esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Hepatic and Renal Histology  
A significant difference between the control group and EE12 was seen in the liver's histology (\(p\) = 0.036), with the control group's histology displaying overall hydropic changes (Table 3). When compared with the other groups, the control mice showed a generalized distribution of these changes (\(p\) = 0.006), while the other groups showed hydropic changes at the centrilobular zone. With at least one animal in each group displaying chronic focal hepatitis, liver inflammation was also evaluated; the findings did not statistically differ between groups. Except for one instance of chronic interstitial nephritis in EE24, no histological alterations were seen in the experimental animals (Figure 4).  
Figure 4: Microscopic images (staining with H&E) of liver sections from the different groups under study. (**a**) Cellular tumefaction and hydropic changes in a mouse’s liver from the control group, 200\(\times\). (**b**) Hydropic changes observable only in the centrilobular region of an animal’s liver from EE12, 100\(\times\). (**c**) Cellular tumefaction and centrilobular hydropic changes in a mouse’s liver from EE24, 100\(\times\). (**d**) Cellular tumefaction, centrilobular hydropic changes, and multifocal inflammatory infiltration in an animal from EE48, 200\(\times\).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Control** & **EE12** & **EE24** & **EE48** \\ \hline Liver & & & & \\ Normal & 1/5 (20.0\%) a  & 5/6 (83.3\%) b  & 4/6 (66.7\%) a,b  & 4/6 (66.7\%) a,b  \\ Hydropic & & 4/5 (80.0\%) a  & 1/6 (16.7\%) b  & 2/6 (33.3\%) a,b  \\ changes (HC) & & 4/5 (80.0\%) a  & 0/6 (0.0\%) b  & 0/6 (0.0\%) b  \\ HC general & 4/5 (80.0\%) a  & 0/6 (0.0\%) b  & 0/6 (0.0\%) b  \\ HC & 0/5 (0.0\%) & 1/6 (16.7\%) & 2/6 (33.3\%) & 2/6 (33.3\%) \\ centrilobular & & & & \\ Inflammation & & & & \\ Absent & 3/5 (60.0\%) & 5/6 (83.3\%) & 3/6 (50.0\%) & 3/6 (50.0\%) \\ Chronic focal & 2/5 (40.0\%) & 1/6 (16.7\%) & 3/6 (50.0\%) & 3/6 (50.0\%) \\ hepatitis & & & & \\ Kidney & & & & \\ Normal & 5/5 (100.0\%) & 6/6 (100.0\%) & 5/6 (83.3\%) & 6/6 (100.0\%) \\ Chronic & & & & \\ interstitial & 0/0 (0.0\%) & 0/0 (0.0\%) & 1/6 (16.7\%) & 0/0 (0.0\%) \\ nephritis & & & & \\ \hline \hline \end{tabular}  
* Significant statistical differences between groups are indicated by different letters (a and b) (\(p\) < 0.05). HC: hydropic changes.  
\end{table}
Table 3: Numbers of animals with hepatic and renal histological lesions per experimental group (%).
## 3 Related Work  
An essential but missing approach for understanding and deploying the QAOA is a constructive one to carry out the outer-loop classical optimization [12]. Recent work showed that for large enough problem sizes, algorithms suffer from barren plateaus with a very low probability to escape, resulting in algorithms to settle at a sub-optimal result [2].  
In 2014, [PERSON] et al. showed that the QAOA with \(p=1\) achieved an approximate ratio of 0.6924 when performing Max-Cut on three-regular graphs. Unfortunately, it did not outperform the best classical algorithm available. In 2002, [PERSON] et al. achieved a significantly better approximation ratio of 0.9326 with a semidefinite programming-based approach [13]. In 2018, [PERSON] showed that the QAOA for Max-Cut could achieve far better approximation ratios [14] than the classical Goemans-Williamson algorithm [15]. In 2019, [PERSON] et al. showed that the QAOA is arguably one of the strongest candidates for demonstrating quantum advantage over classical approaches in the current NISQ era [16].  
It was observed that while the QAOA has a simple description, its effectiveness is not clear beyond the case of \(p=1\)[12]. To be able to achieve quantum supremacy over classical algorithms, it is important that its mechanisms are analyzed and understood for depths \(p>1\). Perhaps, because of lack of this understanding, the QAOA has not shown a greatly improved performance on bounded-degree graphs when the depth \(p\) is shallow [17; 18]. It was conjectured in [12] that the reason for this limited performance of the QAOA could be due to the fact that the algorithm cannot \"see\" the entire graph at low depth. Perhaps, a solution to this might be to increase the depth of the QAOA with the system size (e.g., \(p\geq\log n\)). For example, for the Max-Cut problem, through numerical evidence, it seems that the QAOA yields the ratio (Equation (5)) \(r\geq(2p+1)/(2p+2)\). In another case of [PERSON]'s unstructured search problem among \(n\) items, the QAOA was able to find the target state with \(p=\Theta(\surd n)\), achieving the optimal query complexity within a constant factor [19].  
The QAOA for Max-Cut has been widely studied since [PERSON] et al. initially introduced the algorithm. Recently, in 2021, [PERSON] et al. introduced a \"divide-and-conquer\" approach to solve a large-scale Max-Cut graph. The authors demonstrated that recursively partitioning a large graph into smaller ones and applying combination policy helped them achieve a higher approximation ratio than that obtained with classical methods [20]. This approach utilizes a partitioning heuristic to split a large graph into two sub-graphs with equal nodes and sharing two common nodes. The sub-graphs are then solved by the QAOA and classically combined in the end to retrieve the results. Furthermore, the authors stated this approach only applies to input graphs with connectivity less than the maximum allowed qubit size. Nonetheless, this approach is not appropriate for our enhanced methodology to solve the TSP.  
Figure 1: A high-level schematic for the \(p\)-layer QAOA.
## 4 Case Study
### Unstructured Data Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & \multicolumn{2}{c}{**Top 10 Keywords**} & \multicolumn{2}{c}{**Function of UVA**} & **R\&D Themes** \\ \hline
1 & direction, adjust, posture, implement, carry, shoot, osmo, engine, camera, rotate & Shoot & & \\
2 & machine, shoot, requirement, support, cost, aerial, need, flight, structure, develop & Shoot & \\
3 & adjust, camera, adjust, lens, support, structure, install, osmo, use, cause & Camera & \\
4 & scope, image, shoot, sensor, made, cannot, machine, osmo, requirement, reduce & Process Image & \\ \hline
5 & mission, perform, do, control, flight, apply, scan, situation, field, requirement & Control & Control system \\
6 & make, control, flight, perform, do, position, lead, state, influence, tradition & Flight & \\ \hline
7 & user, cannot, perform, flight, state, develop, apply, telecontrol, provide, situation & Telecontrol & \\
8 & function, user, way, time, requirement, use, perform, apply, machine & Telecontrol & \\
9 & become, perform, use, requirement, field, apply, influence, machine, device, make & Telecontrol & \\ \hline
10 & rotate, connect, engine, lead, motor, exist, process, happen, become, provide & Engine & Energy system \\ \hline
11 & increase, volume, lead, install, cannot, time, exist, happen, lens, requirement & Structure & Transmission system \\ \hline
12 & claim, field, design, influence, develop, signal, requirement, restrict, apply, need & Communication & Communication system \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top 10 keywords of the 12 clusters, and the possibility distribution of six R&D themes. NetworkX [57] was used to draw an IPC network, as shown in Figure 14. NetworkX is a python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. The degree of centrality of each R&D theme node was calculated according to Equation (2), and the results are shown in Table 4. Note that the value of the degree of each R&D theme node was obtained using the NetworkX. By comparison, the most difficult R&D theme was the \"control system relating to flight\", the second most difficult was the \"execution system relating to shoot and process image\", then the \"telecontrol and communication system\", and finally the \"energy system and the transmission system\".
## 2 Materials and Methods  
### Materials  
The plastics used in this research were semi-crystalline polyamide 6.6 and amorphous polypropylene. These were chosen due to the polar amino group of polyamide 6.6 and the lack of any polar group in polypropylene. Figure 2 shows the chemical structure of both the plastics and polar amino group of polyamide 6.6.  
For the metal substrate, the cold-rolled steel AISI 304 (DIN 1.4301, ISO X5 CrNi18\(-\)10) was chosen. For the hybrids with polyamide, the cold-rolled surface was used, while the surface of the steel was sandblasted for the hybrids with polypropylene. This was carried out because untreated polypropylene needs sufficient mechanical adhesion to create sound joints. No further surface treatment was carried out for any of the steels. The parameter set was utilized to identify the change due to the low-pressure plasma treatment. The sandblasting increased the surface roughness of the steel, which was analyzed from five measurements acquired via Tester T1000 by Hommelwerke GmbH, Villingen\(-\)Schwenningen, Germany. Mechanical adhesion is based on the form-fitting connection between the plastic and the steel. The liquid plastic penetrates the pores, capillaries, undercuts and irregularities on the steel surface. By increasing the surface roughness, additional capillaries, pores and undercuts can be created. This enhances the snap-fit effect and enables nonpolar plastics to create sound joints [28; 29]. The average surface roughness is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Surface**} & \multicolumn{2}{c}{**R\({}_{\mathbf{z}}\)**} \\  & \multicolumn{2}{c}{[\(\mathbf{\mu}\mathbf{m}\)]} \\ \cline{2-3}  & \(\mathbf{X}\) & \(\mathbf{s}\) \\ \hline Sand blasted & 14.69 & 1.72 \\ Cold-rolled & 1.44 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average surface roughness of the steel samples used.  
Figure 2: Chemical structure of (**a**) polypropylene and (**b**) polyamide 6.6.  
An MHG SMG 50 (Dusseldorf, Germany) was utilized for the sand blasting. White corundum with a granularity varying between 150 and 212 \(\upmu\)m, an air pressure of 4 bar at an angle of 90\({}^{\circ}\) with a distance between steel and blasting pistol of 50 mm was used in the process. The geometry of both materials was 50 mm \(\times\) 25 mm \(\times\) 2 mm. Before the joining process, the surfaces were cleaned with ethanol.
# Grousers Effect in Tracked Vehicle Multibody Dynamics with Deformable Terrain Contact Model
## 2 Soil Mechanics
also used for compact agricultural terrain due to the limited normal pressure on the soil exerted by the small size tracked vehicle. Focusing on the interaction of a cutting surface as a blade or a grouser with the soil, passive earth failure must be considered. In fact, the terrain in front of the cutting blade or behind the grouser can be brought in state of passive failure, due to the applied force [37]. According to Mohr criterion the passive failure of a soil prism occurs when the principal stress \(\sigma_{p}\) on its vertical sides is reached (Figure 2). Since the major principal stress is horizontal, failure of the soil prism takes place on slip lines sloped at (45\({}^{\circ}\) - \(\phi\)/2). Integrating the principal passive earth pressure \(\sigma_{p}\) on the grouser vertical area (Figure 3) the horizontal force acting on the lug can be determined (Equation (5)). It is the contribution to the vehicle traction force offered by a single grouser sunk into the soil. \[F_{g}=b\int_{0}^{l}\sigma_{p}dz=b\int_{0}^{h_{b}}\left(\gamma_{s}zN_{\phi}+2c \sqrt{N_{\phi}}\right)dz=b\left(\frac{1}{2}\gamma_{s}h_{b}^{2}N_{\phi}+2 ch_{b} \sqrt{N_{\phi}}\right), \tag{5}\]  
in which \(\gamma_{s}\) is the specific weight of the soil, \(h_{b}\) is the grouser height sunk into the soil and \(N_{\phi}\) is equal to \(\tan^{2}\left(45^{\circ}+\phi/2\right)\). If there is a normal pressure \(q\) acting on the terrain surface behind the grouser the resultant force is:  
\[F_{g}=b\left(\frac{1}{2}\gamma_{s}h_{b}^{2}N_{\phi}+qh_{b}N_{\phi}+2 ch_{b} \sqrt{N_{\phi}}\right). \tag{6}\]  
It has to be pointed out that the passive earth pressure approach should be integrated also considering the existence of friction and adhesion on grouser vertical sides. Furthermore because  
Figure 3: Interaction of a grouser with the terrain. Figure 2: (**a**) Soil prism under passive compression; (**b**) Mohr circle. of the existence of shear stress at grouser-soil interface the normal pressure considered is no more a principal stress and the vertical surface of a grouser can be inclined with respect to the horizontal plane of the terrain. Reference [34] proposed a mathematical solution to correctly evaluate the force applied by a grouser taking into account its inclination and the presence of shear stress on its sides. [PERSON] proposed an equation based on an elastic stress distribution into the soil in order to consider the traction force developed by the vertical shear areas of a grouser. In Reference [42], [PERSON] proposed a different equation based on static equilibrium to consider the same contribution. He formulated an equation of the total traction force given by a grouser considering the latter as a cutting surface and referring to a failure state of the soil.
## 2 Test Material and Method
### Experimental Techniques and Test Condition  
Uniaxial compression tests were conducted on an electric servo-hydraulic material testing machine (INSTRON 1346) installed at the Advanced Research Center in Central South University. A load cell and a linear variable differential transformer were applied to monitor and record the axial force and displacement, respectively, on the specimen at 100 Hz logging frequency. Silicon grease lubricant was elaborately smeared on the platens and the top and bottom surfaces of specimens to reduce the end friction on the specimen-platen interfaces.  
An acoustic emission (AE) system manufactured by Physical Acoustic Corporation was used for the acquisition and analysis of the microcrack evolution within the specimen during the loading process. A pair of AE sensors (Nano 30) mounted on the specimen surfaces and pre-amplifiers were adopted to monitor the AE events from the specimens. The threshold value of 45 dB was set to avoid the disturbance of outside noises, i.e., a signal is identified as an AE event and acquired only when its amplitude exceeds 45 dB. Otherwise, it will be eliminated. In each test, the INSTRON and the AE system were manually triggered synchronously.  
To minimize the data discreteness, all specimens were tested under the same conditions. The constant strain rate of \(4\times 10^{-5}\) s\({}^{-1}\) was designed for testing all specimens. The axial displacement rate was thus controlled at 0.24 mm\(\cdot\)min\({}^{-1}\) until specimen failure. Moreover, by means of the air conditioner, the temperature in the laboratory was kept at 25 \({}^{\circ}\)C and the humidity was maintained at 60%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Rock Type** & **State** & \(\phi\) & \(\rho\) & \(A_{w}\) & \(t_{s}\) a  & \(C_{p}\) & \(\kappa\) & \(K\) \\ \cline{3-8}  & & /\% & /kg\(\cdot\)m\({}^{-3}\) & /\% & /h & /m\(\cdot\)s\({}^{-1}\) & /nm\({}^{2}\) & /GPa \\ \hline Sandstone & Dry Sat. & 8.50 & 2368.5 & 0.00 & 48 & 3094.0 & & 4.70 \\  & & 2453.5 & 2.29 & 48 & 3614.5 & 3.76 \(\times 10^{2}\) & & 1.70 \\ \hline Granite & Dry Sat. & 1.07 & 2620.3 & 0.00 & 168 & 4333.4 & & 12.24 \\ \hline Marble & Dry Sat. & 2625.8 & 0.52 & 168 & 5105.6 & & 1.72 \(\times 10^{-2}\) & & 14.91 \\ \hline \hline \end{tabular} Note: \(\rho\), density; \(\phi\), porosity; \(A_{w}\), water absorption; \(t_{s}\), time required for saturation via water immersion; \(C_{p}\), primary wave velocity; \(k\), permeability; \(K\), bulk modulus; N.A., not available. a The time for water saturation was determined by monitoring the weight variation of the rock [4,48]. The specimen was considered to reach a fully saturated state once its weight did not increase with time.  
\end{table}
Table 2: Some essential physical and mechanical parameters of tested rocks.
## 2 Materials and Methods
### Validation Features of the Established UHPLC-MS/MS Approach
#### 2.10.2 Sensitivity and Linearity  
The evaluation of the sensitivity and linearity of the UHPLC-MS/MS system included the loading of 12 calibration curves. The creation of these curves entailed employing eight CSs for DVB, which were performed within the HLMs matrix, all accomplished in a single day. Subsequently, the regression equation of the constructed calibration curve that was created was utilized to determine the DVB in samples of unknown origin. The LOD and LOQ were determined following the ICH guidelines [40]. The study's guidelines entail estimating the LOD and the LOQ by utilizing the slope and the SD of the intercept, as provided by Equations (2) and (3) [41].  
\[\text{LOD}=3.3*\text{SD of the intercept/Slope} \tag{2}\]  
\[\text{LOQ}=10*\text{SD of the intercept/Slope} \tag{3}\]  
The determination of the linearity in the present UHPLC-MS/MS approach included the use of statistical measures, specifically the least squares methodology (y = ax + b) and the correlation coefficient (r\({}^{2}\)).
## 2 The Reliability Based Rescue Resource Scheduling Model
### Analysis of Travel Time and Reliability  
Due to the influence of various random events, such as weather conditions, traffic jams and traffic incidents, the supply of a traffic network may be uncertain, which varies the travel time of a rescue route randomly. The different positions of different rescue centers result in the differences in travel time and reliability of rescue routes. Therefore, it is necessary to analyze the travel time of a rescue route in rescue resource scheduling.
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 5 Conclusions  
This work evaluates the schedulability of the EDZL algorithm. First, we compare the performance of EDZL schedulability tests. With respect to the number of admitted test instances, the demand-based test outperforms the utilization-based test and the slack-based test. In our experiments, Demand + Util can admit all test instances that are admitted by the slack-based test. Demand + Util admits 89.31% of EDZL-schedulable test instances.  
Second, we compare EDZL and \(\text{EDF}^{(k)}\). In respect to the schedulability test, this paper shows that the utilization-based EDZL schedulability test is equivalent to the \(\text{EDF}^{(k)}\) schedulability test. Hence, Demand + Util strictly dominates the \(\text{EDF}^{(k)}\) schedulability test. Additionally, this paper compares EDZL and \(\text{EDF}^{(k)}\) scheduling in terms of schedulable test instances. EDZL and \(\text{EDF}^{(k)}\) do not dominate each other. However, EDZL can accommodate more task sets than \(\text{EDF}^{(k)}\). In our experiments, EDZL successfully schedules 7.0% more test instances than \(\text{EDF}^{(k)}\).  
In future works, we plan to prove that Demand + Util dominates the slack-based EDZL schedulability test. By disclosing the dominance between schedulability tests, we can determine the feasibility of task sets efficiently. Furthermore, our work assumes implicit deadline systems. We plan to develop EDZL schedulability tests under constrained or arbitrary deadline systems.  
Conceptualization, S.H.; Formal analysis, S.H.; Software, S.H.; Writing--original draft, S.H.; Writing--review and editing, W.P., M.-C.K. and M.P.; Validation, W.P., M.-C.K. and M.P. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Konkuk University in 2021 (no. 2021-A019-0364).  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript nor in the decision to publish the results.
## 3 Comparison of Finite Element Simulation of Almen Test and Almen Test
### Comparison of Results of FE Simulation and Almen Test  
The effectiveness of the quantitative description of peening coverage and the FE simulation is proved by comparing the Almen strip's deflection profile of the Almen test and FE simulation.  
In the Almen test and FE simulation, the impact velocities are both 25, 30, and 35 m/s. In the Almen test, peening coverage reaches 100% when the impact time is 5 s, while in the FE simulation, the peening coverage is also 100%. The Almen strip deflection in the Almen test and FE simulation is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Density & 7800 kg/m\({}^{3}\) \\ Poisson’s Ratio & 0.29 \\ Young’s Modulus & 205 GPa \\ Yield Strength & 1268 MPa \\ Ultimate Strength & 1422 MPa \\ Elongation & 8.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of SAE 1070 steel.  
Figure 6: Impact velocity is calculated by measuring the distance a shot traveled. (**a**) A high-speed camera is employed to measure the impact velocity, (**b**) measure the distance between the two balls to get the impact velocity.  
In Figure 7, The red dot points mark the data of the Almen test, while the blue line represents the result of the FE simulation. The simulation results agree well with that of the Almen test. Only small deviation arises near the center of the Almen strip, which may be due to the measurement error of the Almen test. The accuracy of arc height is very high at the center of the Almen strip.  
The arc height (maximum value of the deflection) of the Almen strip is measured in the Almen test with different impact velocity of 25, 30, and 35 m/s, and the same impact velocity is used in the FE simulation, respectively. In Almen test the impact time is 5,10,20 and 40 s, the corresponding peening  
Figure 7: Variations of the Almen strip deflection profile at different impact velocities. (**a**) with impact velocity 25 m/s, (**b**) with impact velocity of 30 m/s, (**c**) with impact velocity of 35 m/s.  
coverage is 100%, 200%, 400%, 800% respectively. The saturation curve of arc height in the Almen test and FE simulation is shown in Figure 8.  
In Figure 8, the red dot points mark the arc height in the Almen test, while the blue line represents the arc height in the FE simulation. With the increase of peening coverage, the arc height both increases and then gradually saturate in Almen test and FE simulation. At the impact velocity of 30 m/s, the results of the arc height of the Almen test agree with that of FE simulation best, while at the impact velocity of 25 and 35 m/s, the simulation results basically agree with the Almen test results.  
Figure 8: Variations of the saturation curves of the arc height at different impact velocities.  
In all, with different conditions, the results of the Almen test and FE simulation are in good agreement, which indicates that the quantitative description of peening coverage and the FE simulation is effective.