esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Load Calculation Models for Anti-Sliding Short Piles Using Finite Difference Method
## 4 Data Comparison
### Comparison of Bending Moments  
Figure 15 illustrates the comparison between model test and calculated model bending moments under varying loads, recorded at a depth of 20 cm both above and below the sliding surface (at the location of maximum bending moment). It is evident that, when subjected to identical loading conditions, the bending moments obtained from model testing and the theoretical calculations employing the finite difference method demonstrate relatively minor disparities. Furthermore, the fluctuation in bending moment corresponds to the three stages of pile deformation, namely the soil compaction stage, pile elastic deformation stage, and pile failure stage. As the load approaches 45-60 kPa, there is a notable increase in bending moment; this is consistent with the underlying elastic-plastic theory upon which the force calculation model relies, signifying substantial deformation of the pile body. This phenomenon aligns with the findings derived from the model experiments.  
Figure 16 presents a comparison between the calculated model values and the model test values of pile bending moment at varying depths, under loads of 20 kPa, 40 kPa, and 60 kPa. It can be inferred that the overall bending moment consistently rises with the increment in load, and the rate of bending moment increase is more pronounced in the vicinity of the sliding surface. The maximum bending moment is attained at a depth of roughly 20 cm above and below the sliding surface, signifying pile failure at this specific depth. The fluctuation of bending moment with depth under different loads demonstrates a resemblance between the finite difference anti-sliding short pile calculation model and the model test results. Both exhibit an \"S\" shape, with relatively minor discrepancies.  
Table 3 allows us to observe that the comparison between the bending moment values obtained from model tests and those calculated in this article reveals the following findings: (1) The maximum bending moment value in the pile above the sliding surface is considerably higher than that in the pile below the sliding surface. (2) The numerical values of the maximum positive and negative bending moments in the pile closely match the experimental values, with a relative error of approximately 6.9%. The positions of the maximum bending moments are both situated roughly
## 3 Result and Discussion
### In Vivo Evaluation  
During the experiment period, the animals had no physical discomfort or weight loss caused by radiation. However, the animals began to show signs of hair loss and redness on the 12 th day, and the wounds varied in size and appeared irregular (Figure 7a). All rats had obvious open wounds between day 6 and day 15. At the same time point, 83.7% of the rats in the EYO group had wounds, and most of them showed mild to moderate hair loss and redness, without large open wounds. In the PGC112 group, 50% of the rats had wounds, which were mild to moderate depilation accompanied by small open wounds. Only 33.4% of the PGC/0.5 EYO group had wounds. The wound area caused by radiation dermatitis in each group was quantified (Figure 7b). As a result, it can be seen that the use of dressing can significantly reduce the wound area compared with the Blank group. Of them all, the PGC/0.5 EYO group had the most obvious effect. By the timing of the radiation dermatitis and the size of the wound area, our study found that the Blank group  
Figure 6: Cell viability performance of mouse fibroblasts cultured with film extract on day 1 and day 3. Means with different letters were significantly different (\(p\) < 0.05, mean \(\pm\) SD, \(n\) = 5).  
and the Control group could not delay the time of radiation dermatitis wounds. The Control group could inhibit the increase of the wound area, but the effect of repairing and reducing the third-degree radiation dermatitis was limited. [PERSON] et al., also found that the use of 3M\({}^{\text{TM}}\) Cavilon\({}^{\text{TM}}\) painless skin retention film in clinical breast cancer patients can delay the later appearance of itching on the skin, but there was no significant difference in the time of first occurrence of radiation dermatitis compared with the Control group [30]. On the other hand, [PERSON] et al., used EYO mixed with chitosan to treat burn wounds in rats, and it showed an extremely significant repair effect on the 21 st day [23]. In our study, the same result was found in the EYO group, maybe because EYO itself has lecithin with hydrophilic and hydrophobic ends, which can keep the irradiated area moisturized and have an antioxidant capacity [29]. In addition, the time of greatest wound area occurrence differed significantly between the PGC films used. Regardless of EYO addition, rats treated with the PGC films had a smaller wound area and exhibited significant differences on the 15 th day compared to other groups. However, the wound area became larger subsequently, with the greatest wound area occurring on the 21 st day, which later repaired swiftly. This shows that the PGC films were able to delay the development of radiodermatitis wounds. In particular, the PGC dressings combined with EYO not only delayed wound occurrence, but also offered better alleviating and restorative effects for radiodermatitis wounds.  
Figure 7: \(Cont.\)
## 3 Result and Discussion
### Photocatalytic Properties  
Photocatalysis of the samples was evaluated by decolorization of MB aqueous solution under visible light irradiation, and the results are shown in Figures 12 and 13. Absorption of MB aqueous solution at \(\lambda\) = 664 nm was used to monitor the MB dye decolorization rate process. The decolorization rate sequence can be concluded as 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 300 \({}^{\circ}\)C, followed by 600 \({}^{\circ}\)C. Improved performance was observed for 60 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C. However, due to the presence of only agglomeration and bulk structures at 600 \({}^{\circ}\)C, the reaction rate declined.  
Figure 11: Zeta potential analysis of BiVO\({}_{4}\) microspheres.  
Figure 12: Rate of decolorization of methylene blue dye for BiVO\({}_{4}\) microspheres calcined at 400 \({}^{\circ}\)C.  
Figure 13: Rate of absorbance of methylene blue dye for BiVO\({}_{4}\) microspheres under calcination temperatures of 60 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C and 600 \({}^{\circ}\)C.  
In addition, we conducted a Total Organic Carbon (TOC) study (Figure 14) on this photocatalyst, showing that it can degrade MB dye better than commercial BiVO\({}_{4}\). Thus, it has better efficiency in decolorizing and mineralizing the dye.  
The recyclability of this photocatalyst was also studied (Figure 15). We found that this material has good stability, since it only declined from 94% to 88% after four cycles of the activity, which indicates that it has good stability in this recyclability study.
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cancer Types** & **Cell Lines** & **References** \\ \hline Brain cancer & LN18, LN229, U87 MG, T98G, U251 SP, U373 MG-CD14 & [6; 7; 8; 9] \\ \hline Breast cancer & MDA-MB-231, MCF7, MDAMB468, MDA-MB-453 & [10; 11; 12; 13] \\ \hline Cervical cancer & HeLa, CaSki & [14; 15] \\ \hline Colorectal cancer & HCT-116, SW480, LoVo, Caco2, HT29 & [16; 17; 18; 19] \\ \hline Head and neck cancer & JHU-022, JHU-028, JHU-029, SCC25, FaDu, SNU1041, SNU899, HN9, OSC 19 & [20; 21; 22] \\ \hline Leukemia & CCRFCCEM, Jurkat, THP-1 & [23; 24; 25] \\ \hline Liver cancer & SK-HEP-1, H-22 & [26; 27] \\ \hline Lung cancer & SW900, TC-1, H460, HCC1588, A549 & [28; 29; 30] \\ \hline Lymphoma & U937 & [31] \\ \hline Oral cancer & KB, SCC25, MSK QLL1, SCC1483, SCC15 & [7; 32; 33] \\ \hline Osteosarcoma & U2-0S, MNNG-HOS, SaOS-2 & [34; 35] \\ \hline Ovarian cancer & OHFC, HPMC, SKOV3 and HRA, OVCAR-3, TOV-21G, TOV-112D & [36; 37; 38] \\ \hline Pancreatic cancer & MIA PaCa2 & [39] \\ \hline Prostate cancer & LNCaP, PC-3, VCaP & [34; 40] \\ \hline Skin cancer & G361, A375 & [41; 42] \\ \hline Thyroid cancer & SNU80, BHP10-3, TPC1 & [7] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cancer cell lines shown to be responsive to cold atmospheric plasma in vitro. The selective effects of cold plasma on different cell types suggest that it is possible to find the right conditions with plasma treatment affecting only cancer cells, leaving normal cells essentially unharmed [28]. In order to outspread the therapeutic application of plasma to cancer treatment, previous observations that cancer cells are more vulnerable to plasma-induced effects than normal cells require further verification on various cell lines and for several plasma sources. In this way, CAP might offer a minimally invasive option that allows specific cell removal without interfering with the whole tissue. Due to the novelty of this field, namely the possibility of using CAP as a treatment approach for cancer, the goal of this study was to evaluate CAP cytotoxicity in different cancer cell lines and phenotypically normal fibroblast cells. This was achieved by evaluating the metabolic activity and viability of several human cancer cell lines when exposed to CAP. Further characterization of chemical and physical properties of plasma was pursued by capturing the plasma optical emission spectrum and quantifying reactive species in plasma exposed media, allowing a direct comparison with other devices reported in the literature pictured in Table 2. Thus, with this work, we clarified that a different response to CAP is better explained by intrinsic cell properties than by device characteristics and settings, as in this screening the same treatment conditions were used among all types of cells. Moreover, we showed that different cell lines display intrinsic CAP sensitivity and resistance.
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
## 3 Results and Discussion
### Methods to Improve Fiber Weave Effects  
#### 3.4.1 Flattened Fiber Weaves  
Flattened fiber weave is a type of fiber that is treated by open-filament or flatten-weave technology. Figure 11 shows the differences in the two styles of fiber weave: Compared to regular glass fiber cloth, flattened fiber weave is more dispersed, the dielectric uniformity is better, and the difference in the dielectric constant between the differential lines becomes smaller.  
The properties of fiber weaves could be expressed by a phase differential (\(\Delta\)Phase), which is the propagation delay at different transmission frequencies (see Equation (4)), and we could use a vector network analyzer to detect \(\Delta\)Phase.  
The \(\Delta\)Phase of 1078 (a type of flattened fiber weave) was much lower than that of 1080 (a type of normal fiber weave) (Figure 12). This means that a flattened fiber weave can improve a PCB's signal transmission quality. According to Equation (4), the skew values of the 1080 and 1078 fiber weaves can be obtained (4 ps/in. and 2 ps/in., respectively). Thus, for a 25-Gbps PCB, especially a backplane, a flattened fiber weave is often used for stack-up. In recent years, flattened fiber weaves have been widely used in high-speed laminates, and traditional 106 and 1080 fiber weaves have been abandoned in the Megtron 7.  
\[\Delta\text{Phase = Skew* Frequency *360.} \tag{4}\]  
Figure 11: The structure of regular weaves and flattened fiber weaves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fiber Weave Style**} & \multicolumn{2}{c}{**Warp Direction**} & \multicolumn{2}{c}{**Weft Direction**} \\ \cline{2-5}  & \(\Delta\)**Dk** & \(\Delta\)**Z0/\(\Omega\)** & \(\Delta\)**Dk** & \(\Delta\)**Z0/\(\Omega\)** \\ \hline Conventional fiber weave & 0.58 & 3.80 & 0.44 & 2.73 \\ Flattened fiber weave & 0.25 & 1.56 & 0.18 & 1.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variations in warp and weft directions of different fiber weave types.  
Figure 12: \(\Delta\)Phase of regular and flattened fiber weaves.
## 4 DMRF Protocol  
The transmission process with the DMRF protocol is depicted in Figure 2. The process is divided into five stages: initialization, data transmission, jumping transmission, jumping probability adjustment and transmission finish.  
Figure 2: Data transmission with DMRF.  
Each node maintains a dynamic routing table which is updated timely to record the states of the surrounding nodes. In initialization phase, DMRF initializes the neighbor nodes list, state list (records the information of node congestion, failure and empty area), **FCS** list, jumping probability table, and the initial transmission path. In data transmission phase, DMRF detects node failure, network congestion and VOID region, the detection methods will be described in subsections 4.1, 4.2 and 4.3, respectively. The left time of data packet will be checked to analyze if that packet need to be transmitted in a jumping mode. If none of the above conditions happen, DMRF will dynamically select a member from FCS as the next hop based on the transmission rate of data packet and local information. Once failure nodes, congestion nodes or VOID area are detected, or the left time factor of the data packet is less than the jumping threshold, the jumping transmission mode will be used. In the jumping transmission phase, each node dynamically adjusts the content of **FCS** (e.g., DMRF updates the member status in **FCS**). Then DMRF calculates the next hop node according to the jumping probability. By utilizing the jumping transmission mode, the data packet can jump over failure nodes, congestion nodes or VOID region, but it cannot guarantee the success of the jumping transmission, therefore the jumping probability adjustment phase is performed after each jumping transmission. In the jumping probability adjustment phase, DMRF adjusts the jumping probability according to the result of jumping transmission (success or failure) and feeds back the information to its upstream node (e.g., If node \(a\) fails to send a message to node \(b\), \(a\) will immediately adjust its jumping probabilities, also it will feed back this information to its upstream node not to send messages to node _b_). When the data packet is sent to the sink node, the transmission is finished. The design principle of DMRF is described below.  
DMRF adopts the similar mechanism used in [28], in which node can directly transmit data to sink node. The node selects the next hop node according to the jumping probability. If data transmission fails, the jumping probability is adjusted via feedback mechanism, which can not only avoid the effect caused by node failure, network congestion and VOID area, but also improve the transmission rate and make the possibly discarded data packet continue to be transmitted and thus lower the energy consumption of retransmission. The jumping transmission method is shown in Figure 3.
## 3 Results
### Soil CO\({}_{2}\) and N\({}_{2}\)O Flux  
Changes in precipitation only had small effects on soil CO\({}_{2}\) and N\({}_{2}\)O fluxes. These changes were less than 1% compared to baseline (Figure 4). These inconsiderable alterations were mainly due to similar litterfall amounts between baseline and climate change scenarios in response to changes in precipitation. For the case of temperature change only, we modeled approximately 0.97 and 23.18% increases of soil CO\({}_{2}\) and N\({}_{2}\)O fluxes, respectively, per 1 \({}^{\circ}\)C increase in mean air temperature. The increased temperature promoted the growth of soil microbes and thus stimulated soil CO\({}_{2}\) and N\({}_{2}\)O fluxes. We noted, under elevated CO\({}_{2}\) change only, an increase in soil CO\({}_{2}\) fluxes, but a decrease in soil N\({}_{2}\)O fluxes. Stimulated leaf photosynthetic CO\({}_{2}\) fixation, due to a high internal CO\({}_{2}\) level, naturally increased litterfall and thus soil respiration. The reduced soil N\({}_{2}\)O emissions were mainly explained by an increase in nutrient uptake of _Pinus densiflora_ to meet its growth demands, resulting in reduced soil nitrogen and losses as N\({}_{2}\)O via denitrification. When all of the climate variables were considered together, changes in soil CO\({}_{2}\) fluxes of 4.30, 7.62, 8.05, and 12.90% while changes in soil N\({}_{2}\)O fluxes of 11.53, 5.39, \(-\)3.49, and \(-\)2.89% were modeled under RCP 2.6, 4.5, 6.0, and 8.5, respectively, compared to baseline. In other words, higher soil CO\({}_{2}\) and lower N\({}_{2}\)O emissions were expected under higher greenhouse gas emission scenarios.  
Figure 3: Projected (**a**) GPP and (**b**) NPP over the 50-year study period for the scenario of precipitation change alone (first column), temperature change alone (second column), CO\({}_{2}\) change alone (third column), and all together (fourth column) under RCP 2.6 (blue), 4.5 (red), 6.0 (green), and 8.5 (orange) for the 2050s (from 2036 to 2065). Error bars are standard deviations. The variables at baseline, climate conditions based on the 2010s (from 1986 to 2015), are shown in dashed lines.
## 3 Results and Discussion
### Reaction Mechanism  
In order to further explore the structure of dissolved Au in the ionic liquid, XPS was used to characterize the samples. The binding energy of Au 4f\({}_{7/2}\) in the resultant sample was 84.3 eV, while that of the mixture of N\({}_{4441}\)Cl and HAuCl\({}_{4}\) was 84.7 eV (Figure 4), which was close to the sample. Therefore, the dissolved gold in the sample was in the form of [(C\({}_{4}\)H\({}_{9}\))\({}_{3}\)CH\({}_{3}\)N][AuCl\({}_{4}\)] ion pair.  
According to the above characterization results, the possible reaction mechanism can be inferred as Scheme 1 and Figure 5. Metallic gold (Au\({}^{0}\)) on the surface of gold sources is oxidized by TCCA at Step (1) and the resultant [AuCl\({}_{3}\)] then coordinates with Cl\({}^{-}\) in IL to form AuCl\({}_{4}\)\({}^{-}\) at Step (2), after that, a more stable ion pair is formed with N\({}_{4441}\)\({}^{+}\) through electrostatic attraction, hydrogen bond and other forces at Step (3), and finally diffuses into the bulk phase of ionic liquids.  
Based on the proposed mechanism, both TCCA and N4441 Cl were indispensable for the high leaching yield. Control experiments using pure TCCA and pure N4441 Cl both showed negligible leaching yield (1.3 and 0 wt%), while for the TCCA/N4441 Cl mixture, the leaching yield could reach 100 wt% (Figure 6). This comparative study further confirms that the synergistic effect of TCCA and N4441 Cl in the composite system is necessary for effective metal dissolving.  
The optimal ratio of N4441 Cl:TCCA:Au for the most effective gold leaching was investigated as well. The effect of the amount of ionic liquid was not investigated since it was already added in large excess, thus the concentration of ligands was much higher than that of Au\({}^{3+}\) during the coordination process. Therefore, the rate of the coordination reaction was hence believed to depend solely on the concentration of Au\({}^{3+}\) generated from oxidation of metallic Au. As shown in Figure 7, when the mass ratio of N4441 Cl:TCCA:Au was set at 300:10:1, 300:15:1, 300:30:1, 300:60:1, the average dissolution rates were 81.6, 276.6, 375, 414 mg/h, respectively. The dissolution rate of Au accelerated rapidly with the increasing TCCA amount when the TCCA concentration was low, but gradually flattened out with the further increasing TCCA:Au ratio. This observation was rationalized to the saturation of adsorbed TCCA on the surface of Au particles. When the concentration of oxidant is low, the rate of reaction would respond sensitively to the change in the concentration of oxidant since there are still plenty of empty sites for adsorption. While at higher oxidant concentration, the number of Au atoms exposed becomes limited. After all the Au sites obtained saturated adsorption, even if the concentration of TCCA in the liquid phase increased, the dissolution rate of Au would depend only on the number of surface Au atoms. Therefore, N4441 Cl:TCCA:Au = 300:30:1 is the appropriate mass ratio.  
Figure 5: Reaction mechanism diagram.  
Figure 6: Difference of gold dissolving rate of single reagent and combined TCCA and N4441 Cl.
# Non-Destructive Study of the Microstructural Effects of Sodium and Magnesium Sulphate Attack on
## 2 Materials and Methods
### Impedance Spectroscopy  
Potential was applied to saturated 2 cm-height cylindrical samples immediately after their removal from the solution and superficial drying. The equipment used to apply the potential was an impedance analyzer capable of capacitance measurements in the \(10^{-14}\)-\(0.1\) F range, with a maximum resolution of \(1\times 10^{-15}\) F. Circular copper electrodes with a flexible graphite contact surface and a diameter of 8 cm were used, and measurements were taken over a frequency range of 100 Hz to 100 MHz. Both contacting and non-contacting methods were used. For the non-contacting measurements, rectangular plastic sheets were used to insulate the sample. The equivalent circuits used were those proposed by [PERSON] et al. [34] (see Figure 1) and the results obtained were fitted to the circuit using a simplex algorithm, described elsewhere [35]. In Figure 2, an impedance measurement of a 5% silica future mortar sample obtained using the contacting method is shown. The causal, linear, and stable nature of the data recorded was validated using the Kramers-Kronig relations, described elsewhere [36]. An example of the experimental Bode plots, as well as the validation of the data obtained during this study using these relations, is presented in Figure 3.  
It has been accepted that the parameters obtained from the equivalent circuits correspond to the characteristics related to the microstructure and porous network of cementitious materials [37; 38]. The resistance \(R_{1}\) corresponds to the total volume of permeable pores crossing the whole sample, and has been observed to be greatly affected by the tortuosity of the pore network [12]. The resistance \(R_{2}\) is similar, but represents the total volume of pores (permeable and occluded), and as such is more affected by the total porosity of the sample. The dielectric capacitance \(C_{1}\) is related to the total solid volume, and the capacitance \(C_{2}\) represents the total pore area in contact with the electrolyte, which is also the total surface area of the pores in the saturated samples. Some authors have argued that by using the contacting method, it is only possible to accurately determine values for the resistance \(R_{1}\) due to problems of verifying the goodness of fit [12]. As such, the other parameters analysed were obtained using the non-contacting method. For each mortar type and solution, four different samples were tested. The variation in impedance parameters was monitored until 90 days of immersion in the sulphate solutions.
# Active Noise Control over Space: A Subspace Method for Performance Analysis
## 5 Simulation Results  
In this section, we conduct simulations to investigate the maximum achievable ANC performance in the 3-D sound field by using the WDLS and the proposed subspace method.  
When the driving signal is unit amplitude, and only the \(l^{\text{th}}\) loudspeaker produces sound, \(\eta_{lum}^{(l)}(k)=\gamma_{lum}^{(l)}(k)\). Therefore, we can capture \(\eta_{lum}^{(l)}(k)\) from the measurement of \(s(\mathbf{x},k)\) based on (6). For the WDLS method, the driving signals can be designed by the solutions of (11). For the subspace method, following the PCA, from (22), we can extract the subspace \(\mathbf{O}\) from the loudspeaker coefficients \(\eta_{lum}^{(l)}(k)\). Representing the \(\eta_{lum}^{(l)}(k)\) in the subspace as \(\mathbf{\kappa}\), and projecting the primary source into the subspace as \(\mathbf{y}\), we can derive the driving signals by solving (31).