esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 5 Results
### Experimental Result
#### 5.2.1 Comparison of Algorithm Optimization Performance  
The algorithm using Equation (9) as the bidding function is represented as c-CBBA (mix), while the algorithm using task profit as the bidding function is represented as c-CBBA (profit). The two are compared with the classical centralized-distributed algorithm Contract Network Protocol (CNP), which uses the task profit as the bidding function. In CNP, the master satellite bids for each task in the descending order of priority. Table 7 shows the comparison of various indicators of the three algorithms in a total of 18 scenarios with different task distributions, task scales, and satellite scales (the highest total profit is in bold). Table 7 compares the algorithms in terms of three aspects: the number of tasks successfully scheduled (TS), the total profit (TP), and the running time of the algorithms. For the convenience of expression, the following text uniformly refers to scenarios in the form of \"task type--number of tasks--number of satellites\". For example, using \"local-360-30\" to refer to a scenario where the task is locally distributed, the number of tasks is 360, and the number of satellites is 30.  
Figure 9: Task profit decay function over time \((\lambda=10^{-5})\).  
As can be seen from Table 7, in most cases, c-CBBA (mix) significantly outperforms the other two algorithms in indicators such as the number of scheduled tasks and the total profit. This is because the bid value is more global when considering storage, conflicts, etc., which is conducive to achieving an overall better performance. The scheduling effect of c-CBBA (profit) is slightly better than that of CNP. In 18 scenarios, nine times of c-CBBA (profit) are better, seven times of CNP are better, and the other two have the same effect. Both have the same bidding function, but the reason for the difference in scheduling effectiveness is that CNP only bids for one task at a time, while CBBA bids for a group of tasks simultaneously. Relatively speaking, CBBA has a more global perspective. However, due to c-CBBA (profit) only considering benefits and not considering the mutual influence between tasks during actual scheduling, the results are poor.
2. When communication time is not factored into the time cost, the CPU time required for both c-CBBA and CNP operation is not high. This indicates that the algorithm does not incur excessive computational burden and is therefore suitable for on-board operation.  
The bid function of the basic CBBA and c-CBBA in the following experiment will be based on Equation (9).
## 4 Simulation Analysis
### Number of Dead Nodes
Similarly, we recorded the average operational time for the varying transmission ranges and show them in Figure 19. It is evident from the figure that operational time of the proposed scheme slightly increases as the transmission range increases. However, no marginal variation in the operational time for different network sizes was observed for the EDOVE. On the other hand, a significant change in the operational time of WDFAD-DBR was observed. It demonstrated the large operational time for the smaller The reason of the large operation time for smaller network size is that it generates less data packet broadcast. However, the simulation results demonstrate that overall operational time of the network using EDOVE is larger than by using the WDFAD-DBR protocol. The operational time of the EDVOE is almost 40 s more than of the WDFAD-DBR. The overall improvement of the proposed EDOVE in contrast to the W  
Figure 19: Average network operational time versus the transmission range. Figure 18: Average network operational time versus the network sizes. It has been evident from all the previous results and discussion that EDOVE has high PDR, less data broadcast, consumes less energy, fewer number of dead nodes, and has large operational time. Next, we investigate the average number of hops the data packet is forwarded from the source to sink node(s). Figures 21 and 22 show the average number of hops for different network sizes and transmission ranges, respectively. These figures show that EDOVE has a slightly larger number of hops than the WDFAD-DBR. The reason for this slightly larger number of hops in EDOVE is that it focuses on the reduction of network's energy consumption and increase network lifetime. This is achieved by EDOVE because it uses node's residual energy, the variance of the neighboring nodes' depths and depth differences between the sender, receiver, and receiver's next hop forwarders. On the contrary, the WDFAD-DBR just considers the depth difference to select the next data packet forwarder, which reduces the number of hops. This is the reason that WDFAD-DBR has less End-to-End delay compared to EDOVE, refer  
Comprehensively, the proposed EDOVE scheme improves the PDR, reduces the data packet broadcast, conserves more energy which in turn reduces the number of dead nodes, and has more operational time than the WDFAD-DBR. However, it has slightly longer end-to-end hop distance and slightly larger delay, which is the tradeoff of the proposed scheme or may be any scheme proposed for the UASNs. Figure 24: End-to-End delay between Source node that generated data message and Sink node versus the transmission range. Figure 23: End-to-End delay between Source node that generated data message and Sink node versus the network sizes. Figure 22: Average No. of Hops data messages traversed versus the transmission range.
## 3 Results and Analysis  
### Kinematic Model Fitting  
The design parameters of the pitch and roll layers of the heavy-load double swing table are shown in Table 1, and the positive kinematic models can be obtained by substituting the parameters as follows:  
\[\hat{x}_{4}=\arccos\left(1.009-\frac{s_{4}^{2}}{179200}\right)-0.1894\pi \tag{14}\]  
\[\hat{y}_{5}=\arccos\left(1.014-\frac{s_{5}^{2}}{184800}\right)-0.1552\pi \tag{15}\]  
In order to facilitate the control of the table, a linear fit of Equations (14) and (15) was performed using the least squares method over the stroke range of the hydraulic cylinder, as shown in Figure 7, and the fitted positive kinematic model is:  
\[\hat{x}_{4f}=0.20555s_{4}-0.2055\pi \tag{16}\]  
\[\hat{y}_{5f}=0.20598s_{5}-0.1778\pi \tag{17}\]  
The fitting coefficient of determination \(R^{2}\approx 1\). The maximum residual of the pitch angle is 0.03%, and the maximum residual of the roll angle is 0.48%, which indicates that the linear fitting is better and can satisfy the motion control accuracy requirements of the table.  
Since the motions between the pitch and roll layers are independent of each other, the ideal inverse kinematics model for both can be derived from the fitted linear positive kinematics model as  
\[s_{4}=4.865\hat{x}_{4f}+179.985 \tag{18}\]  
\[s_{5}=4.8559\hat{y}_{5f}+155.349 \tag{19}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Pitch Layer**} & \multicolumn{3}{c}{**Roll Layer**} \\ \hline
**Parameters** & **Value/(mm)** & **Parameters** & **Value/(mm)** \\ \hline Radius of turn, \(r_{4}\) & 280 & Radius of turn, \(r_{5}\) & 280 \\ Hinge point radius, \(m_{4}\) & 320 & Hinge point radius, \(m_{5}\) & 330 \\ Initial stroke, \(s_{4\text{m}}\) & 180 & Initial stroke, \(s_{5\text{m}}\) & 155 \\ Maximum stroke, \(s_{4\text{max}}\) & 204.3 & Maximum stroke, \(s_{5\text{max}}\) & 179.4 \\ Minimum stroke, \(s_{4\text{min}}\) & 155.6 & Minimum stroke, \(s_{5\text{min}}\) & 130.8 \\ Center radius, \(l_{4}\) & 210 & Center radius, \(l_{5}\) & 220 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanism parameters of pitch and roll layers.  
According to the positive kinematic model of the mechanism, the independent errors of pitch and roll angles can be expressed as  
\[E_{x}=\frac{\hat{x}_{4}^{\prime}-\hat{x}_{4}}{\hat{x}_{4}}\times 100\% \tag{20}\]  
\[E_{y}=\frac{\hat{y}_{5}^{\prime}-\hat{y}_{5}}{\hat{y}_{5}}\times 100\% \tag{21}\]
## 3 Results and Discussion
### Analysis of Contour Reconstruction Reliability for \(S01\)
Overall results showed that an appropriate selection of \(S_{f}\) and \(T\) was required to achieve the best coverage and accuracy results. If a complete coverage was prioritised against accuracy, it could be achieved under many test combinations. Nevertheless, only a small fraction of such combinations would simultaneously provide the lower range of \(\overline{D}\) and \(\sigma_{D}\) values. Simultaneous optimisation under both coverage priority and accuracy priority criteria was not possible for almost all tested edge-detection methods (Table 3). In fact, only the Canny method provided a combination of \(S_{f}\) and \(T\) (\(T=110;\ Sf=19\)) leading simultaneously to the best results of \(C_{f}\) (100%), \(\overline{D}\) (6.65 um) and \(\sigma_{D}\) (7.65 um). Nevertheless, Canny  
Figure 8: MQI results as a function of \(S_{f}\) calculated with ZeroCross for \(SO1\) in a reduced \(T\) range. would not be the best option for the S01 scenario, because ApproxCanny provided a slightly lower \(\sigma_{D}\) value (7.50 \(\upmu\)m) under the coverage priority criterion, and Prewitt provided better results under the accuracy priority criterion (\(C_{f}=93.6\%\); \(\overline{D}=6.12\upmu\)m; \(\sigma_{D}=5.00\upmu\)m).
## 4 Discussion
### Mission Operations Issues and Status
#### 4.2.2 DLP Failure  
A problem with the power line that feeds the DLP measurement system was encountered, making it impossible for the 5 V line to sustain its nominal output level. The root cause of the problem was identified as activation of the overcurrent protection due to the high-frequency transient from the inrush current of the DLP DC/DC converter. During the thermal-vacuum (TVAC) tests, the problem appeared for the first time in the EM model of the satellite. The issue was solved by reducing the amplitude of the inrush current through the use of an in-series resistor with the power line output from the EPS. However, once in orbit, it is suspected that changes in resistance of the used resistor (less than 1 \(\Omega\) resistor value) triggered by temperature variations with respect to ambient conditions on-ground made the problem reappear.  
Comparison tests performed with the EM and FM spare models of the DLP and the EM regulation board from the EPS showed a discrepancy in the inrush current of the DLP DC/DC converter between the EM and FM versions. Even though the difference in the inrush current from EM to FM translates to a difference of the in-series resistor of 0.3 \(\Omega\), it is high enough to trigger the OCP in the FM EPS DLP power line. During the calibration tests and the development of the flight software, the DLP instrument was powered by a workbench power supply adjusted to the same current limit as the DLP line from the EPS (1.5 A). The nominal current consumption was 0.27 A for both the EM and FM hardware. This caused the inrush current issue not to be identified at an earlier stage.  
If re-work of the satellite were possible, a power regulation board (REG_PL) that accounts for the non-linear nature of different loads should be designed to avoid similar issues. As for addressing the issue of the unit currently in orbit, temperature variations will cause the in-series resistor to change its value. Therefore, it is possible that the DLP instrument could be operated at certain times during the lifetime of the spacecraft, as the orbit will naturally evolve and the thermal environment inside the satellite will vary accordingly.
## 4 Thermographic Techniques for Breast Cancer Detection
### Dynamic Infrared Thermography
#### 4.2.1 Cold Stimulus and Patient Comfort  
Contrarily to steady-state thermography, patient comfort related to the application of cold stress is a crucial aspect of dynamic breast IR imaging. Indeed, to improve the thermal contrast between the cancerous and healthy breast, a cold air flow typically between 5 \({}^{\circ}\)C and 15 \({}^{\circ}\)C is often blown to the patient's breast while holding their hands on the head [13]. Also, the cooling period ranges between 2 min and 6 min to create a sufficient thermal contrast to detect tumors at depths less than 15 mm. Obviously, a longer cooling period yields a higher thermal contrast but more discomfort for the patient. Contact cooling methods are also applied in fewer cases.  
According to the numerical results provided by [PERSON] et al. [53] to ensure both a good thermal contrast and better patient comfort, cold air flow at temperatures just below the ambient temperature can be applied for less than 1 min.  
A numerical study to optimize the cooling stress applied for early detection of breast cancer from dynamic IR images was performed by [PERSON] and [PERSON] [54]. Thermal contrasts observable when applying a cold stress for a duration of 15-25 min at a constant cooling temperature of 5-15 \({}^{\circ}\)C on the skin surface were analyzed using a 2D breast model by simulating tumors of different sizes, depths, and locations. The authors reported that to achieve the maximum thermal contrast for deeper and smaller tumors, the tissue should be cooled for 5-15 min; also, this maximum appears after 20-45 min during the thermal recovery phase [54]. Notwithstanding, the thermal signature of smaller tumors close to the muscle layer can be covered by the underlying layers at core body temperature, limiting the ability to detect lesions.  
The efficacy of dynamic cooling was tested by [PERSON] et al. [55] using a gel phantom mimicking the human breast affected by a tumor. A depth-adjustable heat source (a resistor having dimensions of 2 mm \(\times\) 1 mm) simulating a tumor was placed inside the gel phantom, in which three temperature sensors were also embedded. The temperature of the phantom was maintained at 37 \({}^{\circ}\)C through a heating plate placed underneath, whereas the simulated tumor was kept at 39 \({}^{\circ}\)C. A cold stimulus was simulated using a glass container filled with water at 18 \({}^{\circ}\)C. After its removal, the thermal recovery of the phantom was recorded by an FLIR A65 IR camera. The authors found that although the tumor depth affected  
Figure 6: Breast infrared image of a patient with a high asymmetry, as highlighted by the circles drawn in the figure, from Ref. [52]. (permission asked and obtained from Elsevier, Amsterdam, The Netherlands).  
the temperature spread on the phantom surface, the mimicked tumor was detectable at different depths. Additionally, they reported that a cooling time of 3 s was sufficient to induce cold stress on the phantom.
# Multi-Pass Stamping Forming a Concave Ring
###### Abstract  
To improve the production quality and efficiency of the concave ring, a multi-pass stamping technology is used to replace the spinning technology to form the concave ring. First, the multi-pass stamping dies are designed according to the structural characteristics of the concave ring. Then, the process parameters are determined based on the bending conditions and the dimensional requirements of the concave ring. Next, the rationality of the designed stamping dies is verified by analyzing the stress and strain field of the multi-pass stamping concave ring. Finally, the stamping experiments are carried out. Experimental results show that the dimensional error of the concave ring formed by multi-pass stamping is less than 0.5%. The multi-pass stamping technology is feasible to form the concave ring. The reasonable design of the mold is the key factor to prevent defects in the workpiece. The concave ring formed by multi-pass stamping meets the industrial requirements.  
concave ring; multi-pass stamping; mold design; bending forming +
Footnote †: journal: Applied Sciences
# Consumer Characterization of Wet- and Dry-Aged Mutton Flavor Profile Using Check-All-That-Apply
## 3 Results
### Chemical Analysis Results
#### 3.4.2 Fatty Acid Methyl Esters (FAME) Results  
The FAME results are shown in Table 5; methyl octanoate, methyl erucate and methyl lignoacetate were not detected in either sample. There was no difference in the methyl docosanoate levels between the two treatments (\(p\) = 0.350). All other FAME values differed between the treatments (\(p\) < 0.05 for all). Methyl decanoate, methyl laurate, methyl tertrade-canoate, methyl palmitate, methyl palmitate, methyl linoleate, methyl arachidate and methyl linolenate were higher in the dry-aged patties compared to the wet-aged patties, whereas methyl octadeconate and cis-9-oleic acid methyl ester were higher in the wet-aged patties compared to the dry-aged.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Wet-Aged**} & \multicolumn{2}{c}{**Dry-Aged**} & \multirow{2}{*}{**t\({}_{\textbf{d}}\)**} & \multirow{2}{*}{**_p-_**Value**} \\  & **Mean** & **S.D.** & & & & **Value** \\ \hline pH raw patty & 6.46 & 0.046 & 6.56 & 0.023 & \(-\)3.13 & 0.035 \\ moisture raw & \multirow{2}{*}{65.4} & \multirow{2}{*}{1.50} & \multirow{2}{*}{63.4} & \multirow{2}{*}{1.36} & \multirow{2}{*}{\(-\)1.69} & \multirow{2}{*}{ns 1  } \\  & & & & & & \\ \hline pH raw patty & 6.46 & 0.046 & 6.56 & 0.023 & \(-\)3.13 & 0.035 \\ moisture raw & \multirow{2}{*}{65.4} & \multirow{2}{*}{1.50} & \multirow{2}{*}{63.4} & \multirow{2}{*}{1.36} & \multirow{2}{*}{\(-\)1.69} & \multirow{2}{*}{ns 1 } \\  & & & & & & \\ \hline \end{tabular}
\end{table}
Table 4: Effect of ageing method (dry vs. wet), standard deviation (S.D.), t-value (t) with subscript degrees of freedom and statistical significance (\(p\)-value) on total moisture and pH of muton patties.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**CATA Term**} & \multicolumn{2}{c}{**Frequency Count**} & \multirow{2}{*}{**_p-Value_**} \\  & **Wet-Aged** & **Dry-Aged** & \\ \hline Bamyard & 10 & 4 & 0.058 \\ Livery & 11 & 9 & ns 1  \\ Sheepy & 45 & 34 & 0.041 \\ Juicy & 38 & 39 & ns \\ Sweet & 9 & 13 & ns \\ Earthy & 27 & 23 & ns \\ Metallic & 4 & 0 & 0.046 \\ Savory & 18 & 26 & ns \\ Roasted & 19 & 32 & 0.006 \\ Acidic & 3 & 1 & ns \\ Fatty & 38 & 31 & ns \\ Fishy & 2 & 1 & ns \\ Caramel & 0 & 6 & 0.014 \\ Dairy & 2 & 3 & ns \\ Sour & 6 & 1 & 0.059 \\ Green/Grassy & 9 & 8 & ns \\ \hline \end{tabular}
\end{table}
Table 3: Effect of ageing method (dry vs. wet) on frequency of selection (count) of check-all-that-apply (CATA) descriptor terms for cooked muton patties and statistical significance (\(p\)-value).
# Mechanical Properties of White Metal on SCM440 Alloy Steel by Laser Cladding Treatment
## 2 Materials and Methods
### Test Methods  
The laser cladding machine was composed of a coaxial-type laser head and nozzle (maximum power = 2.0 kW, powder feed rate = 5-200 g/min and beam size = 8 mm \(\times\) 3 mm). All tests were performed in an argon atmosphere to prevent oxidation. Table 2 outlines the major white metal cladding process parameters. White metal begins to melt above 240 \({}^{\circ}\)C and completely melts above 354 \({}^{\circ}\)C. Therefore, the melting point for the SCM440 alloy steel bearing base material (1416 \({}^{\circ}\)C) had a temperature difference of about 1100 \({}^{\circ}\)C [18]. For hardness testing, an automatic hardness testing system (AMT-X7 FS, Matsuzawa) was used. To analyze the hardness change, dependent on the depth of the cladding layer and substrate, the cladding layer was measured 5 times, and the substrate was measured 15 times, moving in units of 1 mm \(\times\) 0.1 mm. For material composition analysis, field emission SEM (SU8220, Hitachi) was used to observe the structure and cladding layer morphology, and EDS analysis was performed to determine the components of the cladding layer, base metal and heat-affected zone (HAZ). Friction testing was performed using a ball-on-disc-type tribometer (maximum load = 30 N and maximum speed = 600 rpm). Three measurements were performed per condition to ensure the reliability of the test results. Steel with a diameter of \(\Phi\)8 mm was used as the bearing material. Wear testing specimens (20 mm \(\times\) 6 mm \(\times\) 8 mm) were prepared, and each cladding layer surface was subjected to sandpaper grinding and automatic polishing in the same manner as the SEM specimens. The specimens, thus fabricated, were tested under the loading condition of 20 N and a rotational speed of 200 rpm for 3600 s. Additionally, this study performed XRD analysis using the X'Pert Pro-MPD by PANalytical to analyze the components of the powders generated during the white metal treatment and change to the crystalline structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sn** & **Sb** & **Cu** & **Bi** & **Pb** & **Al** \\ \hline
88.7 & 7.8 & 3.5 & 0.02 & 0.01 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition (%) of white metal powder.  
Figure 1: Scanning electron microscopy (SEM) image of white metal at magnifications of (**a**) \(\times\)100 and (**b**) \(\times\)10,000.
## 3 Results and Discussion  
### State of the Art Regarding This Implant  
A total of forty-nine articles were found in scientific databases fulfilling the inclusion criteria. An additional fourteen files were found through a manual search. After excluding nineteen duplicates, twelve papers not analyzing Prama implants, three conference abstracts, and one paper with inaccessible data (closed access), a total of twenty-eight papers were screened. Of these, twelve were not analyzed because they were not clinical studies (six were laboratory studies, and six were case reports/case series). Among the remaining sixteen studies [8; 17; 19; 20; 21; 22; 23], we selected only the most recent investigations from each research group, resulting in a final analysis of nine studies (Table 1) [17; 20; 22; 25; 27; 28; 30]. Due to the limited presence of prospective case control studies with comparable outcome measures (only three papers were eligible), it was not possible to perform a meta-analysis of the current evidence. The majority of these studies had a prospective design, two being ambispective studies, one being a pilot study, and one being a randomized clinical trial. The studies included a total of 538 implants. The follow-up ranged from 1 to 6 years.