{\prime}}}=\left\{\begin{array}{ll}1&V_{i}\equiv V_{i}^{{}^{ \prime}}\\ 0&\text{otherwise}\end{array}\right\}\]  
where \(V^{{}^{\prime}}\) represents the set of hypothetical traces, \(V\) represents the actual trace set of vehicles, \(N\) denotes the number of vehicles (i.e., the size of \(V\)), and \(V_{i}\) and \(V_{i}^{{}^{\prime}}\) represent individual traces in \(V\) and \(V^{{}^{\prime}}\), respectively. A higher value of \(\rho\) indicates that the adversary has successfully matched more vehicle trajectories, which suggests a weaker performance of the pseudonym-changing scheme. Hence, an effective pseudonym-changing scheme should strive to minimize the value of \(\rho\).
## 3 System Model
### Channel Model
\(d_{\text{S,G}}\) is computed in the same way as \(d_{i,S}\), except that \(\theta_{\text{ec}}\) is computed with \(\theta_{\text{g}}\) instead of \(\theta_{\text{u}}\), \(\theta_{\text{g}}\) is the elevation angle between the gateway on the ground and the LEO satellite in space.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 4 Results
### Detection of Moving Objects  
The detection and tracking methods were implemented in MATLAB (version 8.5) on a PC (Intel i5-7500). The interval \(k_{d}\) in Equation (1) was set at 5, thus the detection process was applied from the sixth frame; \(\theta_{\text{T}}\) in Equation (1) was set at 30; \(D\) in Equation (3) was set at \([1]_{1\times 1}\), \(\theta_{s}\) and \(\theta_{f}\) in Equation (4) were set at 30 and 1200 pixels, respectively. Figure 9 shows the detection process of the 6 th, 350 th, and 550 th frame. The first row is the detection results of Figure 8a. The second and the third rows are the results of Figure 8b,c, respectively. Figure 9a shows the binary images after the frame subtraction with thresholding. Assuming that the size of the targets is known, Equations (2) and (4) were applied to Figure 9a to result in Figure 9b. Figure 9c shows the target regions with rectangular windows. All targets in three frames were detected, with one false alarm in the 550 th frame, as shown in the third row in Figure 9c. Table 2 shows the detection rate of ten targets; the average detection rate was 96.5%. The total number of false alarms detected was 638, thus the false alarm rate was 1.17 per frame. The supplementary material, Video S1: Object Detection (AVI format) for object detection is available online.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Target No.** & **First Frame** & **Last Frame** & **Direction** & **Component** \\ \hline Target 1 & 1 & 515 & Downward & 1–2 person(s) \\ Target 2 & 1 & 237 & Downward & 1 person \\ Target 3 & 1 & 550 & Downward & 2 people \\ Target 4 & 123 & 550 & Upward & 3 people \\ Target 5 & 218 & 550 & Downward & 1 person \\ Target 6 & 266 & 550 & Left & 1 person \\ Target 7 & 310 & 550 & Downward & 1 Car \\ Target 8 & 379 & 550 & Upwards & 1 person \\ Target 9 & 411 & 550 & Upwards & 1 person \\ Target 10 & 397 & 550 & Upwards & 1 person \\ \hline \end{tabular}
\end{table}
Table 1: Target characteristics.  
Figure 9: (**a**) Frame subtraction with thresholding, (**b**) morphological filtering and false region removal, (**c**) target region windows.
## 3 Results and Discussion
### Overall Interpretations of the Results of Baseline Case  
Different wave erosion and slope damage conditions lead to changes in water level, seismic signals, acoustic signals, and pore pressure. To understand the causes of these changes, it is essential first to understand the erosion conditions of the slope. The results of the Baseline Case are divided into three stages, with each stage having the waves set differently in terms of wave height and frequency. In this study, the characteristic of the wave maker is that when a higher wave frequency is set, the wave height also increases accordingly. The limitation of this wave maker causes a correlation between the wave frequency and wave height of the waves produced. Table 4 lists the events and changes of recorded data of the Baseline Case, detailing the changes and observations made at each stage. Figure 4 shows the slope model's erosion process from Stage 1 to Stage 3. During Stage 1 (1180 s-2380 s), the slope was eroded by 0.33 Hz waves. The erosion range of the Stage 1 waves was small due to the highest run-up height of wave (H\({}_{\text{w}}\)) being only about 2.1 cm. The slope damage was limited to the area near the water, forming a wave-cut notch (Figure 4c). Also, because the erosion range of the waves was small, the wave-cut notch did not continue to expand during Stage 1 (Figure 4d,e). During Stage 2 (2520 s-3720 s), the erosion range of the 0.416 Hz waves was slightly larger (the highest H\({}_{\text{w}}\) was about 3.4 cm), which enlarged the wave-cut notch (2528 s-2562 s), triggered landslides, and caused the slope to retreat (Figure 4h). However, the erosion range of the waves was still small during Stage 2, so many soil masses remained on the slope after the landslide and were not washed away by the wave backwash (Figure 4 ij). During Stage 3 (3840 s-5160 s), the waves at 0.5 Hz were higher, and the highest H\({}_{\text{w}}\) reached about 13.3 cm (the erosion range reached 86.6% of the slope height), causing the slope damage and retreat to exceed that of Stage 1 and Stage 2 (Figure 4l-o). This indicates a significant increase in the impact of wave action on the slope. The side view of the test during Stage 3 is available for viewing in Video S1 of the Electronic Supplementary Materials, which is presented in a 16\(\times\) time-lapse format. Supplementary Figure S1 presents a series of side-view images of the slope model from Stage 1 to Stage 3. Supplementary Figures S2 and S3 display the environmental and human noise of seismic and acoustic signals, respectively, for reference.
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
## 3 Improved Sparrow Search Algorithm
### Improved Sparrow Search Algorithm
#### 3.2.3 Osprey Global Exploration Strategy  
In the global search phase, the optimization algorithm needs to have a wider global exploration range and better local stagnation escape ability to avoid falling into local optimal prematurely. However, according to the analysis in Section 3.1, the producer location update mode in the SSA will cause a loss of population information, and the search process is limited by the neighborhood range of the current solution, resulting in a weak global search ability. To solve this problem, the global exploration strategy of the osprey optimization algorithm (OOA, 2023) [38] was introduced to replace the producer location update of the SSA, fully expand the search space by using the information of the entire population, and achieve significant changes in individual location, thus avoid falling into local optimality.  
In the osprey predation mechanism, other osprey locations with better fitness values in the individual search space are regarded as underwater fish, then the location set \(FP_{i}\) of all candidate prey of the \(i\)-th osprey is specified using the following formula:  
\[FP_{i}=\{X_{k}|k\in\{1,2,\cdots,n\}\wedge F_{k}<F_{i}\}\cup\{Xbest\} \tag{26}\]  
Among them, \(Xbest\) is the best position for osprey, and \(F\) is the fitness value.  
At this time, the osprey randomly moves to the position of a candidate prey, and, in this process, the position update of the \(j\)-dimension of the \(i\)-th osprey is described as follows:  
\[x_{i,j}^{new}=x_{i,j}+r_{i,j}\cdot(SF_{i,j}-l\cdot x_{i,j}) \tag{27}\]  
where \(SF_{i,j}\) represents the position of the prey selected by the \(i\)-th osprey in the \(j\)-th dimension, \(r_{i,j}\) is a random number in [0,1], \(l\) is the random number in [1,2], and \(x_{i,j}\) and \(x_{i,j}^{new}\) are the positions of the \(i\)-th osprey before and after moving in the \(j\)-th dimension. If the fitness value of the individual is better in the new position, replace the previous position of the osprey with the following formula:  
\[\mathbf{X_{i}}=\begin{cases}\mathbf{X_{i}^{new}},r_{i}^{new}<F_{i}\\ \mathbf{X_{i}},r_{i}^{new}\geq F_{i}\end{cases} \tag{28}\]  
It can be seen that the position update of osprey fully considers the information of each individual in the population, which can achieve an effective search for a larger range. Therefore, using this mechanism to replace the producer location update mode in the SSA can significantly improve the global search ability of the algorithm.
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 4 Discussion
The spatial resolution of the used images will affect the evaluation accuracy of CLQ because when the rice planted areas that are smaller than the spatial resolution of the images dominate the study area, mixed pixels will exist. The GPP products from finer spatial resolution images acquired from different sensors should be tested. As the correlation coefficient method meets the assumption of normal distribution of data, it was used for determining the use of GPP from the four growth stages. In future study, more powerful methods may be introduced to obtain the growth stages of the crops. Finally, more evaluation algorithms (such as Random Forest and Deep Learning) should be attempted to improve the evaluation efficiency and accuracy for CLQ.
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 1 Introduction
In typical quantum circuit implementations of arithmetic operations, the identification of qubits suitable for the specialisation outlined above is a challenging task as a result of qubits acting repeatedly as control, as well as target qubit in gate operations. However, in reversible arithmetic operations, half of the input qubits will have an unchanged state upon completion of the computation. This domain-specific feature motivated the development of transformations of arithmetic circuits to a _parameterised_ form such that qubit specialisation can be automated in a quantum computing toolchain. This second aspect of the introduced quantum circuit transformations involving parameterisation of circuits represents the main novelty of the present work. Despite this use of domain specific knowledge, it is shown that the approach is still general enough for a wide range of quantum algorithms in the field of computational science and engineering. In a broader context, it can be noted that program transformation algorithms are essential constituents of modern compilers for classical languages [21; 22; 23; 24]. These are used to optimise the instructions corresponding to the original source code of a program to, e.g., minimise register use, instruction count or run time, or make optimal use of features, such as vector operations. Thus, the current work can be considered as extending the concept of program transformations to the field of quantum computing. The specific transformations presented are quantum circuit substitutions that allow static analysis tools to easily specialise circuits for fixed inputs, reducing the quantum circuit width (qubit count). Our main motivation for reducing the number of required qubits is to reduce the classical computing resources. However, the introduced transformations can also be used to create smaller circuits for proof-of-concept demonstrations on near-future quantum hardware, and would be an integral part of any future compiler for mature quantum computers. The rest of this section presents a brief review of quantum computing concepts, provides context in terms of the type of quantum algorithms our transformations introduced are aimed at, and presents the primary contributions of this work.