esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Antenna for Application in UAVs
## 4 Control System Development
### Digital Potentiometer  
The digital potentiometer receives information from the microcontroller and adjusts the desired voltage at the terminals of the diodes. For this, the digital potentiometer varies the value of its resistance and, being the current constant, output voltage varies. Through the digital communication between the microcontroller and the digital potentiometer, it was possible to control the value of the resistance, which, in turn, controls the voltage value. To select the digital potentiometer, the following factors were considered: two output channels; PDIP package; SPI interface; much sensitivity in varying the resistance value; and V\({}_{\text{IN}}\) = 5 V.  
After analyzing all possibilities, the selected digital potentiometer was Microship MCP42100. This potentiometer has two channels, 14 pins, 256 levels, and a resistance of 100 k\(\Omega\). It operates with voltages of 2.7-5.5 V, and the communication interface with the Arduino is SPI.
## 4 Experiment and Analysis
### Comprehensive Analysis  
To simulate the real environment, a comprehensive experiment was designed to explore the combined effects of above parameters. We randomly selected 20 LED-IDs that we used in the previous section to explore the effect of each feature on recognition rate. The feature data of these LED-IDs is shown in Table 3. A total of 120 groups were used as training samples, and the other 30 groups were used as test samples for each class.  
Figure 19: Contrast experiment with QR code in dark condition.  
Figure 20: LED transmitter platform.  
The average training time was 2.304 s and the total image processing/classifying time was 9.47 ms. The recognition rate of these LED-IDs is shown in Figure 21. It can be seen from Figure 21 that the recognition rate of most LED-IDs was 100%.  
This was undertaken in a laboratory scenario. In the future, the system can be applied to more extensive scenarios. Although the system needs pre-training, the user can specify the number of LED-IDs needed and then extracts them from the LED-ID library we have built. The cost is not very high in this way, but it can achieve a fairly satisfactory recognition rate and robustness. At the same time, we can see that the total processing/classifying time was very short when the system was in use, reaching the millisecond level. Last, there is no need to pay too much attention to training time, because users will use trained classifiers.  
What is more, our system can be implemented in many large-scale scenarios, not just in the lab. First of all, in the field of fast payment, scanning a QR code to pay is widely used now. However, it also has some limitations. In dark and distant conditions, it will fail. OBC with a longer effective distance  
Figure 21: The result of the comprehensive test.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Frequency (Hz)** & **Skewing Coefficient (\({}^{\circ}\))** & **Distance (cm)** & **Duty-Ratio (\%)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: The parameters of different OBCs.  
can reduce the queuing time for payments in some cases. Then, in the field of future autopiloting, we can use our system for traffic control. When the vehicle arrives at the designated node, the onboard camera captures the OBC and downloads the road information on the cloud server to perform the corresponding operation. In the outdoor advertising field, the system can also be used. Because the area of an advertising light board is limited, it cannot accommodate a lot of information, and it is limited to static information. By loading our OBC in the advertising light board, you can link to other web pages for more information. Additionally, in the field of indoor positioning, GPS tends to show weakness. Many scholars have proposed localization algorithms based on visible light communication in recent years [24; 25; 26]. However, these high-precision algorithms cannot be correctly labeled LED. It is rare to see the work of LED calibration in this field. Our system can solve this problem well.
## 4 Case Study: The Kimberley Climate Adaptation Project (KCAP)
### Context of the KCAP  
The Kimberley Climate Adaptation Project was a stakeholder-driven climate change process looking at local climate change impacts and adaptation options, that included local citizens, municipal staff, regional advisors, and a university research group [5,48,49]. A key element of the KCAP was a participatory local climate change visioning process, with multiple facilitated stakeholder workshops. The process lead to a public open house in which local climate change impacts, community vulnerability and resilience across different development scenarios, and possible mitigation and adaptation actions, were presented and discussed. Funded by the Real Estate Foundation, the Ministry of Community and Rural Development, and the Swiss National Science Foundation, with pro bono climate modeling by the Pacific Climate Impacts Consortium (PCIC), CALP contributed to the KCAP process through the integration and spatialization of climate change and other data for Kimberley, presenting the material in various media and types of visualizations, and evaluating process and tools.
# Calibration of a 3-Component External Balance for MAVs Wind Tunnel Research
## 4 Balance Calibration
Additionally, loads are applied to points 3 and 5 to generate force in the \(+Z\) direction and counterbalance them with the loads applied to the circular pieces so that no lift is generated and only the pitching moment is generated. Figure 9 shows an example for generating \(2/3M_{ymax}\) and Table 3 indicates the loading and unloading phase to generate pure positive pitching moment, \(+M_{y}\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Loads** & **P.3(+L)** & **P.5(+L)** & **P.7(\(-L\))** & **P.8(\(-L\))** & **L(N)** & **D(N)** & **M\({}_{Y}\)(Nm)** \\ \hline
**0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** \\ \hline +1/3\(L_{max}\) & \(1\times 77\) g & \(1\times 77\) g & \(0\) & \(0\) & \(1.54\) & \(0\) & \(0\) \\ \hline +2/3\(L_{max}\) & \(2\times 77\) g & \(2\times 77\) g & \(0\) & \(0\) & \(3.09\) & \(0\) & \(0\) \\ \hline +\(L_{max}\) & \(3\times 77\) g & \(3\times 77\) g & \(0\) & \(0\) & \(4.64\) & \(0\) & \(0\) \\ \hline +2/3\(L_{max}\) & \(2\times 77\) g & \(2\times 77\) g & \(0\) & \(0\) & \(3.09\) & \(0\) & \(0\) \\ \hline +1/3\(L_{max}\) & \(1\times 77\) g & \(1\times 77\) g & \(0\) & \(0\) & \(1.54\) & \(0\) & \(0\) \\ \hline
**0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Loading and unloading phase to generate pure lift force \(+L\)  
Figure 8: (**A**) Loads to generate pure lift and (**B**) pure aerodynamic drag force. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Loads** & **P.2(\(-D\))** & **P.4(\(-D\))** & **P.1(+D)** & **P.6(+D)** & **L(N)** & **D(N)** & **M\({}_{Y}\)(Nm)** \\ \hline
**0** & **0** & **0** & **0** & **0** & **0** & **0** \\ \hline \(-\)**1/3\(D_{max}\)** & \(1\times 35\) g & \(1\times 35\) g & \(0\) & \(0\) & \(0\) & \(-0.70\) & \(0\) \\ \hline \(-\)**2/3\(D_{max}\)** & \(2\times 35\) g & \(2\times 35\) g & \(0\) & \(0\) & \(0\) & \(-1.41\) & \(0\) \\ \hline \(-\)**\(D_{max}\)** & \(3\times 35\) g & \(3\times 35\) g & \(0\) & \(0\) & \(0\) & \(-2.10\) & \(0\) \\ \hline \(-\)**2/3\(D_{max}\)** & \(2\times 35\) g & \(2\times 35\) g & \(0\) & \(0\) & \(0\) & \(-1.41\) & \(0\) \\ \hline \(-\)**1/3\(D_{max}\)** & \(1\times 35\) g & \(1\times 35\) g & \(0\) & \(0\) & \(0\) & \(-0.70\) & \(0\) \\ \hline
**0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Loading and unloading phase to generate pure drag force \(-D\)
# Performance-Driven Yield Optimization of High-Frequency Structures by Kriging Surrogates
## 2 Yield Optimization Problem and Benchmark Algorithms
### Yield Optimization Problem  
In antenna and microwave designs, performance requirements are frequently formulated in a minimax form, i.e., by setting upper/lower acceptance levels on the electrical characteristics of the device at hand. These may include maximum in-band reflection  
The deviations \(d\mathbf{x}\) (e.g., manufacturing tolerances) are characterized by the specified probability distributions (e.g., uniform of maximum deviation \(\delta_{\text{max}}\) or joint Gaussian \(N(0,\sigma)\)). The deviations may be correlated [60], yet we assume here that they are statistically independent. Yield \(Y(\mathbf{x})\) at a certain the design \(\mathbf{x}\) may be assessed by using Monte Carlo analysis.  
\[Y(\mathbf{x})=\frac{1}{p}\sum_{k=1}^{p}H(\mathbf{x}^{(k)}) \tag{1}\]  
In (1), \(\mathbf{x}^{(k)}=\mathbf{x}+d\mathbf{x}^{(k)}\), \(k=1\),..., \(p\), denote the observables, \(d\mathbf{x}^{(k)}\) are the random deviations, and \(H(\mathbf{x})\) is given by the following.  
\[H(\mathbf{x})=\begin{cases}1&\text{ifdesign specifications are satisfied}\\ 0&\text{otherwise}\end{cases} \tag{2}\]  
We employ the following formulation of the yield optimization task.  
\[\mathbf{x}^{*}=\text{argmin}_{\mathbf{x}}\{-Y(\mathbf{x})\} \tag{3}\]  
Commonly, nominal design \(\mathbf{x}^{(0)}\) is used as a starting point for solving (3), which, in turn, is rendered by solving, e.g., problem (E1.2) (for the example of the multi-band antenna) or (E2.2) (for the example of the microwave coupler).
# Influence of the Microwave Heating Time on the Self-Healing Properties of Asphal Mixtures
## 1 Introduction
Self-healing properties of asphalt mixtures have been measured using the three-point bending test before and after microwave healing at four different heating times: 10 s, 20 s, 30 s and 40 s, during ten cracking-healing cycles. Finally, the main results of this research concluded that the heating time is the most influential variable on the healing level reached by the asphalt mixtures tested by microwave radiation, over the effect of the amount of fibres. Besides, CT-Scan results proved that fibre spatial distribution into the asphalt mixtures play an important role in the asphalt healing level. Furthermore, it was concluded that the optimum microwave heating time is 40 s, to reach the highest healing levels with the lowest damage on the asphalt. In short, the study of the crack-sizes revealed that heating times over 30 s can also seal the cracks, thus achieving the self-healing of asphalt mixtures via microwave heating.
## 1 Introduction
### Literature Review
The impact of a UPFC-based damping controller on the dynamic stability of the Iraqi power network was addressed by the authors of [18]. The optimal multi-objective design of robust power system stabilizers using a tunicate swarm algorithm was presented in [19]. In [3; 20],the authors discussed the dynamic performance of a 48-pulse GTO thyristor-based interline unified power flow controller (IUPFC) system. The authors of [21] introduced an adaptive UPFC-based stabilizer for the damping of low-frequency oscillations. In [22], the authors elaborated on how support vector regression enhanced the stability of an installed PSS-UPFC power system. Particle swarm optimization was employed by the authors of [23] to develop an output feedback UPFC controller for the damping of electromechanical oscillations. In [24], the authors demonstrated the most effective tuning method for a unified power flow controller to reduce interarea oscillations in a multi-machine system using the firefly method. In [25], the authors described a STATCOM controller design that dampens low-frequency oscillations in the power system using a modified shuffling frog leaping algorithm. The positioning and application of STATCOM storage for the enhancement of the voltage stability of power systems integrated with wind farms were detailed by the authors of [26]. A study of the stability and dynamic performance improvements in an autonomous microgrid utilizing an adaptive fuzzy PI controller was documented in [23]. The authors of [24] outlined the enhancement of the dynamic properties of a fuzzy control system. In recent years, additional modulation controllers (MCs) have been integrated into FACTS devices to mitigate interarea oscillations [27]. However, conventional regulation techniques do not ensure the optimal performance of power system controllers, highlighting the need for a simultaneous regulation solution for PSS parameters. Several approaches have been suggested for the coordination of the regulation of PSS and FACTS device parameters, primarily relying on parametric optimization models [28; 29]. These models, such as the transient state model [30] and the closed-loop residuals model [31], utilize gradient-based nonlinear optimization methods to tackle the issue. Despite their computational efficiency, these methods often struggle to identify the global system optimum or appropriate starting points.
## 3 Results
### Effect on Enzyme Activity  
The biological targets for plasma reactive species are diverse and it has been shown both in a cellular context and in isolation that exposure to these species can lead to lipid peroxidation, protein modification, and changes to enzymatic activity. Lactate dehydrogenase (LDH) is found in most mammalian and bacterial cells and has been used as a model to study the effects of plasma treatment on enzyme function.  
The same samples of plasma activated water studied above were tested for their effect on the enzyme LDH. All three sets tested showed a strong reduction of enzymatic activity by SD PAW for all plasma activation times. However, incubation with GD PAW resulted in similar or slightly enhanced enzyme activity compared to the control as observed in the time course of enzymatic NADH generation (Figure 8A-C) and the calculated LDH activity (Figure 8D). The three experimental sets were tested simultaneously and indicated similar effects of specific PAW activation times on LDH activities, even though the sets were of different age between 1 day and 2 weeks after generation.  
Figure 8: Effect of SD and GD PAW and their activation times on the enzymatic activity of lactate dehydrogenase (LDH); absorbance at A450 indicates reduction of NAD to NADH. Graphs show the results of three sets of PAW, generated at different time points but tested simultaneously. (**A**) Set A 2 weeks after generation; (**B**) set B 1 week after generation; (**C**) set C 1 day after PAW generation; (**D**) average LDH activity for all sets (data shown as mean \(\pm\) SD).  
Figure 7: Cell growth of CHO-K1 (**A**) and HaCaT (**B**) cell lines exposed to PAW generated by SD and GD discharge at 20% (v/v) as plasma activation time of PAW increases (data shown as mean \(\pm\) SD, \(n=3\)).
## 2 Project Overview
### Ground Improvement Overview  
The purposes of ground improvement at the pit bottom are threefold: first, to enhance the strength of the weak basal soil and reduce deformation of the diaphragm wall support structure; second, to lower the permeability of the basal soil, enabling dry excavation while ensuring anti-heave stability of the pit bottom; third, to increase the foundation bearing capacity and guarantee anti-uplift stability. Based on the comprehensive analysis of the anchorage system, the improved soil must meet three parameter requirements: 28-day-cored, unconfined compressive strength \(\geq\) 1.8 MPa, 90-day-cored, unconfined compressive strength \(\geq\) 2.7 MPa, and permeability coefficient \(\leq\) 1 \(\times\) 10\({}^{6}\) cm/s.  
To address varying geological conditions and distinct reinforcement priorities at different depths below the base, layered and zoned reinforcement was implemented: Grid reinforcement was applied within the elevation range of \(-\)18.8 m to \(-\)44.3 m, where high-pressure jet grouting piles were arranged in a quincunx pattern with an effective pile diameter of 1.7 m, center-to-center spacing of 1.7 m, and tangent to each other; full-area reinforcement was adopted within the elevation range of \(-\)44.3 m to \(-\)49.3 m, where high-pressure jet grouting piles were also arranged in a quincunx pattern but with an effective pile diameter of 2.4 m and center-to-center spacing of 1.7 m, forming secant pile reinforcement, as shown in Figures 3 and 4.
# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 1 Introduction
This will allow the model to acquire generalization capabilities that allow it to adapt in more diverse situations or in different environments. Through this process of idea generation, the model proposed in this paper can be further enriched and innovated. This paper proposes a novel approach in the field of domain adaptation by combining bidirectional feature learning and generative networks. The main idea is to enhance cross-domain adaptation through bidirectional feature learning and to improve the adaptation ability by more accurately simulating actual data distribution through generative networks. Based on the evaluation results, the proposed model demonstrated significantly higher accuracy than existing models such as DANN and ADDA. Through experiments, it was found that the proposed model exhibited outstanding performance in domain adaptation. These results strongly indicate that combining bidirectional feature learning and generation networks is an effective method for domain adaptation. As a major contribution and result, this paper not only presents an innovative solution in the domain adaptation field, but also provides a model that is superior to existing models such as DANN and ADDA. Thus, this research breaks new ground in domain adaptation and provides significant inspiration for future research and applications. Furthermore, the experimental results demonstrated that the proposed approach could minimize performance degradation of the generative model while aiding in the generation of desired outputs. This indicates the effectiveness of this paper in improving the stability and reliability of the generative model simultaneously. These evaluation results are of great significance in presenting the validity and practicality of the proposed approach in the paper. They are expected to contribute to research aimed at enhancing the security and robustness of generative models. The primary contribution of this paper is the development of a novel approach for domain adaptation in generative models and representation learning. By combining bidirectional feature learning and generative networks, we significantly improve the adaptation ability, accurately simulating the real data distribution. The experimental results showcase the superiority of our approach over traditional methods, marking a substantial advancement in the field of domain adaptation and enhancing the data and domain robustness in generative models. This article is structured into five sections. The first section introduces the background, the problem statement, the derivation process, and a brief summary of the idea. The second section presents a summary of previous research and relevant techniques. The third section provides an overall description of the proposed model. The fourth section explains and presents the results of experiments conducted to evaluate the model's performance.
## 7 Conclusions  
This work presents an analysis of the tuning methods for Extended Kalman Filters (EKFs) used in INS/GNSS integration. By comparing the standard (STD) tuning approach with a newly proposed approach (NA), the study demonstrates the efficacy of the NA tuning in maintaining trajectory accuracy, especially during periods of GNSS signal degradation. The NA tuning consistently shows better alignment with the platform results across various metrics, including altitude, latitude, longitude, velocities, and Euler angles. The findings indicate that the proposed tuning method not only enhances the performance of inertial navigation systems by providing more reliable and precise navigation solutions but also offers significant improvements in tuning efficiency. The proposed method achieves appropriate values for the **Q** matrix based on the datasheet specifications of the inertial sensors, streamlining the tuning process and reducing the time required to reach these desired values.  
Conceptualization, A.J.-A.T.J.; Methodology, A.J.A.T.J.; Software, A.J.A.T.J.; Formal analysis, A.J.A.T.J.; Investigation, A.J.A.T.J.; Writing--review & editing, A.J.A.T.J. and N.M.F.O.; Supervision, N.M.F.O.; Project administration, N.M.F.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CAPES (Coordination for the Improvement of Higher Education Personnel) grant number 88887.489312/2020-00.  
Not applicable.  
Note applicable.  
The original contributions presented in this study are included in this article.  
**Acknowledgments:** We would like to express our sincere gratitude to Castro Leite Consultoria for providing the inertial platforms used in this study. Their support and the equipment provided were invaluable for the successful completion of our tests and experiments.
## 4 Discussion
### Comparison of the Total Gas Content Tests Using Different Methods
#### 4.1.3 Results Comparison  
At present, among all the gas content-testing techniques, it is believed that the pressure-holding coring technique is the most accurate method for characterizing gas content in situ [50]. The total gas content characterization results from the different methods were compared and analyzed (Figure 9). The results showed that the traditional method measured the lowest total gas content at 11.82 m\({}^{3}\)/t, the total gas content via pressure-holding coring was 15.31 m\({}^{3}\)/t, and the total gas content tested via the NMR method was 18.88 m\({}^{3}\)/t.  
The following two factors contribute to the low gas content measured via traditional methods. Firstly, there is still significant controversy over the accuracy of shale porosity testing results [45; 46]. Inaccurate porosity measurement can lead to deviations in gas saturation, which affects the calculation results for free gas content. Secondly, in a confined space, the volume of methane under high temperature and pressure may no longer satisfy the ideal gas state law. As the temperature increases, the original adsorbed gas will then transform into free gas [40; 52]. Therefore, we believe that the total gas contents under high-temperature and high-pressure conditions measured using traditional methods are smaller and have greater errors.  
However, the dried plug L214 sample tested via the NMR method was measured at 18.88 m\({}^{3}\)/t, which is greater than the pressure-holding coring test result of 15.31 m\({}^{3}\)/t. This is because the object of the pressure-holding coring experiment is the underground core, which has a certain water saturation of about 15%. When the water saturation is about 10-20%, the adsorption capacity of shale decreases by 20-40% [51; 64]. Therefore, the adsorbed gas under in situ geological conditions is 0.7 times the amount of adsorbed gas in the dried sample, which was calculated to be 1.75 m\({}^{3}\)/t. The free gas in situ was directly calculated based on gas saturation and was found to be 13.92 m\({}^{3}\)/t. The total gas content under in situ geological conditions was 15.67 m\({}^{3}\)/t, which is very close to the result yielded by pressure-holding coring. This also confirms the reliability of the method established in our studies.  
Figure 9: Comparison of the gas content test results using the different methods.