esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Section 3 describes observer design, and Section 4 presents the controller design and a stability analysis. Simulation results are presented in Section 5. Section 6 concludes this paper. \begin{table}
\begin{tabular}{l l} \hline \(\mathbb{R}^{n}\) & \(n\)-dimensional Euclidean space \\ \(|\cdot|\) & Absolute value of a variable \\ \(\lambda_{min}(\cdot)\) & Minimum eigenvalue of a matrix \\ \(\min\{\cdot\}\) & Minimum value of a set \\ \(\inf\{\cdot\}\) & Supremum of a set \\ \((\cdot)^{T}\) & Transpose of a matrix \\ \(\|\cdot\|\) & Euclidean norm of a vector \\ \(\operatorname{diag}\{\cdot\}\) & Diagonal matrix \\ \(I_{n}\) & \(n\times n\) dimensional identity matrix \\ \(0_{n}\) & \(n\times n\) dimensional zero matrix. \\ \((x_{i},y_{i})\), \(\psi_{i}\), \((x_{j},y_{j})\), \(\psi_{j}\) & Position and yaw of the \(i\)th USV and the leader (\(j\)) \\ \(u_{i}\), \(v_{i}\), \(r_{i}\), \(u_{j}\), \(v_{j}\), \(r_{j}\) &
\begin{tabular}{l} leader (\(j\)) \\ \end{tabular} \\ \(m_{i,11}\), \(m_{i,22}\), \(m_{i,33}\) & Inertia mass of the \(i\)th USV \\ \(\sigma_{i,h}\) & Hydrodynamic damping parameters and disturbances \\ \(\tau_{i}=[\tau_{i,u},0,\tau_{i,r}]\) & Input signals of the \(i\)th USV \\ \(\eta_{i}\), \(v_{i}\) & \(\eta_{i}=[x_{i},y_{i},\psi_{i}]^{T}\), \(v_{i}=[u_{i},v_{i},r_{i}]^{T}\) \\ \(\eta_{j}\), \(v_{j}\) & \(\eta_{j}=[x_{j},y_{j},\psi_{j}]^{T}\), \(v_{j}=[u_{j},v_{j},r_{j}]^{T}\) \\ \(M_{i}\) & \(M_{i}=\operatorname{diag}\{m_{i,11},m_{i,22},m_{i,33}\}\) \\ \(R_{i}\) & Rotation matrices of the \(i\)th USV \\ \(\tau_{ic}=[\tau_{i,uc},0,\tau_{i,rc}]^{T}\) & \(\tau_{ic}\) calculated by the proposed controller \\ \(\alpha_{i}=[\alpha_{i,u},0,\alpha_{i,r}]^{T}\) & The mismatch function between input without saturation and input with saturation \\ \(\rho_{ij}\), \(\lambda_{ij}\) & Relative distance and angle between the \(i\)th USV and the leader (\(j\)) \\ \(\rho_{ij,d}\), \(\lambda_{ij,d}\) & Desired distance and angle \\ \(\hat{x}\) & Estimate of variable \(x\) \\ \(t_{k}\) & Previous event-triggering time instant \\ \(\hat{v}_{i}\) & Transmitted signal between \(\eta_{i}(t)\) and \(\eta_{i}(t_{k})\) \\ \(d(t)\) & Event-triggered mechanism \\ \(\hat{x}=\hat{x}-x\) & Estimation error of variable \(x\) \\ \(p_{ij}=[\rho_{ij,e},\lambda_{ij,e}]^{T}\) & Formation tracking error vector \\ \(\Theta_{i}\) & Estimator used to estimate velocity of the leader \\ \(l_{i,h}\) & Time constants of the first-order filters \\ \(\psi_{i,e}\) & Yaw tracking error \\ \(\hat{p}_{i,h}\) & Virtual controls \\ \hline \end{tabular}
\end{table} 
Table 1: Notations and variables used in this paper.
# Time and Its Measure: Historical and Social Implications
## 2 The Social Implications of the Measurement of Time
This is the reason why some countries like China, with extensions covering up to five different time zones, have adopted only one (+8 compared to Coordinated Universal Time). An exception is the Xinjiang region, where two time zones are followed at the same time to optimize commercial activities with neighboring Kazakhstan and Kyrgyzstan, however creating some confusion among the inhabitants. Regarding the second aspect, the GPS system has now supplanted the navigation systems developed during World War II, when it witnessed a tremendous research and development effort of radar and related techniques. Among the latter, the most widespread was, until the 1960s, the LORAN: Long Range Navigation System [8], which uses the time interval between signals received from three or more stations to determine the correct position. The continuous development of technology made possible today the multi-GNSS (Global Navigation Satellite System) constellations (about 140 GNSS satellites in space in 2020 managed by eight different analysis centers [9]), significantly improving performance, satellite visibility, the dilution of precision, accuracy, spatial geometry, redundancy and reliability [10]. It is significant to underline that, despite the GNSS requiring high-accuracy time information, it is used successfully in many scientific (studies of the Earth atmosphere, snow depth, soil moisture and terrestrial water storage variation) [11] and practical applications (the positioning accuracy of a smartphone), with the advantages of continuous operation, a low cost, operation in all-weather conditions, a high accuracy and a high temporal resolution [12]. Examples of temporal cadences are as follows:  
Figure 5: GPS operating scheme: through the signals received from satellites in orbit (at least 4), the terminal is able to calculate its position by knowing the flight time of the signals, the map of the satellites and their time synchronization. In juridical practices for Roman law, \"_dies a quo non computatur in term_\"; for cultures such as Judaism, it is \"_the third day He rose again_\", that is, from Thursday to Saturday and not \"on Sundays\";
* The measurement of the average speed with the safety tutors used for road \"safety\", allowing for the measurement of the crossing times of the coils drowned in asphalt, which, compared with the data relating to distances (fairly precise mileage values), allows one to calculate the average speed (remember that this quantity is not measurable because it is physically inconsistent).
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 5 Assessment of the Usefulness of the Collected Data  
The mobile application collected data on the duration of individual maintenance tasks included in the considered inspection. For each of the 101 technical maintenance tasks, approximately 20 measurements of task execution time were made.  
From the perspective of a maintenance planning engineer, information about the time of the completion of a specific task is very important [23]. Accurate duration data enable the creation of more effective schedules, ultimately leading to reduced downtime due to technical servicing. The more accurately the available information describes the analyzed phenomenon, the greater the potential for achieving improved outcomes, such as reduced downtime caused by technical servicing.  
The data collected by the application could be used to estimate the probability distribution that describes the time of performing the individual maintenance task. The developed algorithm for identifying the probability distribution and the identification process for an exemplary technical task are presented below. Such considerations were carried out for all maintenance tasks of the considered inspection, and the characteristics describing their duration were determined.
# Through-Wire Microstrip-to-Empty-Substrate-Integrated-Waveguide Transition at Ka-Band
## 3 Results and Discussion
In the whole Ka-band, the measured insertion losses are lower than 3.7 dB and the return losses are higher than 11.7 dB. Figure 4: Assembled back-to-back transition. Figure 3: Layers of the back-to-back transition. From left to right: upper layer, middle layer, and lower layer. The back-to-back transition manufactured in this research has also been compared with other transitions in Table 3. In this case, the complexity of the transition, although it continues to be easy, is a little higher due to the increase in frequency that makes it necessary to improve the precision of each one of the manufacturing steps. Moreover, although insertion losses are higher than in other transitions at the same frequency, the advantages of this one (easiness of manufacturing, independence of microstrip and ESIW heights, possibility of implementation of planar circuits in top and bottom layers, feeding of the ESIW from any angle, possibility of designing broadband and narrowband devices and frequency independently) make this transition the most versatile one.
# Mechanical Performance Analysis and Experimental Study of Four-Star-Type Crank-Linkage Mechanism
## 2 Numerical Calculation of Crank Linkage
### Static Strength Verification of Some Parts  
As it is necessary to ensure equal masses of pistons at all levels as much as possible, and the bore of the first-stage cylinder is larger, the static strength is calibrated for the first-stage piston and the second-stage linkage assembly according to the load-bearing capacity of the material and the calculated multiple piston force [16]. First, the 3D solid model is simplified by ignoring features such as chamfers and oil holes. Then, the mesh is divided for the calibration object, and material properties and boundary conditions are also defined to constrain the radial motion of the piston, and the round hole of the big end of the linkage is completely fixed. The piston end is loaded with the maximum value of the multiple piston force, as shown in Figure 5a. The stress distribution nephogram of each component is obtained, as shown in Figures 6-8. The contact force area between the needle roller bearing and the primary piston pin is small, and the stress amplitude at the location where the two are in contact is large. The large-diameter first-stage piston is made of aluminum due to the demand for a lightweight piston. The strength is ensured mainly by internal gussets. The location where the gussets are connected to the pin bore is prone to stress concentration. The tension-compression stress will exist cyclically during the operation of the linkage. It is necessary to pay attention to the stress concentration in the transition fillet of the linkage body.  
Figure 5: Variation curve of piston force with crank angle. (**a**) Variation of multiple piston force with crank angle. (**b**) Variation of piston side thrust with crank angle.  
Figure 6: Stress distribution nephogram of the first-stage piston assembly.  
Calculated by finite element analysis, the maximum stresses of the above parts do not exceed the ultimate strength of the corresponding materials. The specific values and parts with the maximum occurring stress are shown in Table 2.
## 3 Experimental Results and Discussion
### Clinical Study  
To evaluate the proposed method's overall performance and actual focus effect, 40 volunteers (17 women, 23 men; mean age = 35 \(\pm\) 12 years, range 18-75; mean refractive error = +0.89 \(\pm\) 0.32 D, range \(-\)0.55-+4.5 D, no history of other eye diseases) were recruited to participate in a clinical study. The Ethics Committee of Tianjin University approved the study, and all participants provided informed consent to participate in the retinal examination (both eyes), in compliance with the Declaration of Helsinki. Manual focus was performed in each case (Experts A, B), as well as three search strategies for autofocus: Gaussian function fitting (GFF) [37], IHCS, and POSE. Autofocus was considered to be successful if an expert believed that that automatic and manual focus results were consistent [16]. This study adopted adaptive window and sharpness evaluation criteria in [21] for all autofocus cases with uniform search accuracy (1/4\(\Delta\)) and scanning start point. The comparison results and some autofocus results are shown in Table 2 and Figure 9.  
The study results show that the combination of adaptive window and POSE achieves the highest autofocus success rate of up to 90%, confirming the simulation results. Moreover, the proposed method is the least expensive regarding motor movement count and steps (each autofocus takes about 2.1 s), thus striking a better balance between search efficiency and accuracy. From Figure 9a,b, three strategies achieved acceptable focus results under both ideal and pupil misalignment (generating SL) conditions, mainly due to the excellent  
Figure 8: Relationship between search success rate and diopter.  
Figure 7: Search results for some simulated curves (black dotted lines indicate the correct peaks): (a) Oscillation near the start point; (b) oscillation near the peak; (c) valley near the start point.  
evaluation curve performance provided by the adaptive window. However, both local IHCS and global GFF strategies suffer from local extrema when nystagmus and eyelsh occlusion occur, resulting in defocused images, as seen in rows 1-2 of Figure 9c,d. Conversely, POSE still provides focused images containing distinct and recognizable retinal anatomies, including vessels and leopard patterns, as seen in row 3 of Figure 9c,d. These results illustrate that the proposed method can rapidly and accurately provide autofocused images of superior quality under complex fundus imaging conditions. In addition, our method exhibits 10% failure in cases that are primarily attributed to (i) involuntary eye movements or blinking during autofocus initiation; (ii) motor speed limitations that prevent faster autofocus. Using faster system configurations (such as piezoelectric ceramic stages and GPU parallel computing) and good individual guidance before examination can help further improve the autofocus success rate.
# Experimental Study of LoRa Transmission over Seawater
## 4 Experimental Results and Analysis
### LOS Scenario
#### 4.1.2 ISM 434 MHz
The uncorrelated fluctuations of SNR to RSS are due to the same reasons as in the other two experimental scenarios for 434 MHz--the saturation of SNR measurement chain and oscillation which chain exhibits in that region. Eventually, Figure 20 gives an insight in the distribution of the RSS and SNR moving averaged values, calculated on 1-min window for the same measurement. Here the same conclusions from the previous experimental case can be applied. Figure 18: RSS and SNR histograms for ODT: \(f=434\,\mathrm{MHz}\), \(BW=125\,\mathrm{kHz}\), \(SF=10\), \(CR=4/6\), LOS. Figure 19: RSS, SNR, and PRR for ODT at 434 MHz in LOS channel conditions for \(BW=125\) kHz, \(SF=12\), and \(CR=4/6\). Figure 20: RSS and SNR histograms for ODT: \(f=434\) MHz, \(BW=125\) kHz, \(SF=12\), \(CR=4/6\), LOS.
## 4 Simulation Verification and Physical Experiment
### Method Robustness Verification
#### 4.3.2 Single-Switch Fault Under Load Fluctuation  
Figure 9 illustrates the diagnostic results of switch OC fault under load fluctuation. As shown in the Figure 9, the load resistance increases by 15% at \(t=0.11\) s. The detection variable \(R_{c}\) remains consistently below \(T_{c}\), showing no signs of misdiagnosis. When an OC fault occurs in \(T_{c4}\) at \(t=0.167\) s, both \(R_{c}\) and \(T_{c}\) change, with \(R_{c}\) subsequently exceeding \(T_{c}\). Meanwhile, it is observed that \(W_{c}>w_{c}\) and \(\eta_{c}\geq\delta_{1}\), with all phenomena indicating a fault in \(T_{c4}\). The above analysis shows that the proposed method remains reliable without generating false alarms even under load fluctuation, further verifying its robustness.
## 2 Methods and Materials
### AlfaCrux Spacecraft
#### 2.2.2 Operational Modes  
The AlfaCrux operational modes schematic can be seen in Figure 5. A short description of each mode is as follows.  
* Boot-up: During the first boot up of the system, the software shall execute a sequence of first tests and actions, including health check and memories and file system initialization. These actions shall be executed only once. After the completion of the boot up actions, the satellite shall enter Init mode and continue.
* Init: In this mode, the software checks if the antenna was deployed. If not, it shall immediately enter Startup mode; further health checks will be executed once the antenna is deployed.
* Startup: The main objective of this mode is to deploy the antenna and to move to Survival mode once the antenna deployment sequence finalizes. This mode will also collect telemetry from the first minutes of the mission. The antennas shall be autonomously deployed by the onboard software 30 min after ejection. At the end of the antenna deployment sequence, the TTC is configured to allow transmissions. The file system will not be enabled in Startup to avoid errors that could avoid the completion of the Startup sequence. It will be enabled after the completion of Startup, just before the change to Survival mode.
* Survival: This mode is intended to be the fallback mode in case of critical problems. This mode shall have a positive power budget and shall guarantee the possibility to access the satellite from ground. Payloads and non-critical subsystems are turned off in this mode. A beacon with basic health information is transmitted periodically to ease the location of the satellite and to provide a first assessment of its status. TTC is configured with its default configuration. The only way to exit Survival mode is to set a mode change by telecommand (TM) and reboot the software.
* Nominal: The main tasks for the mission execution are performed in Nominal mode. Payloads are allowed to operate in this mode, providing a digital data repeater at 437.225 MHz based on AX.25 protocol, and also a store-and-forward service at 437.125 MHz using Gaussian Minimum Shift Keying modulation. The main scheduler will be in charge of the execution of the different tasks. Health checks are executed periodically. If a critical problem is found, the software will change to Survival mode autonomously.  
Figure 4: AlfaCrux configuration.
## 1 Introduction
Fatigue crack growth analysis is of paramount importance in the field of structural engineering as it ensures the safety and reliability of diverse components and structures. Traditionally, fatigue crack growth analysis involved extensive physical testing, which could be both time-consuming and expensive; however, with advancements in numerical simulation techniques, engineers and researchers now have a powerful tool at their disposal to predict and analyze crack behavior in a more efficient and cost-effective manner. The utilization of numerical simulation, particularly with ANSYS Workbench 19.2, offers numerous advantages for FCG analysis. The software's comprehensive capabilities, time and cost efficiency, ability to conduct parametric studies, validation and verification features, and its status as an industry standard make it a preferred choice for engineers and researchers in the field. Engineers can effectively improve the safety, reliability, and performance of structures subjected to fatigue loading conditions by leveraging the capabilities of ANSYS Workbench. This software platform offers a range of powerful tools and simulations that enable engineers to analyze, optimize, and predict the behavior of designs under fatigue conditions. While ANSYS Workbench [33; 34; 35; 36; 37; 38; 39] is a prominent software for FCG analysis, it is important to acknowledge that there are other software tools available in the field with their own unique strengths and areas of application. Examples of such software tools include ABAQUS [40; 41], FRANC3D [42], ZENCRACK [43], COMSOL [44], BEASY [45], and NASTRAN [46]. Certainly, this article primarily emphasizes the importance of ANSYS Workbench in the FCG analysis. The main objective is to investigate the intricate relationship among stress ratios, stress intensity factors, and crack growth in the context of fatigue analysis. By conducting a comprehensive analysis of these factors, a deeper comprehension of the fatigue behavior of materials and components can be attained. The stress ratio range of 0.1 to 0.9 is selected because it represents a wider range of real-world loading conditions. The outcomes of this study will contribute to the advancement of more precise fatigue life prediction models and provide insights for designing and maintaining practices that guarantee structural integrity and reliability. The research gap in this study pertains to a lack of specific information regarding the existing knowledge and understanding of the relationship between the stress ratio and fatigue crack propagation within the framework of linear elastic fracture mechanics. This indicates a need for further investigation to address this knowledge gap and develop a more comprehensive understanding of how stress ratio influences the behavior of fatigue crack propagation.
## 6 Proposed DL Network Architecture and Framework
### LSTM Cell Structure and Mechanism  
In LSTM cell, the output is generated based on the current input and the preceding cell state. In order to remember the previous cell state and determine if the prior state will be used or not, the LSTM cell consists of different types of gates, these gates are the forget gate, the input gate, and the output gate. In LSTM, there are two states, the cell state \(C_{t-1}\), which is called internal memory where all information is stored, and the hidden state \(h_{t-1}\), which is used for computing the output.  
Figure 3 illustrates the internal structure of LSTM cell [32], where \(t\) represent the time instant, \(x_{t}\) is the current input, and \(h_{ti}\) represent the current output channel coefficients for user \(i\) at time \(t\). In addition, \(C_{t-1}\) represents the previous cell state, which is shifted from a hidden layer to the next every single iteration. At every time step, the LSTM cell can add up information or remove information from the cell state. LSTM cell can regulate these updates using several gates that can be briefly described as follows:  
1. The forget gate is responsible for controlling the level of cell state that need to be reset: \(f_{t}=\sigma\left(\mathbf{W}_{j}x_{t}+\mathbf{R}_{j}h_{t-1}+\mathbf{b}_{j}\right)\);
2. The input gate is responsible for controlling the level of cell state that need to be updated: \(\mathbf{i}_{t}=\sigma(\mathbf{W}_{j}x_{t}+\mathbf{R}_{j}h_{t-1}+\mathbf{b}_{j})\);
3. The candidate state is responsible for adding information to the cell state: \(\mathbf{g}_{t}=tanh\big{(}\mathbf{W}_{g}x_{t}+R_{g}h_{t-1}+\mathbf{b}_{g}\big{)}\);
4. Updated cell state: \(\mathbf{C}_{t}=(\mathbf{C}_{t-1}\odot f_{t})+(\mathbf{i}_{t}\ \odot g_{t})\), where \(\odot\) is element-wise multiplication.
5. The output gate is responsible for controlling the level of cell state added to hidden state: \(\mathbf{O}_{t}=\sigma(\mathbf{W}_{o}x_{t}+\mathbf{R}_{o}h_{t-1}+\mathbf{b}_{o})\);
6. Estimated output coefficients: \(\mathbf{h}_{t}=\mathbf{O}_{t}\odot tanh(\mathbf{C}_{t})\).
## 4 Discussion
#### 4.1.4 Relocation-Reallocation with the VAOMP Model to Service All Demands Using Both Algorithms
Since the final output of these models indicates the number of demands allocated to each station, the third parameter, which is examined to establish the robustness of the solutions, is the difference in the allocations between the various iterations. The proportion of total demands that are assigned in the same way in the 10 iterations of the experiment is considered the evaluation parameter. The values obtained for the two methods are shown in Table 5.  
As can be seen, the increase in the number of stations, due to the complexity of the space, and reduced the accuracy of allocation in both methods. The fourth parameter is the average percentage of demand allocated by the two algorithms. Its results are shown in Table 6.  
The last parameter examined is the time taken for the problem-solving, which is measured during the test. In Table 7, the average runtime in each of the implementations has been shown for the two methods. Regarding the parameters studied, it can be said that the Tabu Search method has greater stability and strength than the Simulated Annealing method. The Simulated Annealing algorithm produces a worse result in solving this type of problem, while its solving time for the Median problem is higher than Tabu.
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 3 Experiments and Evaluation  
### Experimental Equipment  
To validate the performance of the proposed algorithm for velocity calculation in vision navigation, a practical experiment based on an UAV was carried out in the campus of North University of China, Taiyuan, China. In the experiment, the flight speed of our UAV was about 8.4 m/s. The optical flow sensor was calibrated by the method proposed in reference [41], and the internal and external parameters of the sensor were obtained. The NovAtel ProPak6 GPS was used in this experiment as the high-precision speed and position reference. After calculation, about 50 pixels were moved between the two frames. The parameters of cameras and reference are shown in Table 2. The experimental facilities are shown in Figure 7.