esa-sceva/satcom-chunk-collection huggingface dataset:
# Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems
## 2 Background
### Field Irradiance Measurement Theory  
Predicting the entrance aperture-reaching spectral radiance from point targets relies on both the total downwelling irradiance and the amount of diffuse sky contributing to the measured signal [5]. In this section, we define the field irradiance measurement followed by the formal spectral radiance description of point targets in Section 2.3.  
The ASD field spectroradiometer uses an advanced cosine corrector (i.e., optical diffuser with a collecting solid angle of 2\(\pi\) steradians) to measure the total downwelling irradiance \(E_{T}(\lambda)\). This can be represented mathematically as  
\[E_{T}(\lambda)=E_{solar}(\lambda)+E_{sky}(\lambda) \tag{1}\]  
where \(E_{solar}(\lambda)\) is the direct solar irradiance and \(E_{sky}(\lambda)\) is the sky irradiance. We assume no additional scattering from surrounding objects such as vegetation or man-made objects. To account for the contribution from the sky irradiance into the point targets spectral radiance signal, the sky irradiance term must be estimated. Without a precise instrument to perform this task (i.e., rotating shadowband radiometer which performs shading), the sky irradiance can be estimated manually by blocking (i.e., shading) the solar disk with a black object attached to a pole [13]. Even though this provides a good estimate of the Global-to-Diffuse Ratio, a side-shaded measurement would improve the accuracy. Side shading corrects for the signal lost during the sun-blocked measurement. This method was not implemented; however, we did take into account conservative estimates of uncertainty [14].  
Figure 2: Validating the HSI system, highlighted within the yellow box, against the NIST-traceable integrating sphere at four different light levels. Agreement is within the expected uncertainty as can be seen in the plot.  
With estimates of the total downwelling irradiance and the sky irradiance (i.e., the shaded measurement), the Global-to-Diffuse Ratio, \(G(\lambda)\) can be defined as  
\[G(\lambda)=\frac{E_{\text{sky}}(\lambda)}{E_{T}(\lambda)}=\frac{E_{\text{sky}}( \lambda)}{E_{solar}(\lambda)+E_{sky}(\lambda)} \tag{2}\]  
Equation (2) is the fractional contribution of the sky irradiance to the total downwelling irradiance and includes the instantaneous scattering properties of the atmosphere [15]. The Global-to-Diffuse Ratio \(G(\lambda)\) is calculated from field measurements and is used in predicting the entrance aperture-reaching spectral radiance from convex mirrors and will be discussed in the following section.
## 2 Material and Methods
### Chemicals  
LiChroult EN cartridges (500 mg, 6 mL) were obtained from Merck (Darmstadt, Germany). All chemicals were of analytical reagent grade unless otherwise stated, and water was obtained from a Milli-Q purification system (MilliporeSigma, Burlington, MA, USA). Folin-Ciocalteu reagent, sodium carbonate, sodium acetate, potassium chloride, sodium chloride, sucrose, methanol (HPLC grade), ethanol (HPLC grade), dichloromethane (HPLC grade) and gallic acid were purchased from SCR(Shanghai, China). Eucalyptus (99%), linalool (\(\geq\)95%), (\(-\))-myrtenol (95%), carveol (97%, mixture of isomers), borneol, (\(\geq\)99.0%, sum of enantiomers, GC), terpinolene (\(\geq\)94.0%), \(\beta\)-citronello1 (95%), geraniol (98%), ethyl-2-methylbutyrate (99%), ethyl-3-methylbutyrate (98%), (Z)-3-hexenol (98%), (E)-2-hexenol (96%), benzyl alcohol (\(\geq\)99%), phenylethyl alcohol (\(\geq\)99%), cuminic alcohol (97%), methyl butanoate (99%), isobutyl acetate (99%), ethyl butanoate (99%), isoamyl acetate (\(\geq\)99%), methyl benzoate (99%), ethyl benzoate (\(\geq\)99%), diethyl succinate (99%), methyl salicylate (\(\geq\)99%), ethyl octanoate (\(\geq\)99%), ethyl decanoate (98%), methyl vanillate (99%), decanoic acid (\(\geq\)98.0%), benzaldehyde (\(\geq\)99%), p-cresol (99%), 4-vinylguaiacol (\(\geq\)98.0%), vanillin (99%), (E)-asarone (98%), (Z)-asarone (70%) were obtained from Sigma-Aldrich (St. Louis, MO, USA). All volatile standards were prepared by dilution with HPLC grade methanol.
# Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems
###### Abstract  
This paper presents control algorithms for multiple non-holonomic mobile robots moving in formation. Trajectory tracking based on linear feedback control is combined with inter-agent collision avoidance. Artificial potential functions (APF) are used to generate a repulsive component of the control. Stability analysis is based on a Lyapunov-like function. Then the presented method is extended to include a goal exchange algorithm that makes the convergence of the formation much more rapid and, in addition, reduces the number of collision avoidance interactions. The extended method is theoretically justified using a Lyapunov-like function. The controller is discontinuous but the set of discontinuity points is of zero measure. The novelty of the proposed method lies in integration of the closed-loop control for non-holonomic mobile robots with the distributed goal assignment, which is usually regarded in the literature as part of trajectory planning problem. A Lyapunov-like function joins both trajectory tracking and goal assignment analyses. It is shown that distributed goal exchange supports stability of the closed-loop control system. Moreover, robots are equipped with a reactive collision avoidance mechanism, which often does not exist in the known algorithms. The effectiveness of the presented method is illustrated by numerical simulations carried out on the large formation of robots.  
formation of robots; non-holonomic robot; stability analysis; Lyapunov-like function; target assignment; goal exchange; path following; switching control +
Footnote †: journal: Applied Sciences
## 3 Results  
Figure 3 shows the SPLs obtained for the HATS according to the hardness of the pinna simulators and the SPLs obtained for human ears. To ensure the repeatability of our method, we attached the transducers to the HATS and removed them three times, as shown by solid lines (the averaged values) in the figure. The dashed lines indicate the average values from three human participants in a previous study [6].  
In the human cartilage-conduction sound data (dashed line), the spectral peak around 2.5 kHz is caused by resonance in the external auditory canal, while the peak around 800 Hz is specifically associated with cartilage conduction [6]. Although the lower peak was too sharp for the original HATS pinna, as shown in Figure 3d, the urethane pinna simulators in the present study produced close spectral shapes that were lower than 1 kHz. Particularly, when the hardness of the pinna simulator approached that of human aural cartilage (A10 to A20), the spectral shapes became more similar to the dashed line representing the human data (Figure 3b,c). In contrast, for frequencies higher than 5 kHz, we observed large differences in cartilage conduction between the HATS and human measurements, regardless of the pinna hardness. The averaged errors between the conduction sounds generated by the simulator (solid line) and human cartilage (dashed line) were 6.9 dB for A40, 6.4 dB for A20, 4.8 dB for A10, and 4.8 dB for the original HATS pinna in the frequency range below 5 kHz. Although the original HATS pinna showed a different spectral shape, the averaged error was close to the A10.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 5 Performance Evaluation
### Performance in Small-Scale Constellation
Figures 11 and 12 show the average and maximum delays with the three methods in a period, respectively. The horizontal axes for the two figures are the topology snapshot number. As the LPTSO topology lasted for a longer time and there were fewer topology snapshots in the period, the lines for LPTSO in the figures are shorter. It can be seen from the figures that the average delay and maximum delay of LPTSO were small and stable over the period. As MSN builds ISLs between fixed ISL terminals, the number of ISLs in the network was lower and, so, the average and maximum delay were both high. The number of ISLs in the network with LCTS is large, making the average delay close to or slightly smaller than that of LPTSO in most snapshots. However, due to the frequent and unstable ISL switching in the time dimension, the average delay fluctuated greatly. Furthermore, the ISLs with LCTS were unevenly distributed among different adjacent planes, so the maximum delay was higher than that of LPTSO. Figure 7: Topology snapshots of the link-building planning with LPTSO. \begin{table}
\begin{tabular}{l c} \hline \hline  & **Value** \\ \hline Avg. ISL Duration & 3168 s \\ \hline Avg. Topology Duration & 300 s \\ \hline Avg. Delay & 2.74 hops \\ \hline Max. Delay & 4.00 hops \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of link-building planning with LPTSO. Figure 11: Average Delay with three different methods. Figure 8: ISL duration with three different methods. Figure 10: Topology duration with three different methods. Figure 9: Topology switches with three different methods.
## 2 System and Channel Models
### Channel Models
According to (18) and (21), the PDF of \(C_{t}\) can be calculated as follows:  
\[f_{C_{t}}(c)=\int_{0}^{c}f_{C_{f}}(x)f_{C_{RF}}(c-x)dx \tag{24}\]  
The final expression of \(f_{C_{t}}(c)\) is given by  
\[ \begin{array}{l}f_{C_{t}}(c)=\frac{\xi_{3}^{2}\lambda b^{\mu+\beta-2}}{ \Gamma(\alpha_{3})\Gamma(\beta_{3})(2\pi)^{b-1}}\sum\limits_{m_{0}=0}^{k} \left(\begin{array}{c}k\\ m_{0}\end{array}\right)\left(\sum\limits_{n=1}^{\infty}\frac{\left(-1\right)^{ n}}{n}\right)^{k-m_{0}}\\ \times\left(\frac{W_{f}}{\ln 2}\right)^{k-m_{0}}c^{m_{0}}T^{kn-m_{0}n}G_{b+1, 3b+1}^{3b,1}\left[\begin{array}{c}D_{3}^{b}T\\ \bar{b}^{2b}\mu\tau\end{array}\right]\left.\begin{array}{l}m_{0}n+1-kn,\ E_{5} \\ E_{6},m_{0}n-kn\end{array}\right]\end{array}  \tag{25}\]  
Proof. : Please refer to Appendix A. \(\square\)  
The CDF of \(C_{t}\) is calculated by  
\[ \begin{array}{l}F_{C_{t}}(c)=\int_{0}^{c}f_{C_{t}}(x)dx\end{array}  \tag{26}\]  
The final expression of \(F_{C_{t}}(c)\) is given by  
\[ \begin{array}{l}F_{C_{t}}(c)=\frac{\xi_{3}^{2}\lambda b^{\mu_{3}+\beta_{3}-2 }}{\Gamma(\alpha_{3})\Gamma(\beta_{3})(2\pi)^{b-1}}\sum\limits_{m_{0}=0}^{k} \left(\begin{array}{c}k\\ m_{0}\end{array}\right)\left(\sum\limits_{n=1}^{\infty}\frac{\left(-1\right)^{ n}}{n}\right)^{k-m_{0}}\left(\frac{W_{f}}{\ln 2}\right)^{k+1}\sum\limits_{h=0}^{\infty} \frac{\left(-1\right)^{h}}{h!}\\ \times\left(\sum\limits_{z=1}^{\infty}\frac{\left(-1\right)^{z-1}}{z}\right)^ {m_{0}+h}T^{kn-m_{0}n+m_{0}z+hz+1}G_{b+2,3b+2}^{3b,2}\left[\begin{array}{l}D_ {3}^{b}T\\ \bar{b}^{2b}\mu\tau\end{array}\right]\left.\begin{array}{l}m_{0}n-kn-m_{0}z- hz,m_{0}n+1-kn,E_{5}\\ E_{6},m_{0}n-kn,m_{0}n-kn-m_{0}z-hz-1\end{array}\right]\end{array}  \tag{27}\]Proof. : Please refer to Appendix A for the derivation for \(F_{C_{t}}(c)\) and please refer to Appendix B for the interval of convergence for \(F_{C_{t}}(c)\). \(\square\)
## 3 Results and Discussion
### Effect of Process Variables on the Lignin Yield  
In order to observe the experimental effects of all the three lignin isolation variables, 3D response surface graphs were also drawn (Figure 1). These plots exhibit the interaction of two variables while keeping the third at the center value. In Figure 1A, the effect of solids loading and time while keeping catalyst dose at a fixed value is shown. During the lignin isolation process, an elevated time and solids loading both led to a corresponding rise in the yield of lignin. However, after a certain time, a further increase of these variables resulted in less lignin yield. Moreover, in short time of the process no significant difference in the lignin yield was observed when then solids loading was varied \(t\) between their range of 5-15% \(w/w\). Similarly, for the other two variables, the trend kept its similar behavior trend (Figure 1B,C).  
Figure 1: 3D response surface plots for interactive behaviour of process variables: (**A**) solids loading and time, (**B**) catalyst dose and time, and (**C**) catalyst dose and solids loading.  
As it is obvious from the figures, as solids loading and catalyst dose concentration rose, the yield concomitantly rose and then it decreased, indicating that neither harsh nor overly mild conditions had positive impacts on the lignin yield. However, the variables time and the catalyst strongly influenced the response (lignin yield) compared to the interaction of the process variables with solids loading, in our case only two process variables significantly contributed to the significance of quadratic model (value lower than \(p=0.05\)); from a point of view of the interactive effect of process variables, time and catalyst dose have had a significant role on the lignin yield and this similar trend was also observed in our preliminary trials. From here, it can be anticipated that under our prescribed range of factors time and catalyst dose played a significant role to have a substantial impact on the lignin yield.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 3 Energy Management
### Alternative Energy Sources
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Generator** & **Benefits** & **Limitations** & **SoTA for Mars** \\ \hline \multirow{6}{*}{Solar Panel} & \(\bullet\) Inexpensive & \(\bullet\) Accumulations of dust & \\  & \(\bullet\) Interspensive & cover impacts efficiency & \\  & \(\bullet\) Portable & \(\bullet\) Frequent maintenance & \\  & \(\bullet\)High power conversion & required & Used for Mars \\  & \(\bullet\) efficiency (up to 28\%) [12] & \(\bullet\) Decreased radiation & \\  & \(\bullet\) Mature technology & intensity—36 to 52\% & \\  & & compared to Earth [39] & \\ \hline \multirow{6}{*}{RTG} & \(\bullet\) All-weather operation & \(\bullet\) Uses radioactive & \\  & \(\bullet\) All-weather operation & materials & \\  & \(\bullet\) Minimum or no & \(\bullet\) Influence on local & \\  & maintenance required & environment and acquired & Used for Mars \\  & \(\bullet\) 6–7\% of efficiency [21] & data & \\  & & \(\bullet\) Not suitable for & \\ \hline \multirow{6}{*}{Ambient Heat-TEG} & \(\bullet\) No environmental impact & \(\bullet\) Low efficiency & \\  & \(\bullet\) Least impacted by & \(\bullet\) Low power density & Still in research \\  & \(\bullet\) Least maintenance & \(\bullet\) Portability depends on & phase \\  & required & the choice of heat source & \\ \hline \multirow{6}{*}{TENG} & \(\bullet\) No environmental impact & \(\bullet\) Low efficiency & \\  & \(\bullet\) Dust storms are & \(\bullet\) Impact by weather & \\ \cline{1-1}  & \(\bullet\) advantageous & conditions & Still in research \\ \cline{1-1}  & \(\bullet\) Lightweight & \(\bullet\) Not a well-established & phase \\ \cline{1-1}  & & technology & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of conventional and alternative energy sources and generators for Mars exploration.
# Exploring CP Array Antenna Characteristics and Its Applications to a Deployable Structure
## 2 Design and Experimental Results
### Circularly Polarized Antenna Design
#### 2.2.1 Single Radiator and Feed Line Designs
Lastly, two large panels, each identical to the initially designed 8 \(\times\) 8 array antenna, are connected using deployable hinges. Each configuration corresponds to the mounting configurations previously described in Figure 1.
## 2 FRCCs-Based Electrical Impedance Acquisition System and Crack Localization Algorithm
### Preparation of Fiber-Reinforced Cemertitious Composites  
Four FRCCs specimens are basically made by adding steel fibers to the mortar matrix, making it possible to have conductive nature. The mortar matrix used in this study is composed of water, Portland type I cement, sand and superplasticizer as summarized in Table 1. Here, the grain sizes are less than 5 mm. The fine aggregate is prepared as the surface-dry saturated condition. The steel fibers have two different diameters of 0.3 mm and 0.2 mm, and their lengths are 30 mm (long fiber) and 19.5 mm (short fiber), respectively. The short fiber increases the electrical sensing capacity of FRCCs, while the long fiber enhances the mechanical strength of FRCCs [46]. Each type of fibers is mixed as 12.5% of cement weight ratio, and total steel fibers for the FRCCs specimen is 25% of cement weight ratio. For better workability between mortar matrix and steel fibers, 0.2% poly naphthalene sulfonate superplasticizer with respect to the cement weight is used in this study. Note that the four specimens have the same water-to-cement ratio and the aggregate-to-cement ratio of 0.45 and 1, respectively.  
First, the mortar matrix and steel fibers are mixed during 5 min using a planetary mixer. Then, it is placed in a cuboid-shaped mold on the vibrating table for eliminating cavity inside FRCCs. The cuboid-shaped mold having the dimension of 50 \(\times\) 40 \(\times\) 400 mm\({}^{3}\) is fabricated for curing of FRCCs. As shown in Figure 1, the five copper electrodes with a diameter of 1.78 mm and a length of 70 mm are embedded in FRCCs with intervals of 50 mm from the center location of FRCCs. For the crack localization of FRCCs, experimental regions are divided into four different regions defined as Regions A, B, C and D. After mixing, the FRCCs specimens are cured at a constant temperature of 20 \({}^{\circ}\)C and a relative humidity of 80% in a thermo-hygrostat for 24 h. After demolding, the FRCCs specimens are water-cured at a room temperature for a week.
## 6 Case Study: Application of the Method on LoA 4 Valet Parking with V2X
### Collaborative Driving--AVP  
Collaborative driving in AVP is possible both by V2I and V2V communication, noting that the former approach may partially or fully fall into the area of decision making. However, collaboration is also possible on the V2V level by sharing sensor data and aiding local trajectory planning or evasion maneuvers, offloading the bandwidth load on the communication between the actors and the infrastructure. The information shared this way may include raw sensor data, (processed) information of the 3D environment, driving intentions, emergency status or other non-driving related information, such as outdoor conditions. Collaborative driving also helps the local solution of deadlock situations and facilitates the optimal use of parking slots in terms of fuel efficiency, time and other factors.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 2 Corrosion Mechanism and Experimental Study of Anchor Bolts
### Electrochemical-Accelerated Corrosion Experiment  
Electrochemical-accelerated corrosion is a method used to simulate the corrosion process and accelerate the corrosion rate of the anchor bolt. In this study, an anchor bolt with a length of 1.5 m was selected, with a free segment length of 50 cm and an anchored segment length of 100 cm. An anchoring agent with a length of 8 cm was applied in the middle of the anchored segment prior to the experiment. After curing the sample, the bolt specimen was submerged in a 3.5% NaCl solution for a duration of 12 h to ensure the full penetration of the NaCl solution into the anchoring agent. Subsequently, the anchor agent was also immersed in a 3.5% NaCl solution to facilitate accelerated corrosion testing. The choice of 3.5% NaCl concentration is widely adopted by researchers due to the solubility properties of NaCl, aligning this range with safe laboratory conditions and reflecting real-world scenarios. The corrosion period was set at 12 h, maintaining room temperature throughout. Following corrosion, the anchor was retrieved and subjected to surface cleaning before undergoing ultrasonic guided wave testing. A total testing duration of 72 h was conducted to replicate potential corrosion conditions that the bolt might encounter in an authentic environment. The specimen is shown in Figure 2. The connection for the electrochemical-accelerated corrosion test is illustrated in Figure 3.  
In this study, a DC stabilized power supply with an adjustable range of 0-32 V and 0-3 A was used to achieve the accelerated corrosion process. Each specimen was subjected to a constant current of 2.5 A for corrosion. Specifically, the anchor bolt was used as the anode, and a copper rod was used as the cathode. A DC regulated power supply was employed to apply the corrosion current, with a current density of 40 mA/cm\({}^{2}\) on the surface of the anchor bolt. During the testing process, water was periodically replenished, while maintaining a constant NaCl solution concentration.  
Figure 2: The corroded anchor bolt specimen.
# Wheeled Permanent Magnet Climbing Robot for Weld Defect Detection on Hydraulic Steel Gates
## 4 Experimental Validation
### Weld Defects Detection
#### 4.1.2 Model Training and Testing  
After data augmentation, 1900 images were randomly selected from the weld surface defect dataset as the training set, 400 images were used as the validation set, and the remaining 100 images served as the test set. The neural network classification model was trained based on the YOLOv5 object detection framework. The training parameters were configured as follows: the learning rate was set to 0.001, the batch size was 4, and the number of training epochs was 200. The variation in the loss function value during the model training process is illustrated in Figure 12. The final loss function value converged to approximately 0.02.  
The trained model was employed to detect the defect types in the input images, and the detection results are illustrated in Figure 13. The detailed detection performance for various types of weld surface defects is summarized in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Categories of Weld Defects** & **Number** \\ \hline Undercuts & 2236 \\ Pores & 1511 \\ Weld bumps & 2648 \\ Weld pits & 1601 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of defect samples after data augmentation.  
Figure 12: Loss function in the model training process.  
Figure 13: Image of defect detection test.  
As shown in Table 2, the defect type detection model exhibits relatively high accuracy and demonstrates favorable detection performance.