esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Darwin  
The Infrared Space Interferometer program [5], known as Darwin, was proposed and implemented by the European Space Agency (ESA) in 1993. It is composed of 4-9 spacecraft.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Formation Flying Type** & **Mission** & **Technical Features** & **Control Precision** \\ \hline \multirow{4}{*}{Precision Formation} & Three-dimensional stereo & Through autonomous & \\  & imaging, virtual telescope, & navigation and precision & \\  & synthetic aperture radar, & control technology, precise & Long-term continuous \\  & synthetic aperture optical & shapes and poses can be & precision measurement \\  & interferometry, gravitational & maintained between & control \\  & wave detection & spacecraft over time & \\ \hline \multirow{4}{*}{Knowledge Formation} & Ground-probing, & Mutual measurements exist & \\  & Intermittent & between the spacecraft, but & \\  & three-dimensional passive & coupling control is not & \\  & navigation & required & \\ \hline \multirow{4}{*}{Collaborative Formation} & Rendezvous docking, twin & Spacecraft maintain relative & \\  & formation flight and & measurement and control for & Precision measurement \\  & fly-around & short periods of time during & control stages \\ \hline \end{tabular}
\end{table}
Table 1: Example of spacecraft formation flying.  
Figure 1: Diagram of the sparse aperture observation.  
The mission is to search for Earth-like planets among the stellar planets, determine the composition of planetary atmospheres, and infer the possibility of signs of life from them. Darwin can achieve high spatial resolution in the infrared wavelength region and is the first program to search for an exoplanet. The optimal formation of spacecraft was determined as follows: four spacecraft (the collecting spacecraft, CS) are configured with 3-4-meter astronomical telescopes forming a quadrilateral (the Darwin Emma X-array), and one spacecraft (the beam-combiner spacecraft, BCS) focusing the beam, as shown in Figure 2. In order to meet the requirements of interferometry, the formation is located at the L2 point where the spacecraft hovers under force balance; it is necessary to maintain high-precision control during maneuvering. The project was canceled in June 2011 for technical and budgetary reasons.
## 4 Numerical Analysis of Case Study
### The Evolution of the Global Safety Factor with the Excavation Load Releasing Ratio  
After each step of the excavation load releasing, the \(F\) of 40 monitoring objects are recorded in Figure 7. With the completion of excavation, the point safety factors around the waist continue to decline, while the factors around the crown and the bottom show a dramatic growth before the continuous decrease. When \(r\) reaches 60%, the safety factors at tunnel waist are the first to be less than 1, and half of the monitoring objects are in the state of instability.  
Figure 6: Computational model: (**a**) the extent of model, (**b**) boundary conditions, (**c**) the details of the monitoring section, (**d**) the reference number of the monitoring object.  
Figure 7: Evolution of point safety factor with the excavation load releasing, number of 1–40 showing the elements’ location around a boundary.  
Considering that the structure and the stress state are symmetrical, the point safety factors of monitoring points in Figure 6d from 1 to 20 after each step of excavation load releasing, which are denoted as \(\mathbf{F}_{1}^{i}\), \(\mathbf{F}_{2}^{i}\),..., \(\mathbf{F}_{20}^{i}\), are recorded in Figure 8. All these data are contained to a certain range and the degree of dispersion reaches the maximum when the excavation load releases around 70%. Then, at each step, the global safety factor, \(\overline{\mathbf{F}}\), can be calculated by Equation (9) and plotted as a red line in Figure 8. As shown in Figure 8, there are two y-axes. The primary y-axis represents the point safety factor, \(\mathbf{F}\), and the secondary y-axis represents the global safety factor, \(\overline{\mathbf{F}}\). For instance, in Figure 8, when \(\mathbf{r}\) is 10%, the point safety factors of 20 monitoring points around the boundary are calculated by Equation (3) and recorded as a scatter plot, of which the maximum value is 1.842 and the minimum value is 1.516. Then, the geometric average upon these 20 points, namely global safety factor, \(\overline{\mathbf{F}}\), is calculated as 1.705 by Equation (9) and plotted as a red line. In this way, the global safety factors at each calculation step are calculated to obtain the \(\mathbf{r}-\overline{\mathbf{F}}\) curve with the downward tendency in Figure 8.
## 3 Bridge Case Study
### Verification of FE Modeling  
To ensure the calculation accuracy of the multi-scale FE model, the calculated axial forces of the upper chord, lower chord, inclined bar, upper stiffening chord, and vertical stiffening bar under the design load were compared with the design data. The design data presented in the figure were all derived from the engineering design drawings. Due to space limitations, only the partial comparison results of the pier tops and mid-span areas in a half-span symmetrical structure are presented. In Figure 5, the position of the bar corresponding to each bar number is indicated by a red circle.  
From Figure 5, it can be seen that the value of each regional component in the design data and the calculation results of the ABAQUS multi-scale mixed unit model is above 0.97, and the coefficient of variation (C.V.) is less than 4%. This demonstrates that the results generated by the ABAQUS model are stable, reliable, and in good agreement with the design data, and the calculation results can meet the precision requirements.  
Figure 4: The multi-scale FE model of the tested bridge.  
Figure 5: _Cont._
## 4 Results
### ANOVA Performance Comparison
By applying Equation (9), the following is obtained: \[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]0 Next, we calculate the Within-Groups Sum of Squares (SSW) using Equation (10) [61] as follows: \[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]1 (10) where \[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]2 is the Within-Groups Sum of Squares; \[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]3 (11) where \[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]4 is the Within-Groups Sum of Squares; \[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]5 (12) where \[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]6  
Next, we calculate the Between-Groups Degrees of Freedom (_df_B), where the number of groups is \(g\) = 2 using Equation (11), and the Within-Groups Degrees of Freedom (_df_W), where a total number of observations is \(k\) = 8 using Equation (12) [61]. \[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]7  
\[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]8  
Next, we calculate the Between-Groups Mean Square (_MSB_) and Within-Groups Mean Square (_MSW_) using Equations (13) and (14) [61] as follows:  
\[Mean_{CWSM}=\frac{99.75+99.50+100.00+99.75}{4}=99.75\]9  
\[Mean_{Overall}=\frac{88.7+81.3+97.6+94.4+99.75+99.50+100.00+99.75}{8}=95.12\]0  
We calculate the _F_-statistic using Equation (15) [61] and determine the critical value for \(F\) at a significance level of 0.05 with dfB = 1 and dfW = 6. We identify the critical value at 5.99 using the Excel function FINV(0.05, 1, 6), where 0.5 is the significance level, 1 is the dfB, and 6 is the dfW. Since the calculated F = 6.668 > 5.99, we reject the null hypothesis. \[Mean_{Overall}=\frac{88.7+81.3+97.6+94.4+99.75+99.50+100.00+99.75}{8}=95.12\]1  
By rejecting the null hypothesis, it is accepted that the CWSM yields superior results compared to similar models in the literature, as evidenced by the comparison with the TrashNet model. The ANOVA statistical analysis validates this comparison.
# Ultra-Wideband Analog Radio-over-Fiber Communication System Employing Pulse-Position Modulation
## 3 Experimental Setup  
A series of captured experimental illustrations of the setup is shown in Figure 3. At the same time, a block diagram of the developed ARoF-based UWB communication system employing PPM is depicted in Figure 4, where the optical part is shown in red, the electrical part is displayed in blue, and the digital part is depicted in gray. The experimental system mainly consists of three parts: (a) ARoF transmitter, (b) RAU transmitter, and (c) RAU receiver. In the experimental setup, the following primary assumptions have been made: firstly, the radio channel is exclusive to our experiment; secondly, both the transmitter and receiver remain stationary; and, thirdly, multipath propagation effects are negligible due to the controlled environment. These assumptions align with the experimental constraints and ensure the system's performance is evaluated close to ideal conditions.
## 3 Results and Discussion
### Discussion  
Equation (1) was used to match the damage-strain curves of fracture-grouted rock specimens during various dry and wet cycles, as shown in Figure 9. The damage growth of fracture-grouted rock specimens tends to slow down as the number of dry and wet cycles rises, which also denotes a change from brittle to ductile rock quality. With the entire process of tight closure of primary cracks in the initial stage of fissure-grouted rock specimens, stable extension of cracks in the elastic stage, and unstable extension of cracks in the elastic-plastic stage to the appearance of macroscopic cracks until damage, the strain-damage relationship of fissure-grouted rock specimens exhibits a good echo of the evolution of damage variables. The time of rapid expansion for damage variables in fracture-grouted rock specimens progresses and the process of damage to the specimens be-comes quick as the number of dry and wet cycles steadily rises. For 20 cycles, the damage of the fracture-grouted rock specimens increased more quickly, with the damage variable rapidly rising to 0.8 at a strain of 0.0075. With five cycles of fracture-grouted rock, the damage growth in rock specimens was slower. The damage progression of the cracked grouted rock specimens in the preceding test section during various dry and wet cycles was verified. The fissure-grouted rock specimen did not tend to be completely damaged because the fissure-grouted rock specimen itself was damaged after the specimen was close to failure, and the higher strength portion of the damage no longer developed.  
Figure 10 shows the theoretical stress-strain curves of fracture-grouted rock specimens with various numbers of dry and wet cycles and test curves in accordance with Equations (5) and (7). The damage model of fracture-grouted rock specimens considering water-rock action indicated in Equation (5) is more logical, as shown by the test curves' overall agreement with the theoretical curves. After dry and wet cycle action, the model may depict the damage evolution process of fracture-grouted rock specimens.  
Figure 10: Theoretical stress–strain curves of fracture-grouted rock specimens with different numbers of dry and wet cycles. (**a**) 0 cycles. (**b**) 5 cycles. (**c**) 10 cycles. (**d**) 20 cycles.  
Figure 9: Strain–damage fitting curves of fracture-containing grouted rock specimens under dry and wet cycles.
# Improving Response Time for Crowd Management in Hajj
## 1 Introduction
This paper proposes an image transmission mechanism called a Priority-Based Routing Framework for Flying Area Networks (PRoFFAN), which will enhance the delivery speed of essential image features from a UAV to the base stations. Our motivation application is crowd management, and we believe that obtaining important image features as early as possible will save lives and enhance the safety and flow of the crowd. In this work, we have simulated the transmission from UAVs to the base-station in Cooja. We implemented our framework in Contiki-NG OS. The simulations were carried out using standard aerial images used in image processing. We used image peak signal-to-noise ratio (PSNR) [3] and structural similarity index (SSIM) [4] to show the benefit of using PRoFFAN as compared to typical Zigbee based communication. As discussed before, the image size can be reduced using image compression. PRoFFAN leverages the ability of image compression algorithms that generate multiple layers of compressed data. The first layer contains the most prominent features of the image, for example, the edges of objects or coarse image data. The subsequent layers contain the details that when merged with the first layer, and restore the original image. Using such image compression algorithms, the response time of FANET applications can be reduced. If the base-station receives the image data required for first pass sooner than data requiredfor subsequent passes, it can start processing the first pass and take action accordingly while the data of subsequent layers arrive. The rest of the paper is organized as follows. The following section discusses related works. Section 3 discusses the PRoFFAN methodology. Section 4 contains details of the experiment setup. Section 5 elaborates the results, whereas the paper is concluded in Section 6.
# Cascaded Nonlinear-Optical Loop Mirror-Based All-Optical PAM Regenerator
###### Abstract  
An all-optical regeneration scheme based on cascaded nonlinear-optical loop mirrors (NOLMs) for pulse amplitude modulation (PAM) signals is proposed. The optimal working point (WP) of every NOLM can be set by means of the normalized power transfer function (NPTF) and normalized differential gain (NDG). As examples, the PAM-4 and PAM-8 regenerations based on cascaded two-NOLMs are demonstrated by optimizing the splitting ratios of the optical couplers and the gain coefficient of the inter-stage matching amplifier, achieving the Q-factor improvements of 25.32 dB and 21.18 dB, respectively. Compared to the conventional regenerator scheme, a 1.69 dB signal-to-noise ratio (SNR) gain is achieved due to the flat power response within the regenerative range.  
all-optical regeneration; pulse amplitude modulation; nonlinear optical loop mirror; noise reduction ratio +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Sensory Analysis of Beef Burgers  
The organoleptic aspects of the different beef burger formulations were analyzed through a sensory analysis with a consumer panel. A total of 25 untrained tasters participated in the analysis; 56% were female and 44% male. Participants ranged in age from 18 to 64 years. Fifty-six percent of the panelists indicated that their frequency of consumption of beef burgers was 'occasional', while 36% and 8% consumed 1-3 times a week and 3-5 times a week, respectively. None of the panelists indicated that they never consumed this food. In addition, 72% of the participants stated that they usually liked this food, while the remaining 28% stated that they 'neither liked nor disliked it'.  
As observed, \"smell\", \"taste\", \"color\", \"appearance\", and \"texture\" parameters of the reformulated samples were highly valued by the panelists. This is in line with the question asked above, highlighting that the overall taste for beef burgers is similar to that obtained in the test. However, no significant differences were found among the different sensory parameters measured (Figure 2A). Juiciness was the lowest-rated parameter in all the formulations, obtaining a score of 3.00-3.16. In addition, it is important to highlight that there were no significant differences in the color of the burgers. This is in accordance with the results of a\", L*, and b* expressed in Table 5, indicating that the initial differences in color disappeared during cooking. There were also no significant differences between beef burgers in overall acceptability and purchase intention (Figure 2B).  
Figure 2: Sensory analysis (**A**), purchase intention and acceptability (**B**), and preference ordination (**C**) of control and reformulated beef burgers. CB: control burger; FRB: fat-replaced burger; FRB-AE: fat-replaced burger with artichoke extract; FRB-TAE: fat-replaced burger with treated artichoke extract.  
In general, all burgers were well rated, obtaining similar acceptability values (3.60-3.72). In addition, the preference ordination yielded similar results, with all four formulations being chosen first in a very proportional ratio, although FBR-TAE was most often chosen as the worst burger (32% of respondents). Sensory results for fat-replaced burgers with oil emulsions and extracts reported in the literature showed similar results. No differences were found in the overall acceptability of burgers with up to 50% of the fat replaced by different emulsions of chia [13], hazelnut [83], or canola [84]. In addition, the sensory effect of artichoke incorporation was also evaluated by Mazzaglia et al. [85] in aubergine burgers, where the addition of 1% and 3% artichoke extract had excellent results in overall acceptability. In summary, positive results were reported for the application of EVOO and artichoke, generating no sensory impact on the cooked burgers.
## 3 Results
### Sensor Node Operation with an Energy Harvesting Scheme as a Power Supply and a Battery as a Backup
By utilizing the capacity of the Lithium-Ion battery and the recorded discharge current, we can estimate the time required for the battery to become  
Figure 11: TI-CC2650 autonomous operation failure, indicated by the non-recovering EH-ON signal measured on the DC2151-A power management board, corresponding to TI-CC2650 Launchpad + internal timer (**a**) and TI-CC2650 Launchpad + TPL5110 timer (**b**). Different colors are employed to differentiate between the two operating configurations. Figure 10: Current traces of BLE packet corresponding to TI-CC2650 Launchpad + internal timer operating at 5 s broadcast time interval (**a**) and TI-CC2650 Launchpad + TPL5110 timer operating at 7 s broadcast time interval (**b**), illustrating the EH-ON signal read directly from the power management source DC2151-A and indicating the used power source. Different colors are employed to differentiate between the two operating configurations. fully discharged. This estimation is performed in accordance with equations such as Equations (3)-(5) [47; 48]. \[C_{battery}=I_{battery}\times t \tag{3}\]  
\[t_{discharge}^{battery-only}=\frac{C_{battery}}{t_{discharge}^{battery-only}}, \tag{4}\]  
\[t_{discharge}^{battery+harvest}=\frac{C_{battery}}{t_{discharge}^{battery+harvest}},\]  
\[r=\frac{t_{discharge}^{battery+harvest}}{t_{discharge}^{battery-only}} \tag{5}\]  
where \(C_{battery}\) is the nominal battery capacity, \(I_{battery}\) is the battery current, \(t\) is the time, \(t_{discharge}^{battery-only}\) is the battery discharge time in the battery-only mode, \(t_{discharge}^{battery-only}\) is the recorded discharge current in the battery-only mode, \(t_{discharge}^{battery+harvest}\) is the battery discharge time in the battery+harvest mode, \(t_{discharge}^{battery+harvest}\) is the recorded discharge current in the battery + harvester mode, and \(r\) is the battery-life-extension coefficient. Table 5 summarizes the battery-life-extension coefficient for the different sensor nodes. To validate our estimation of battery life extension, we compared our estimated results with direct measurements using the MAX17055 XEVKIT Evaluation Kit provided by Maxim Integrated(r), San Jose, CA, USA. The data presented in Table 5 clearly demonstrate the benefits of integrating our energy harvester as a power supply for our system, even in the worst-case scenarios where the system relies solely on the battery. In such situations, the harvester effectively extends the battery life. Our results show that this approach can increase battery life by a factor ranging from 2.8 up to approximately 9 times. This significant life extension feature has a substantial impact, notably reducing the frequency of battery replacements and associated maintenance costs.
## 4 Results and Discussion
### Axial Velocity Distributions  
Figure 13 presents the axial velocity at the crossing section (\(Z\) = 310 mm). The trend appears to be quite symmetrical. The velocities decreased sharply in the radial direction from the wall of the classification chamber. In the near-wall region, they exhibited negative values, meaning that the airflow moved upward and carried quartz particles up before entering the rotor cage (as shown in Figure 3a). Next, they showed positive values and continued to increase at the inlet of the rotor cage. In the blade region, they first became stable. They then decreased gradually at the outlet of the rotor cage. This meant that the material particles entered the rotor cage in a diagonal downward direction. After this, the airflow changed its direction with negative axial velocities when exiting the rotor cage, which caused the fine material particles to move up and discharge easily through the product outlet. Clearly, \(V_{a1}\) was the highest while \(V_{a2}\) was the lowest in both the blade region and the annular region.  
Figure 14a shows that the axial velocities reached positive values; this means that the airflow moved downward, and only \(V_{a2}\) exhibited negative values at the upper part of the rotor. In a similar pattern, from the top surface to the bottom surface of the rotor cage, the axial velocities first increased significantly in the upper part of the rotor cage. After this, they were quite stable. Finally, they fluctuated due to the effects of the large vortex forming in the low part of the rotor cage. In a similar pattern, as shown in Figure 14b, from the top of the classification chamber, the axial velocities first rose significantly. After this, they decreased sharply. A negative sign indicates that the airflow was directed upward in the area near the wall of the separation chamber. At the bottom of the chamber, the airflow changed its direction with positive values of the axial velocities, carrying the material particles to the guide cone. \(V_{a1}\) was the highest, while \(V_{a2}\) was the lowest in both investigated regions, which is in good agreement with the above analysis.  
Figure 12: Comparison of the radial velocity distributions at points near the wall. \(V_{r1}\), \(V_{r2}\), \(V_{r3}\), and \(V_{r4}\) are the radial velocities corresponding to the guide cone with \(d\) of 708, 808, 758, and 658 mm.  
Figure 13: Distributions of air axial velocities at section Z = 310 mm. \(V_{a1}\), \(V_{a2}\), \(V_{a3}\), and \(V_{a4}\) are the axial velocities corresponding to the guide cone with \(d\) of 708, 808, 758, and 658 mm.  
According to the above analysis, the guide cone structure had a significant impact on the velocity field within the industrial classifier. In the following sections, we examine how this impacted the particle flow and classification efficiency of the micron air classifier.