esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
This study explored various scenarios:  
* MDDA was applied to all visible satellites and processed exclusively by the receiver.
* MDDA was integrated into the LC architecture to enhance its overall performance.
* MDDA was also incorporated into the TC architecture to further optimize performance.
* Lastly, all these scenarios were assessed while taking into account the additional constraints posed by restricted receiver channels.  
The MDDA algorithm proposed in this study was applied to all visible satellites, resulting in a significant improvement in position accuracy. The evaluation of the algorithm's performance was based on the comparison of 2D and 3D position root mean square error (RMSE) values. The RMSE was calculated by squaring the differences between the reference solution and the solution obtained from the proposed system and then taking the square root of the mean of these squared errors, as described by [PERSON] et al. [57]. The equation for calculating the RMSE is shown in Equation (55):  
\[RMSE=\sqrt{\frac{1}{k}\sum_{k}\left(p_{k,ref}-p_{k,proposed}\right)^{2}} \tag{55}\]  
where \(k\) is the time epoch number, \(p_{k,ref}\) is the reference position, and \(p_{k,proposed}\) is the position for the proposed system.
## 5 Discussion
#### Limitations  
This study provides an operational method and shows that it is sensible for iMMS to create a new EDP that also prescribes the necessary product development methods. In addition to its findings, the present study, however, has limitations, which must be taken into account when interpreting the results. Such limitations are:  
* Based on the literature analysis, only MID technology and DMT were considered in the survey and during the creation of the use cases.
* The requirements for iMMS and the explanations of the requirements were defined and determined through 17 experts. With a larger population of experts, it is probable that additional requirements need to be identified.
* Even though the use case with the most effort has been conducted through a real case study, not all collected use cases could be conducted and, therefore, could not be validated.
* Detailed use cases were considered in this work, but use cases are often presented in a short and simple way. Thus, collecting detailed use cases remains a challenge.
## 4 Simulation Verification  
The performance of LCARQ was verified by a comprehensive experiment with simulations. Drone-networking cooperative communication tests were organized in the playground of the university before the simulation experiment. The measured data were used as parameters for the simulation. The colors of the 20 images of the playground in Figure 3 are different, which indicates that the images transmitted by the listening nodes can replace the relay nodes. The experiments were performed using the Matlab (2020b) software as a platform, the \"bpsk\" modulation mode was input [32], and the cooperative communication process was simulated, which could be repeated when the simulation was performed.  
\begin{table}
\begin{tabular}{l l} \hline \hline Input: & \\  & \(s\) denotes the source node, \\  & \(d\) is defined as the destination node, \\  & \(r_{i}\) denotes the \(i\)-th optimal relay node, \\  & \(L_{j}\) denotes the \(j\)-th optimal listener node. \\ Output: & \\  & Forwarding node \\  & 1: Cooperative communication divides the nodes into several groups. \\  & 2: The source node transmits the signal and the surrounding nodes receive it \\  & 3: & \(r_{i}\) is selected by link quality comparison \\  & 4: & \(r_{i}=max(h\text{-link})\) \\  & 5: & for \(i=1\): \(n\) \\  & 6: & the optimal relay node is solved iteratively using Equations (4)–(5). \\  & 7: & end \\  & 8: & The \(\text{r}_{i}\) node failed to forward the signal. \\  & 9: & \(L_{j}\) listener node is selected. \\  & 10: & for \(j=1\):\(n\) \\  & 11: & the optimal relay node is solved iteratively using Equations (6)–(7). \\  & 12: & end \\  & 13: & The best forwarding node is determined. \\  & 14: & Stop broadcasting and keep the link. \\  & 15: & Return Forwarding node. \\  & 16: end \\ \hline \hline \end{tabular}
\end{table}
Table 2: Improved algorithms of LCARQ.  
The simulation results were collected in Table 3. The experiment simulates a cooperative communication process with a common communication part in the first phase and different algorithms transmitting signals according to different strategies in the second phase of signal forwarding. The comparison method contains the improved algorithm LCARQ, the relay-node-based forwarding algorithm RCARQ and the dominant algorithm ORCARQ strategy. The experimental metrics contain BER, BER and the auto retransmission time of different algorithms, and the performance of the improved algorithms is analyzed. The performance of the improved algorithm and the comparison algorithm are calculated and the simulation results are compared to prove the practicality and innovation of the improved algorithm.
## 3 Results and Discussion
### Discussion
#### 3.2.1 Analysis of Media Optimization with High-Throughput Bioscreen Study
This can be seen in the results from a significant pairwise comparison of the mean titers from each carbohydrate block: All of the following comparisons were statistically significant (\(p<0.05\)): G vs. SH, G + Y vs. SHH, G vs SHH + Y, G + Y vs SHH + Y, G + Y vs. MSH + Y, GL vs. SHH, GL vs SHH + Y, GL vs. MSH + Y, GL + Y vs. SHH, and GL + Y vs. MSH + Y.  
Glucose and glycerol as carbohydrate sources in rhamnoid production have been extensively shown to produce rhamnoids in titers ranging from 2 to 4.5 g/L [22; 23; 24], which are similar to the titers obtained in this study. Soy hull hydrolysates, which have previously been shown to generate microbially produced biosurfactants, were less efficient in generating rhamnoids as compared to pure glucose- and glycerol-based media (1.5-1.9 g/L). The novel indication of this study was the production of rhamnoids on soy hull hydrolysates and their mimicking sugar  
Figure 8: Forty-eight-hour rhamnoid concentrations in 100 mL shake-flask cultures of each optimized medium at 2.5% total carbohydrate concentration (G = glucose, G + Y = glucose and yeast extract, GL = glycerol, GL + Y = glycerol and yeast extract, SHH = soy hull hydrolysate, SHH + Y = soy hull hydrolysate and yeast extract, MSH = mimic soy hull hydrolysate, MSH + Y = mimic soy hull hydrolysate and yeast extract (n = 2 replicates each)). control-based media, which could improve the techno-economic efficiency and environment feasibility of rhamolipid production [13, 14]. Well-defined metabolic pathways could explain these results by _Pseudomonas aeruginosa's_ pattern of glucose utilization for growth and production of the rhamnose sugar moieties that are eventually synthesized to mono- and di-rhamolipids. The relatively inferior titer performances on SHH and MSH sugars compared to those on glucose and glycerol could be attributed to the defined metabolic path for the production of glucose-6 phosphate in the breakdown of glucose by _Pseudomonas_. This production of glucose-6-phosphate leads to the activation of rhIAB and rhIC operons, which is critical for producing rhamnose rings. This synthesis may not have been as effective in soy hull hydrolysate and MSH media, which, despite containing a similar percentage of xylose, galactose, and arabinose, might not have been utilized efficiently by _Pseudomonas_ for activating genes for rhamolipid synthesis [13, 14].
# Selection Criterion of Reanalysis Methods for Plane Truss Optimization
## 4 Construction of Selection Criterion
### Method Comparison Study
#### 4.1.3 Decreased Number of DOFs  
For the situation where the DOFs decrease, the MPCG method [18] was employed in this study. To evaluate its effectiveness, the examples presented in Section 4.1.1 were used for verification. Specifically, the top-right corner node was removed from examples 1 and 2, and the bottom-right corner node was removed from example 3 while keeping all other conditions constant. The complete analysis and MPCG methods were utilized for the calculations, and the results are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Case No.** & **Indicator** & **Cholesky** & **Guyan + Epsilon** & **Guyan + PCG** \\ \hline \multirow{4}{*}{1} & \(s\) or \(k\) & - & 3 & 2 \\  & Computation time & 1.8388 s & 2.1057 s & 2.0882 s \\  & Proportion & 100\% & 114.5\% & 113.6\% \\ \hline \multirow{4}{*}{2} & \(s\) or \(k\) & - & 3 & 2 \\  & Computation time & 63.7727 s & 18.3677 s & 17.6684 s \\  & Proportion & 100\% & 28.8\% & 27.7\% \\ \hline \multirow{4}{*}{3} & \(s\) or \(k\) & - & 3 & 2 \\  & Computation time & 149.7456 s & 2.6594 s & 2.1351 s \\ \cline{1-1}  & Proportion & 100\% & 1.8\% & 1.4\% \\ \hline \end{tabular}
\end{table}
Table 4: Results of structural cases with increased DOFs.  
Figure 6: Indicator curves of two combinations: (**a**) _Fr_–[PERSON]/Guyan + PCG; (**b**) _Mr_–[PERSON]/Guyan + PCG; (**c**) _Fr_–[PERSON]/Guyan + Epsilon; (**d**) _Mr_–[PERSON]/Guyan + Epsilon.  
According to Table 5, the computational efficiency of the MPCG method increases with the increase of DOFs. Moreover, for low DOFs, the computational efficiency of the complete analysis method is higher than that of the MPCG method. Therefore, a criterion for selecting the reanalysis method under decreased DOFs is proposed. Specifically, when the DOFs are reduced, the MPCG method should be selected. However, if the number of DOFs is less than 50, the complete analysis method should be used for solution.
## 2 Materials and Methods
### DOM Extraction and Quenching Titration  
Before extraction of DOM, the biochar was sieved through a 60 mesh sieve and HA was sieved through a 200 mesh sieve and then dried in an oven to avoid the effect of moisture on DOM extraction. A total of 45 g of biochar was dissolved in 450 mL of deionized water (\(w\):\(v\) = 1:10), and 45 g of biochar + 15 g of HA was dissolved in 600 mL of deionized water (\(w\):\(v\) = 1:10) and shaken on a shaker for 24 h. After shaking, the solution was filtered through a 0.45 \(\upmu\)m membrane by vacuum filtration, and the DOM was determined by filtration three times and diluted to 10 mg C L\({}^{-1}\) to reduce the effect of infiltration [30].  
The DOM extracted from biochar was denoted as BC-DOM, and the DOM extracted from biochar and HA was denoted as BC + H-DOM. Mn and Cu were selected for the experimental titration in this experiment, with MnCl\({}_{2}\) and CuCl\({}_{2}\) at contents of 0.1 mg L\({}^{-1}\) and 0.02 mg L\({}^{-1}\), respectively. A certain amount of the above solutions was aspirated and titrated to a solution containing a range of contents of HM (Mn: 0, 100, 150, 200, 250, 300, 400 \(\upmu\)mol L\({}^{-1}\); Cu: 0, 10, 20, 40, 60, 80, 100 \(\upmu\)mol L\({}^{-1}\)) were titrated into a 50 mL brown volumetric flask containing 30 mL of DOM solution. The volume aspirated was less than 0.25 mL for each HM content and the pH of the mixture was maintained at about 8.25 with NaOH and HCl during the titration to minimize the effect of changes in solution and acidity. After the titration, the mixed solution was placed on a shaker for 24 h to ensure complexation equilibrium [28]. After shaking, the solution was stored at 4 \({}^{\circ}\)C, and one part was used for spectroscopic (UV-Vis and 3 DEEM) determination and the other part was freeze-dried at \(-\)80 \({}^{\circ}\)C for infrared analysis (Nicolet iS 10, Thermo Scientific, Waltham, MA, USA).
## 2 Materials and Methods  
### Patient Image Dataset  
At the end of brain tumor operations, 3D iB-mode and 3D iCEUS data were acquired using a neuronavigation system (SonoNavigator, Localite, Sankt Augustin, Germany) coupled with an ultrasound device (AplioXG, Toshiba Medical Systems Europe, Zoetermeer, The Netherlands). The resection cavity was filled with physiological liquid for the propagation of the ultrasound waves. A large linear array transducer (contact area: 13 mm \(\times\) 46 mm; range of frequency: 4.8 to 11.0 MHz; average frequency: 8 MHz; frame rate of the 2D ultrasound images: 29 fps (frames per second)) was positioned through the skull opening, in contact with the brain surface and the resection cavity surface. The surgeon scanned the cerebral region of interest with the 2D ultrasound transducer whose position was followed by the navigation system's optical tracking module. A 3D ultrasound volume was then reconstructed from the 2D slices by the neuronavigation system. The 3D iCEUS data were obtained by injecting 4.8 mL of an intravenous ultrasound contrast agent (SonoVue, Bracco s.p.a, Milan, Italy) at a rate of 3.0 mL/min using a syringe pump (ACIST Vueject, Bracco s.p.a, Milano, Italy) and the contrast harmonic imaging (CHI) modality [10]. The contrast agent injection was performed via the central venous catheter positioned in the vena jugularis interna. In the original 2D ultrasound images, the pixel size is 0.422 mm \(\times\) 0.422 mm, and the voxel size of the reconstructed 3D volumes is 1 \(\times\) 1 \(\times\) 1 mm\({}^{3}\).  
An image database of patients with different kinds of tumors has been collected by the Department of Neurosurgery at the University Hospital of Leipzig, in the context of a previous research project funded by the German Research Society (Deutsche Forschungsgemeinschaft) and accepted by the ethics commission of the University of Leipzig. Twenty three patients were included in this \"offline\" analysis based on intraoperative ultrasound images (see Table 1), the histopathology of a glioblastoma WHO Grade IV and a planned gross total or subtotal resection. Glioblastoma's are tumors that infiltrate the brain tissues, and their borders with healthy tissues are unclear. Therefore, the removal of the whole tumor is a complex task for the surgeon. Possible tumor residuals in the 3D i-Bmode and iCEUS data were manually segmented by four experts (neurosurgeons and scientists), who have had experience with intraoperative ultrasound imaging of brain tumors (image data acquisition and analysis) for more than seven years. The task was performed using radiological findings and postoperative MR image data. For four patients, no tumor was visible in the iUS and MR image data. Radiological findings are medical reports provided by radiologists in which possible operation complications (for example blooding) and the presence of possible remnants of tumor tissue are described. These reports are routinely achieved based on postoperative MR data.
## 2 Material and Methodology
### Cerchar Abrasivity Index Test  
There are two kinds of testing instruments for the Cerchar abrasivity index test; original Cerchar equipment and West Cerchar equipment. The original Cerchar equipment features a moving pin lever with a fixed rock specimen, while, for the West Cerchar equipment, the rock specimen travels under a fixed stylus pin. All test data included in this study were obtained using the West Cerchar equipment, as shown in Figure 2. This equipment's stylus pin has a 90\({}^{\circ}\) tip and a diameter of 10 mm, and is made of steel with a specific Rockwell hardness. The suggested hardness of the stylus pin (HRC 55) was used for the tests, which is referred to as the ASTM standard [33].  
Table 4 summarizes the Cerchar abrasiveness test results obtained for Korean rock types, and some of these testing results were also presented in a previous study [34]. It should be noted that the Cerchar abrasiveness test was carried out on the saw-cut surface. The research database included 14 igneous rocks, 11 metamorphic rocks, and 5 sedimentary rocks. The UCS of the rocks varied from 34.9 to 235.3 MPa, and the BTS varied from 1.6 to 18.2 MPa.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Origin** & **Rock Type** & \begin{tabular}{c} **UCS** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **BTS** \\ **(MPa)** \\ \end{tabular} & **EQC** & **CAI** & \begin{tabular}{c} **B1** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **B2** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **B3** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **B4** \\ **(MPa)** \\ \end{tabular} \\ \hline \multirow{10}{*}{Igeneous} & Granite \#1 & 178.4 & 8.2 & 58.3 & 3.001 & 21.75 & 0.912 & 93.3 & 731.4 \\  & Granite \#2 & 145.9 & 7.8 & 64.1 & 2.753 & 18.61 & 0.898 & 76.9 & 569.0 \\  & Granite \#3 & 135.3 & 6.8 & 43.0 & 2.688 & 19.93 & 0.904 & 71.1 & 460.0 \\  & Granite \#4 & 170.9 & 9.0 & 64.5 & 2.902 & 19.08 & 0.900 & 90.0 & 769.1 \\  & Granite \#5 & 176.7 & 8.3 & 59.5 & 3.061 & 21.43 & 0.911 & 92.5 & 733.3 \\  & Granite \#6 & 151.3 & 10.2 & 64.0 & 2.410 & 14.89 & 0.874 & 80.8 & 771.6 \\  & Granite \#7 & 173.6 & 6.9 & 59.1 & 2.555 & 25.20 & 0.924 & 90.3 & 598.9 \\  & Granite \#8 & 121.5 & 6.2 & 62.4 & 3.184 & 19.77 & 0.904 & 63.9 & 376.7 \\  & Granite \#9 & 163.6 & 9.8 & 63.6 & 3.217 & 16.65 & 0.887 & 86.7 & 801.6 \\  & Granite \#10 & 34.9 & 1.6 & 61.7 & 2.599 & 22.22 & 0.914 & 18.3 & 27.9 \\  & Diorite & 235.3 & 14.8 & 36.6 & 2.658 & 15.86 & 0.881 & 125.1 & 1741.2 \\  & Gabbro & 110.0 & 7.8 & 38.3 & 2.625 & 14.03 & 0.867 & 59.9 & 429.0 \\  & Diabase & 234.5 & 14.2 & 39.4 & 2.658 & 16.58 & 0.886 & 124.4 & 1664.9 \\  & Porphyy & 195.6 & 14.3 & 44.6 & 2.422 & 13.65 & 0.863 & 105.0 & 1398.5 \\ \hline \multirow{10}{*}{Metamorphic} & Gneiss \#1 & 126.9 & 7
## 3 Efficiency Analysis of Herringbone Planetary Gear
### Power Loss Due to Gear Meshing  
Frictional power losses associated with the gear surface can be divided into sliding friction power loss and rolling friction power loss. The total losses are related to the lubrication condition (friction coefficient of the gear surface), gear speed, and load acting on the gear tooth surface. Moreover, windage power loss is related to the viscosity of the lubricating oil, gear speed, gear radius, gear width, and so on.
## 4 Discussion
### Post-Construction Settlement of Roadbeds Under Traffic Vibration Loads  
Roadbeds constructed on soft clay foundations, along with the underlying foundations, constitute either upper-hard, lower-soft systems or soft-sandwiched systems. Combining the vibration response analysis in Section 2 and the vibration reconsolidation test results in Section 3, we can jointly explain the reasons for the significant post-construction settlement caused by traffic vibration loads from both wave propagation and vibration perspectives. A picture emerges: Under traffic vibration loads, a significant amount of kinetic energy from the vibration is transmitted to the deep soft clay foundation below the roadbed through vibration waves. The kinetic energy of soil particle vibration causes changes in the microscopic structure of the soft clay, leading to the damage of the soil's structure. This, in turn, induces reconsolidation settlement of the soft clay under the existing roadbed load and self-weight load. Due to the deep and long-lasting influence of traffic vibration loads and the pronounced vibration reconsolidation phenomenon in soft clay, the entire roadbed-soft clay foundation system will experience substantial post-construction settlement.  
In engineering practice, to calculate the total post-construction settlement of a roadbed under traffic vibration loads, we need to consider both wave propagation and vibration aspects:  
1. Wave propagation analysis: Analyze the distribution of static loads \(P\) in the foundation, including the loads from the roadbed and the self-weight of the soft clay. Analyze the dominant frequency \(f\) and amplitude of the traffic vibration loads. Using the numerical simulation method in Section 2, we can calculate the distribution of vibration load amplitude \(P_{1}\) in the foundation;
2. Vibration analysis: Obtain the \(e\)-lgp curve of the soft clay through conventional one-dimensional consolidation tests. Obtain the relationship between the equivalent static load increment \(\Delta P\), static load \(P\), vibration frequency \(\bar{f}\), and vibration load amplitude \(P_{1}\) through vibration reconsolidation tests.  
Combining the above wave propagation and vibration analyses, we can calculate the distribution of the equivalent static load increment \(\Delta P\) in the foundation. Then, using the \(e\)-lgp curve of the soft clay and the layer summation method, we can estimate the total post-construction settlement of the roadbed under the traffic vibration loads. Since research on vibration reconsolidation tests is still in its early stages, more experimental research is expected to verify and promote this method.
# Taxonomy of Scheduling Problems with Learning and Deterioration Effects
## 3 Results
### Solution Methods
#### 3.4.1 Exact Methods  
In general, the NP-hardness of these scheduling problems makes it difficult to find exact methods to find solutions in polynomial time. Moreover, exact methods are usually proposed to mathematically formalize the problems and to solve small-sized instances. Similarly, they have been used to evaluate the performance of approximation algorithms. Modeling has mainly used mixed-integer linear programming (MILP), mixed-integer programming (MIP), mixed-integer non-linear programming (MINLP) or fuzzy mixed-integer non-linear programming (FMINLP) [94]. In general, the solvers used to deal with these models are CPLEX (e.g., [PERSON] et al. [95]), GAMS (e.g., [PERSON] [96]) and LINGO (e.g., [PERSON] et al. [97]). Almost 11.2% of short-listed articles propose an exact method. However, in the 1970s, the research community found that some of the optimization problems, including scheduling problems, were difficult to resolve. The first scheduling problem listed was minimizing the total weighted completion time on a single machine. Similarly, problems related to complex machine environments and time-changing effects have become computationally complex. Thus, the use of non-accurate methods to resolve them is more popular every day, as seen below.