esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Parameter Study of the Adsorption of DR-81 onto the Cockleshell Powder
#### 3.2.4 Effect of the pH of the Solution  
The effect of pH is crucial in influencing the surface charge of the bio-adsorbent in the solution of DR-81 dye. It was studied in the pH range of 2 to 12. Figure 11 illustrates the impact of pH on the adsorption process of DR-81 dye.  
Figure 11: Effects of pH solution on the removal efficiency and adsorption capacity for DR-81 (C0 = 10 mg/L, m = 1 g, V = 100 mL, T = 22 \({}^{\circ}\)C, stirring speed = 300 rpm).  
Figure 10: Effects of temperature on the removal efficiency and adsorption capacity for DR-81 (C0 = 10 mg/L, m = 1 g, pH = 7.09, v = 300 rpm).  
The effects of solution pH on the removal of DR-81 are shown in Figure 11. The results indicate that the removal efficiency of the DR-81 dye by CS decreased from 100 to 46% with a large increase in the pH range (2-12). This was responsible for the force of attraction between the solid surface and the functional groups of DR-81. However, when the pH was alkaline (pH > pH\({}_{\text{PZC}}\)), the negatively charged bio-adsorbent surface caused the formation of repulsive forces between the bio-adsorbent and DR-81.  
These results show that there is a good removal of the Direct Red 81 dye in a very acidic environment (pH = 2). However, since almost all water's pH varies from 6 to 8, a pH of 6 was chosen to give an idea of the adsorption capacity under normal conditions [12].
# DQN-Based Shaped Reward Function Mold for UAV Emergency Communication
## 2 Materials and Methods
### System Model  
This paper focuses on the application of Unmanned Aerial Vehicles (UAVs) in mountain emergency communication scenarios. An illustration depicting the UAV emergency communication networks is provided. In this study, we specifically concentrate on the flight speed and communication range of the UAV. Upon locating the target point, it is presumed that the UAV automatically establishes a communication connection. Given that only the UAV and the target point can engage in bidirectional communication, while the target points cannot communicate with each other, we can reasonably model this network as a star network.  
To effectively address the primary research questions, this paper narrows its scope to the UAV maneuver model and the transmission model, disregarding other factors. We operate under the assumptions that the UAV maintains a constant speed and has adequate endurance, and that transmission loss of the communication signal during transmission is negligible. The typical communication scenario is depicted in Figure 2.  
This paper posits a scenario in a mountainous region where N users are located at unknown positions alongside one UAV communication node and multiple relay nodes. Occasionally, due to the devastating impact of landslides, all base stations within the area suffer severe damage and become inoperable. Given the narrow terrain and other constraints, only one UAV can be deployed as a communication node, while the remaining relay nodes remain functional. In such instances, the UAV must establish a route that connects all users and conveys crucial information to each node. We designate both ends of the communication path as relay nodes, and information is returned directly after being transmitted to the backbone node. Upon the information's arrival at the node, a reward is granted, and the user subsequently exits the network. The simplified test platform simulates time as a discrete time step with the agent executing each action at regular intervals.  
The disaster area is defined as a 25 * 43 grid in which all the objects are to be observed by the UAV. The UAV moves at a constant speed, moving 3 grids per step. In order to facilitate learning, it is assumed that the UAV agent does not change the starting position. At the initial moment, the UAV agent can first select the first observation target arbitrarily and start movement. After completing the observation round for all objects, the UAV agent  
Figure 1: DQN.  
Figure 2: Typical communication scenario.  
will receive the corresponding reward. If the observation cannot be completed within the specified time, the round ends and the UAV agent cannot receive the reward.  
This article assumes the following:  
* The paper does not consider attenuation and other factors in the transmission process, simplifying the system model.
* The relay node directly transmits the signal downward after receiving it, and there is no delay in the middle.
* The UAV flies at a constant slow speed and only moves in two-dimensional space.
## 3 Results and Discussion
### Adsorption Performances of DE and DE-LDH
#### 3.2.11 Comparison with Other Adsorbents  
The present study of maximum adsorption capacity has been compared with earlier reports for CR (Table 4). From this table, DE-LDH was either identical to or was a better adsorbent than earlier reported adsorbents for the adsorption of CR [2; 28; 30; 31; 32; 33; 34; 35; 36; 37; 38]. Moreover, all the adsorption data were obtained at pH 7, indicating the possibilities for practical applications on an industrial scale.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Adsorbents** & **pH** & **Dosage (mg)** & **Time (min)** & **qm, (mg/g)** & **Reference** \\ \hline C/MnCuAl-LDOs & 4.5 & 20 & 180 & 317.2 & [2] \\ MgO-GO & 7 & 200 & 60 & 237 & [28] \\ Cross-linked PFCs & 8 & 50 & 60 & 256.4 & [30] \\ SiO\({}_{2}\)-PDA-PAPTCl & 7 & 200 & 60 & 302.6 & [31] \\ Magnesium borate & - & 10 & 180 & 183.15 & [32] \\ CABI nano-geothetic & 3 & 70 & 180 & 181.1 & [33] \\ DAMFC/chitosan & 5.5 & 100 & 10 & 152.5 & [34] \\ Mg–Al-LDH & 4 & 50 & 20 & 111.11 & [35] \\ Maghemite & 5.9 & 50 & 30 & 208.33 & [36] \\ XG-g-PAM/SiO\({}_{2}\)-2 & 4 & 50 & 150 & 209.20 & [37] \\ IL-LDH & 7 & 5 & 60 & 288.63 & [38] \\
**DE-LDH** & 7 & **40** & **30** & **305.8** & **This work** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of DE-LDH with reported different adsorbent in the adsorption of CR.  
Figure 12: Reusability of DE-LDH for the adsorption of CR.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Deposition-Associated Issues  
#### 3.2.1 Under-Extrusion  
Under-extruded areas will show visible gaps. There will be material missing in or between the print layers. Under-extruded objects will be fragile and may easily break. This is usually due to  
Figure 8: Spaghetti monster.  
Figure 7: Grinding or stripping of filament.  
hardware problems such as a loose extruder gear, dirty extruder gears, poor quality filament with diameter fluctuations, incorrect extrusion multiplier setting, and/or slicing errors if manual adjustments have been made. Solutions include the following:  
1. Making sure that the extruder gear is correctly tightened (refer to 3.1.6: (4) Overtightened extruder idler).
2. Clear any plastics with the sharp corner of a plier or with a durable pin (refer to 3.1.6: (5-6) Dirty extruder gears).
3. Poor quality filaments may have inconsistent diameter tolerances that often lead to a clogged nozzle. Use a caliper to measure the consistency of the filament diameter. The diameter difference should not exceed \(\pm\) 0.05 mm. Replace the filament or use a better-quality filament if necessary.
4. Tune the extrusion multiplier settings only if the under-extrusion is not caused by a hardware issue. Increasing the extrusion multiplier consequently increases the flow of the filament into the extruder. Decreasing the extrusion multiplier will also slow down the flow of the filament into the extruder.
5. Check the generated G-code, preview the slices using the latest slicing software. Repair any errors in the.STL file and make adjustments in slice settings if necessary.
# Efficacy of GS-441524 for Feline Infectious Peritonitistis: A Systematic Review (2018-2024)
## 3 Results
### Influence of Clinical Factors on Efficacy  
We examined the subset of complicated FIP (neurologic/ocular cases) more closely. As noted, success was lower in these cases. Among neurologic FIP cases across all studies (whether treated with higher doses or not), only 67.7% achieved success. In contrast, ocular-only cases had a 90.5% success rate, nearly as high as uncomplicated cases, indicating that standard dosing often suffices for ocular FIP. For neurologic FIP, many studies escalated GS-441524 doses (up to 10-15 mg/kg) specifically to address CNS penetration issues. Despite higher dosing, the CNS cases still had lower success, underscoring the difficulty of achieving adequate drug levels in the central nervous system. This finding aligns with the known pharmacokinetic challenge that GS-441524 faces with the blood-brain barrier [24].  
It was notable that when GS-441524 was combined with other antivirals in neurologic cases, outcomes improved. In the 10 neurologic cases treated with GS-441524 + Remdesivir, all survived. In eight neurologic cases treated with GS-441524 + GC376, seven survived (one died, despite the combination) [25]. These small numbers are promising, though a lot more data is needed to determine whether combination therapy may improve outcomes for neurologic FIP.  
Regarding the FIP form, we found that effusive FIP cases had worse outcomes than non-effusive FIP. When controlling for other factors, effusive FIP had an OR of \(\sim\)0.49 for success compared to non-effusive FIP, and an RR indicating about an 11% reduction in success probability. This supports clinical observations that non-effusive FIP tends to respond better to treatment than effusive FIP, perhaps because cats with effusive FIP often have more advanced disease or a higher virus burden. Mixed FIP cases (with partial effusion) had outcomes closer to non-effusive FIP. For this limited number of cases, if effusions were small or localized, the prognosis with GS-441524 remained good.  
GS-441524 was given through single or multiple routes of administration, with the majority of cases being single-route oral (522/650) or subcutaneous (95/650). Regarding the oral route, 454/522 cats (87%) achieved remission, whilst the proportion was 73/95 (77%) when delivered subcutaneously. This difference (87% vs. 77%) is statistically significant (\(p=0.01\)); however, the two groups differ in size considerably and are not stratified by the type of FIP or the treatment regimen. The absolute numbers of cases for some of these parameters are too small to apply statistics (single animals in some instances). Therefore, we cannot conclude with confidence that delivery via the oral route represents an intrinsically more effective method than subcutaneous delivery. A larger, stratified cohort is needed to evaluate this effectively.
## 3 Fresh and Hardened Properties of GPC
### Tensile and Flexure Strength  
Tensile strength, an imperative mechanical property used in design facets of concrete structure is determined by direct tension, splitting tension (ASTM C496) [137], and flexural tension (ASTM C78) [138]. According to [PERSON] et al. [55], tensile strength of GPC has a direct relation with compressive strength. Hence, the effect of different variables on tensile strength was the same as in case of the compressive strength. [PERSON] et al. [139] concluded that the spilt tensile strength of heat-cured GPC is similar to the corresponding strength of OPC concrete, and its relation with the compressive strength of GPC was also well comparable to that of ACI-318-99 for Ordinary concrete as expressed by regression analysis. [PERSON] et al. [140] recognized the effect of nano-silica (NS) on strength and durability characteristics of GPC and concluded that the tensile strength of GPC at normal curing enhanced with the inclusion of 56% nano-silica. In addition, while increasing slag content, the tensile strength increases [114]. It was reported that under ambient curing conditions, the mechanical strengths such as compressive, tensile, and flexural strength increases with the addition of 6% nano-silica [140] (Figures 15 and 16). [PERSON] et al. [141] evaluated the effect of sand/FA ratio on FA-based GPC and found that with an increase in the sand/FA ratio, the tensile strength at 7, 14, and 28 days decreased.  
Figure 14: Factors affecting compressive strength after 28 days, modified from [2]; Elsevier, 2019.  
Figure 15: Relation of flexural strength with molar concentration, modified from [140]; Elsevier, 2014.  
As far as the flexural strength of geopolymer mortar is concerned, [PERSON] et al. [116], found that it increases with the enhancement of compressive strength (Figure 17). The figure summarizes the relationship between the square root of compressive strength and flexural strength of different mixes.  
Table 6, which summarizes the fresh and hardened properties of GPC, shows that the geopolymer mix having lower flexural strength is due to the absence of calcium oxide amount that enables the CSH and CASH formation in geopolymer process [101]. The increase in additive content enhances the tensile and flexural strengths [56].
## 3 Results and Discussion  
### Fresh and Hardened Properties on Concrete  
Table 5 shows the fresh properties on concrete. When all the concrete mixtures were evaluated, the slump could meet the target range of 180 \(\pm\) 25 mm both at the beginning and after 60 min. The air content of concrete met range of 4.5 \(\pm\) 1.5% for all the mixtures, and its reduction was not significant even after 60 min.  
Figure 2 shows the concrete strength at early age. As for the concrete strength that used OPC according to the unit weight of cement, no difference between the strength developments of 330P and 350P was reported. For 380P, the strength continuously increased even at 13 \({}^{\circ}\)C. At 20 \({}^{\circ}\)C, the concrete strength rate was found to be similar for all the specimens until 24 h. However, 380P showed a higher  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Series** & **Test Item** & **Test Method** \\ \hline Raw materials & Particle size distribution (\%) & ASTM C204 \\ (Cement) & X-ray fluorescence & ASTM C114 \\  & Heat of Hydration & ASTM C1702 \\ Mechanical properties analysis & Compressive strength (MPa) & ASTM C873 \\ (Concrete) & Maturity (D-h) & ASTM C1074 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test methods for engineering properties of raw materials and concrete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mix ID.**} & \multicolumn{2}{c}{**Slump (mm)**} & \multicolumn{2}{c}{**Air Content (\%)**} \\ \cline{2-5}  & **Initial** & **After 60 min.** & **Initial** & **After 60 min.** \\ \hline
330P & 190 & 175 & 5.4 & 5.0 \\
350P & 195 & 185 & 4.8 & 4.4 \\
380P & 195 & 180 & 4.3 & 3.9 \\
330 HFS & 200 & 185 & 4.4 & 4.0 \\
330 HFS\_ePC & 205 & 190 & 5.8 & 5.4 \\
330 EP & 195 & 175 & 5.5 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fresh properties on concrete.  
strength development after 72 h. In addition, for the concrete mixtures that used OPC, a compressive strength of 5 MPa was developed after 24 h and 72 h in the case of 20 \({}^{\circ}\)C and 13 \({}^{\circ}\)C, especially.  
For 330 HFS, the concrete strength was slightly higher compared to OPC until 24 h. However, it significantly increased and exceeded 10 MPa at 72 h under the curing condition at 13 \({}^{\circ}\)C. For 330 HFS_EPC, the compressive strength was 2.7 MPa at 18 h and close to 5 MPa at 24 h. It increased with a slope similar to that of 330 HFS at 72 h, thereby exhibiting very high strength development compared to 330P. 330 EP exhibited a difference in strength development of less than 1 MPa compared to 330 HFS_ePC, but showed a significantly higher compressive strength increase rate at 72 h.  
At the 20 \({}^{\circ}\)C curing temperature, concrete exhibited a higher strength development rate than that at 13 \({}^{\circ}\)C. For 330 EP and 330 HFS_ePC, the compressive strength reached 5 MPa within 18 h, and this value was reached within 24 h for 330 HFS and in the 24-72 h range for 330P. 330P_ePC reached 5 MPa within 24 h at 13 \({}^{\circ}\)C, and the effect was significantly increased at a higher curing temperature.
## 4 Identification of Noise Sources around Rearview Mirror  
### The Mirror Modelling and Dipole Noise Source Distribution  
The geometric feature structure of the mirror is shown in Figure 5; the cameras are installed in position 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Boundary** & **Boundary Condition** \\ \hline Inlet & Velocity inlet, u = 120 km/h \\ Outlet & Pressure outlet, \(p\) = 0 Pa \\ Calculate the top and side of the domain & Symmetrical wall \\ Others & Wall \\ Boundary & Boundary condition \\ \hline \hline \end{tabular}
\end{table}
Table 1: Setting of boundary conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Option 1** & **Option 2** & **Option 3** & **Option 4** & **Option 5** \\ \hline Number of grids & 15.6 million & 26.1 million & 32.4 million & 38.8 million & 45.6 million \\ Wind resistance coefficient & & & & \\ (simulated value) & 0.352 & 0.332 & 0.314 & 0.315 & 0.315 \\ Wind resistance coefficient & & & & \\ (test value) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid-independent verification.  
Formula (12) is a method for calculating the intensity of the dipole noise source. The values of vorticity \(\mathbf{\omega}\) and velocity \(\mathbf{u}\) in the formula are obtained by transient calculation. Then, the relationship between the intensity of the dipole noise source and the flow physical quantity is used to establish a function to characterize the dipole noise source in the flow field. The noise source distribution on the rearview mirror is shown in Figure 6. It can be seen that the dipole noise source is mainly concentrated at the edges and bulges. Generally, the airflow separates at the edges and bulges and results in a strong pressure fluctuation. Meanwhile, at the junction of the rearview mirror shell and the rearview mirror frame, the noise source is mainly distributed in the place where the curvature changes greatly (position 1), where airflow separation is prone to occur. There is a similar strength distribution at position 2 near the body, which is also a significant location for airflow separation.  
There is a strong source of dipole noise at position 3, as shown in Figure 6. This is because the protruding features at these edges disrupt the smooth flow of air, resulting in increased vorticity. The structural protrusions at position 4 result in a dipole noise source at its edges. Position 5 is the junction of the windward and leeward sides of the mirror. It can be seen from the figure that the closer the location is to the bodywork, the larger the dipole noise source becomes. This is due to the fact that the presence of the bodywork obstructs  
Figure 5: The geometric feature structure of the mirror.  
Figure 6: Distribution of dipole noise sources.  
some of the airflow, causing the airflow at this location to become complicated and create vortices.
## 2 Numerical Methods
### Cloud Composition and Weighting Function  
Similar to any other gridless method, it is necessary to construct clouds using neighboring computational points within a specific distance, which is called the radius of influence, kernel radius, effective radius, etc., to form a calculation point of interest. As indicated in Figure 1a, if the points are distributed and the effective radius is \(r_{0}\) at the calculation point colored red, ten neighboring points become members of the cloud. In Lagrangian gridless methods, a constant \(r_{0}\) is generally used for all particles. However, it is not appropriate for Eulerian methods because the distances between points are not always the same, and the distances near the wall need to be shorter than in other regions. Moreover, it is desirable for the points in the cloud to be arranged in a state where information in all directions can be captured, as this reduces the adverse effect of information from points locate d far from the point of interest. To address these issues, a method [31] that divides the cloud into quadrants was used together with a weighting function, called the kernel function, as illustrated in Figure 1a. As shown in Figure 1b, after finding the closest point to the calculation point, where the distance is \(r_{min}\), the rectangular coordinate \(p\)-\(q\) is constructed around the calculation point such that it forms an angle of \(45^{\circ}\) with the line segment between the calculation point and the closest point, and the cloud is divided into four regions. Two points were selected from each quadrant, and consequently, the cloud contained eight surrounding points. The reason for fixing the number of surrounding points to eight is to reduce the computational time and improve the efficiency by making the calculation load at each calculation point as similar as possible when developing parallelization codes in the future. Near the boundary, if two points are not included in each quadrant, the p-q coordinate system is rotated gradually to satisfy this condition. The \(r_{0}\) value was set as \(2.5r_{1}\), where the reference distance \(r_{1}\) is obtained by dividing the distance from the calculation point to the farthest point in the cloud by \(\sqrt{2}\), that is, \(r_{1}=r_{max}/\sqrt{2}\). In the case of an equally spaced distribution, \(r_{1}\) becomes \(\Delta x\)\((=\Delta y)\).  
The weighting function used in this study is as follows:  
\[w(r)=\begin{cases}\exp(-\frac{6.3y^{2}}{r_{0}^{2}})&(r\leq r_{0})\\ 0&(r>r_{0})\end{cases} \tag{3}\]  
Figure 1: Schematic view of (**a**) a cloud with weighting function and (**b**) ways to construct the cloud.  
Here, \(r\) is the distance from the calculation point \(i\) to the surrounding point \(j\).