esa-sceva/satcom-chunk-collection huggingface dataset:
# A Broadband Active Microwave Monolithically Integrated Circuit Balun in Graphene Technology
## 3 Results
### Circuit Description  
The fabricated circuit is presented in Figure 2. The GFET-Balun is realized using two active GFET transistors (T1 and T2) where the sources are connected together and two loads (transistors T\({}_{\text{AL}}\)1 and T\({}_{\text{AL}}\)2) that are tied to the drains of T1 and T2 and connected to the power supplies VDD and VSS, respectively (see Figure 2b). In comparison to [15], lumped resistors are replaced by integrated active loads using transistors where the gate is connected to the source. The input signal is applied to the gates of T1 and T2 (both gates are connected together). The gate-source voltage of the graphene transistors is hence equal to VGS0 + Vac, where VGS0 is the DC-offset voltage used for accurately setting the quiescent point. The balanced output signals are measured at the drains. The circuit has been simulated with the compact model described in [25; 26] and also applying a mixed SPICE and electromagnetic simulation through ADS-Momentum. In the previous graphene simulated circuit [26] and considering the former available technology developed at University of Lille [15], circuit shows performances in the GHz range with a difference of phase lower than 10\({}^{\circ}\) up to 1.2 GHz.  
Figure 1: **(a)** 60 \(\times\) 60 \(\upmu\)m\({}^{2}\) AFM image of the graphene on SiC which we used for balun circuits. **(b)** Two representative Raman spectra of our graphene, the SiC background Raman signal has been subtracted.
## 2 A New Research Scheme for Full-Scale/Model Test Comparisons  
The proposed new research **scheme** for future full-scale/model test comparisons consists of the following steps:  
1. [label=(0)]
2. Conduct full-scale measurements for wind effects on the full-scale structure.
3. Manufacture an aero-elastic pressure measurement model and a rigid pressure measurement model of the prototype in the wind tunnel, and simulate the high Re effects on these models by roughening the surfaces of the models.
4. Conduct wind tunnel model tests for wind effects on scaled models.
5. Compare wind effects according to the in situ measurements to those according to the wind tunnel model test using the rigid model without Re effects simulation to quantify the total discrepancies, which contain Re effects, flow characteristic effects and aero-elastic effects. Then, use the wind effects measured on the aero-elastic and the rigid models with the high Re effects simulation to separate the mingled adverse effects. Specifically, flow characteristic effects can be obtained by subtracting wind effects from full-scale measurements from those using the aero-elastic model with high Re effects simulation; aero-elastic effects can be obtained by subtracting wind effects using the aero-elastic model with high Re effects simulation from those using the rigid model with high Re effects simulation; and Re effects can be obtained by subtracting wind effects using the rigid model with high Re effects simulation.
6. Reveal the most significant adverse influence on the reliability of the traditional ABL wind tunnel pressure measurement technique by comparing the three adverse effects.  
The flowchart of the new research scheme is presented in Figure 1. As can be seen, the feasibility of the new research scheme largely depends on the successful manufacture of an aero-elastic pressure measurement model. Under the engineering background of the Peng-cheng cooling tower, the following case study employs an innovative beam-net aero-elastic model manufactured using the method proposed by Ref. [19].
## 4 Simulation Results and Comparisons
### Example (4): Comparison of the Proposed AUFPSO-NTE with HAFPSO, GAPSO, HFPSO, FPSO, and PSO
Overall, the performance of the proposed AUFPSO-NTE was better than others in Example (4).
## 3 Higher-Layer Protocols: MAC & Routing
### DACap  
Distance Aware Collision Avoidance Protocol (DACAP) [20] is a handshaking protocol designed for _Ad Hoc_ Underwater Acoustic Sensor Networks. The protocol includes a power aware behavior that that it is intended to reduce power consumption by avoiding/reducing collisions and at the same time achieving good network throughput. It also minimizes the handshake time by using the tolerance to interference of receiver node, especially when receiver is close to the reception range limits. The network nodes do not need to be synchronized and it supports node mobility (dynamic scenarios). The throughput achieved by DACAP is several times higher than the one achieved with Slotted FAMA [18], while offering similar protection from collisions.  
The improvement introduced by DACAP towards the traditional CSMA/CA mechanism lies on the behavior of the receiving node when is waiting for a data packet. If it overhears a control message coming from other node, it will send a warning packet (WAR) to this node in order to let it know that there is already a transmission in process. Moreover, after receiving the CTS control packet, the sender node defers the transmission data packet for a defined delay time. The transmission attempt is aborted if by any chance the sender node receives another control or warning message. The delay times are determined according to the distance between the nodes involved in the transmission, that can be solve during the handshake by measuring the roundtrip time. Even though when the receiver node sends a warning message, it has no feedback that lets it know if the interfering node cancels its transmission. That is the reason why the receiver keeps listening to the channel after sending the warning message, and thus the defer state is set to a minimum delay time between the CTS and the DATA so that it avoids a collision.
# Virtual Prototyping of Bulk Material Preparation Devices in Mining Using Multiphysics Simulations
## 1 Introduction
### Practical Usage of DEM
Another field of application of the DEM is the prediction of wear of the machine components as a result of their operation. The wear of a mining rope shovel bucket analysis presented in [44] is an example of an analysis carried out for this purpose. Based on the results of numerical simulations, it is possible to learn more about how the excavator bucket wearsin friction, both regarding the wear location and its size. An analysis of the results allows for planning maintenance and repair work, and it also directs work to optimize the bucket design. Another aspect of using DEM simulations is the possibility of simulating the cooperation of machines during the technological process. An analysis of cooperation of a longwall shearer with the armored face conveyor (AFC) described in [45] is an example of such an application of numerical simulations in mining operations. The author paid special attention to the aspect of loading the excavated material onto the longwall conveyor with changing kinematic parameters of the longwall shearer's movement. The aim of the simulation was to optimize the cooperation of both machines and avoid emergency situations, such as burying the AFC, resulting in it getting stuck. DEM is also used on a smaller scale, namely, to optimize the cutting tools used in the mining machines. For this purpose, it is required to understand the interaction between the tool and the rock in the mining process. This issue was presented in [46], where the authors discussed simulations enabling the analysis of the rock cracking mechanism. In turn, [47] presents the possibility of using the DEM simulation method to analyze the deformation of roof rocks in mining areas. By analyzing stresses, places of stress accumulation, and geological conditions, the authors drew attention to the threats, e.g., water, occurring in a given region. By thoroughly understanding the nature of the phenomenon, the occurrence of selected hazards in the mine can be minimized. The mining industry is one of the most important sources of income in many countries, as the development of the mining industry often regulates the source of potential and economic growth of countries [48]. The mining industry includes the exploration, planning, and permissions for the building of mines, extraction of raw minerals in open-pit and/or underground mines, processing of minerals, and recultivation of mine areas at the end of their economic life [1].
## 2 Results and Discussion
### Application to the Analysis of Body Odor Changes in Various Lifestyles  
Table 3 shows the results of HS-SPME/GC-MS analysis of the samples collected from the forehead by the wiping method after a certain period of time after each treatment. 2-Nonenal secretion was significantly increased on the high-fat diet compared with the low-fat diet. 2-Nonenal secretion was also slightly increased in lotion use and smoking compared with non-use and non-smoking, but the difference was not statistically significant. In contrast, mental stress caused by long meetings significantly increased 2-nonenal secretion. This may be because stress-induced sweating increased the secretion of fatty acids and lipid peroxides from the sebaceous glands, increasing oxidative degradation of these compounds to 2-nonenal by indigenous bacteria on the body surface [1].  
Figure 5: Circadian rhythm of 2-nonenal emission from skin (forehead) (average contents of 2-nonenal from three people).  
Figure 6: Total ion chromatogram of skin emission sample (forehead, male in their 30 s).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatment** & **Sampling Time after Treatment** & **2-Nonenal Content (ng)** & **T-Test** \\  & & **Mean \(\pm\) SD (\(\emph{n}\) = 3)** & _p Value_ \\ \hline Low fat diet & 12 & 0.49 \(\pm\) 0.09 & \(p\) \(<\) 0.05 \\ High fat diet & 12 & 0.70 \(\pm\) 0.09 & \(p\) \(<\) 0.05 \\ Without using cosmetics & 6 & 0.23 \(\pm\) 0.04 & \(p\) \(>\) 0.05 \\ Using cosmetics & 6 & 0.26 \(\pm\) 0.08 & \(p\) \(>\) 0.05 \\ Before smoking & 2 & 0.27 \(\pm\) 0.08 & \(p\) \(>\) 0.05 \\ After smoking & 2 & 0.37 \(\pm\) 0.09 & \(p\) \(>\) 0.05 \\ Before meeting & 6 & 0.23 \(\pm\) 0.04 & \(p\) \(<\) 0.05 \\ After meeting & 6 & 0.35 \(\pm\) 0.09 & \(p\) \(<\) 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: 2-Nonenal contents detected in body odor samples in various life styles.
## 4 Conclusions  
In this paper, the results of biaxial quasi-static testing of 14 RC bridge piers with different axial load ratios, slenderness ratios, and reinforcement ratios were analyzed to examine their hysteretic behaviors and damage evolution under bilateral low-frequency cyclic loading. A new seismic damage model for RC bridge piers subjected to bilateral seismic load was developed, and the evaluation results are compared with those from the Usami model and the Park-Ang model. The following conclusions can be drawn from this study.  
* Four consecutive damage stages were found: (a) cracking on concrete surface, (b) yielding of longitudinal reinforcements, (c) spalling of concrete, and (d) pier collapsing after the concrete crushed and the longitudinal bars ruptured due to the flexural failure.  
* Both damage level and cumulative energy dissipation increase with displacement load. The damage level caused by the seismic waves can be reduced by designing specimens with a good seismic energy dissipation capacity.
* The cumulative dissipated energy parabolically increases with displacement load. Thus, the influence of energy dissipation on structural damage is more significant after the occurrence of reinforcement yielding, while the cumulative dissipated energy value is minimal when the displacement load is minute before the concrete cracking at the yielding displacement.
* The Usami damage model was improved by considering the impacts of energy dissipation at different displacement loads on the damage level to evaluate the damage index of hollow RC pier subjected to bilateral seismic excitations.
* The developed M-Usami damage index well characterizes the damage levels consistent with the testing results, and could be potentially used to evaluate the damage levels of hollow RC bridge pier in practice.  
Conceptualization, S.L. and Z.X.; Data curation, J.X.; Formal analysis, S.L.; Funding acquisition, Z.X.; Investigation, Z.X. and J.X.; Methodology, S.L.; Supervision, J.X.; Validation, S.L., Z.X., and J.X.; Visualization, J.X.; Writing--original draft, Z.X.; Writing--review & editing, S.L. and Z.X.  
This research was funded by the Natural Science Foundation of China (NSFC), grant number 51178101 and 51378112.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Building of Stress-Strain Relationship Model
#### 4.2.1 Development of the Proposed Models
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Swarms Size**} \\ \cline{2-5}  & **40 Swarms** & **60 Swarms** & **80 Swarms** & **100 Swarms** \\ \hline \(\alpha_{0}\) & 1.5407 & 1.0783 & 0.7533 & 0.7560 \\ \(\alpha_{1}\) & \(-\)0.0024 & \(-\)0.0001 & 0.0008 & 0.0008 \\ \(\alpha_{2}\) & 0.00003 & \(-\)0.0008 & \(-\)0.0007 & -0.0007 \\ \(\alpha_{3}\) & 1.0873 & 1.0873 & 1.0873 & 1.0873 \\ \(\alpha_{4}\) & 5.9940 & 6.8733 & 6.6133 & 5.9445 \\ \(\alpha_{5}\) & 9.1699 & 6.7863 & 8.4157 & 3.8006 \\ \(\alpha_{6}\) & 0.0310 & 0.2989 & 0.2234 & 0.0147 \\ \(\alpha_{7}\) & 0.8189 & 0.0531 & 0.6006 & 0.9368 \\ \(\alpha_{8}\) & \(-\)38.4471 & -11.0016 & 21.0096 & \(-\)0.2854 \\ \(\alpha_{9}\) & 63.6051 & 11.0420 & -36.6410 & 80.9326 \\ \(\alpha_{10}\) & 37.4007 & 62.5102 & 1.0888 & 31.4167 \\ \(\alpha_{11}\) & \(-\)47.4932 & 45.0255 & 48.2601 & \(-\)23.5096 \\ \(\alpha_{12}\) & 0.2754 & 3.5791 & 44.1699 & 61.9069 \\ \(\alpha_{13}\) & 7.1427 & \(-\)71.4840 & 39.3211 & -6.4045 \\ \(\alpha_{14}\) & 7.0842 & \(-\)43.5667 & 26.5333 & \(-\)4.3061 \\ \(\alpha_{15}\) & \(-\)89.6652 & 1.5469 & 1.0000 & 0.0886 \\ \(\alpha_{16}\) & \(-\)22.8279 & \(-\)12.7488 & 4.4237 & \(-\)17.8640 \\ \hline \(MV\) & 1.065 & 0.952 & 1.004 & 0.963 \\ \(SD\) & 0.224 & 0.161 & 0.186 & 0.122 \\ \(CoV\%\) & 21.03 & 16.95 & 18.56 & 12.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The PSO algorithm and the best possible values for the unknown coefficients. Figure 11: Using the proposed model, a comparison of predicted and experimental stress capacity. Figure 10: Convergence of swarms of various sizes. * Predicted and actual values have a very strong correlation if the model's, \(|R|>0.8\). * Good correlation can be found between actual and predicted values when an R-squared model provides \(0.2<|R|<0.8\). * When a model provides, \(|R|<0.2\), the correlation between the expected and the actual values is weak. As depicted in Figure 12, this work used a variety of statistical methods to verify the geographic variation of the expected stress capacity by the proposed model over that which was observed in a single topology. These results, as illustrated in this figure, show the superior performance of the proposed model for stress capacity forecast because they are so similar to what has been measured. Additionally, Figure 12 shows that the PSO model accurately predicts target values with a high R-value. This suggests that the model presented is capable of prediction based on low values and the applicability performance based on comparable values.
###### Abstract  
Foamed bitumen stabilisation is an attractive technology for increasing the use of marginal materials in pavement construction and rehabilitation. However, by their very nature, marginal materials do not meet the prescriptive requirements of many standard specifications. Consequently, performance-related evaluation is required. For foamed bitumen stabilised marginal materials, the cured and saturated moduli are common performance-related parameters that are also used for characterisation during structural pavement design. In this research, the indirect tensile moduli of three foamed bitumen stabilised marginal granular materials were compared to the modulus of a standard or premium material, in both cured and saturated conditions, after \(3,7\) and \(14\) days of accelerated laboratory curing. The results indicated that the magnitude of granular material marginality was not related to the stabilised material modulus. Furthermore, the gradations of the two most marginal materials were improved by blending with another granular material and the improved marginal materials were also stabilised and tested. The gradation improvement had a variable effect on the stabilised material modulus, with the average modulus increasing by more than \(20\%\). The modulus increase associated with the gradation improvement was related to the basis and magnitude of granular material marginality, with the saturated modulus of the most plastic marginal material increasing by the greater amount after improvement. It was concluded that foamed bitumen stabilisation is a particularly effective treatment for marginal granular materials. Furthermore, when used in combination with gradation improvement, the resulting foamed bitumen stabilised material can perform similarly to standard materials, based on cured and soaked modulus values. However, to allow the use of foamed bitumen stabilised marginal materials in pavement construction, specifications must be more performance-related and the current limits on plasticity and gradation must be relaxed.  
foamed bitumen; bitumen stabilisation; marginal material; improvement +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### HS-SPME Method Optimization  
#### 3.1.1 Optimization of Vial Size  
Vial size can change the headspace-to-sample volume ratio, an important factor that affects the HS-SPME efficiency [52]. We hypothesized that smaller vials would outperform the larger vials in terms of total peak area and total peak number. To evaluate our hypothesis, we tested 2 mL, 10 mL, and 20 mL vials. It should be noted that for 10 mL and 20 mL vials, a 2 cm fiber was used, whereas for 2 mL vial, a 1 cm fiber was used. Although the fiber surface area has a greater impact on the amount of VC extracted, we realized that the headspace-to-sample volume ratio can have a more substantial impact, especially for compounds with a larger partition coefficient between headspace and sample phase (Supplementary, Equation (S1)). The results show that the 10 mL vial surpassed the performance of the other two vial sizes in terms of total peak area and total peak number, showing statistically-significant differences (Figure 1). The 10 mL vial yielded the highest total peak area, with an average value of 2.69 \(\times\) 10\({}^{7}\), higher than both the 20 mL (2.37 \(\times\) 10\({}^{7}\)) and 2 mL (2.57 \(\times\) 10\({}^{7}\)) vials. Additionally, the 10 mL vial resulted in the highest mean total peak number of 290, surpassing both the 20 mL and 2 mL vials, which extracted an average of 276 and 248 peaks, respectively.  
In a system in equilibrium, the amount of volatile compounds extracted on the fiber is negatively correlated with the headspace-to-sample volume phase ratio and positively correlated with the volume of the fiber coating [52; 53]. The superior performance of the 10 mL vial, yielding both higher total peak areas and numbers compared to the 20 mL vial, can be attributed to its smaller phase ratio. Interestingly, despite the fact that the 2 mL vial has a more favorable phase ratio than the 10 mL vial, the total peak number extracted by the 10 mL vial was significantly higher than the 2 mL vial, while the difference in total peak area was not statistically significant. We speculate that the reduced fiber surface area might be a decisive factor, resulting in a smaller extracted peak area and a lower total peak number. Moreover, the broader diameter of the 10 mL vial provides an expanded surface area, facilitating the transition of a higher number of compounds into the headspace from the liquid phase [54]. Similar results were reported in an HS-SPME optimization study for urine samples, in which the 10 mL vial yielded a significantly larger mean number of volatile compounds than the 2 mL vial [54].  
Figure 1: (**a**) The total peak area and (**b**) the total peak number extracted using different vial sizes. The \(p\)-value was calculated based on the one-way ANOVA test followed by Turkey’s test. \({}^{\star}\)\(p\)-value \(<\) 0.05; \({}^{***}\)\(p\)-value \(<\) 0.001; \({}^{***}\)\(p\)-value \(<\) 0.0001. The \({}^{\prime\prime}\)ns” stands for “not statistically significant”. The shape of the points represents different vial size: triangle—2 mL vial; round dot—10 mL vial; square—20 mL vial.
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
## 5 ESA Model Performance
The ESA from a 20-story SB to a 100-story SB when \(d\) varied from 0.5 km to 32 km can be seen in Figure 13. Figure 13 shows that the ESA for a 40-story SB was distributed through 67% of the total area of the building, while for an 80-story SB, the ESA corresponded to 58% of the total area, and for a 100-story SB, the ESA occupied 52% of the total area of the building when the SB was located 2 km from the BS. Meanwhile, at small \(d\) values, a low ESA was obtained, and the ESA grew as \(d\) grew. The same figure shows that the taller buildings had lower ESA values than the smaller buildings for the same distance \(d\). This implies that the smaller buildings are more efficient at containing ESA, because as the SB grew, the ESAF decreased. This behavior was maintained regardless of \(d\) until 16 km, so it can be said that an SB improves its efficiency by between 7% and 9% each time \(d\) is doubled. A new SB could be built by considering its inner ESA, as the ESA target of an SB can be achieved by either varying the height of the building or moving it away from a reference point. Figure 14 shows that the ESA gain when \(d\) = 0.5 km can be considered as an ESA reference. This figure shows that a 60-story SB has an ESA gain of 1.16 if the building is moved 0.5 km from the reference point, but the gain will be 2.07 if \(d\) is 32 km. However, a 100-story SB will gain 1.20 in the first case and 2.46 in the second case. In addition to this, the results allow an analysis of the ESA's gain if a specific building is established as a reference at a specific distance. Table 3 shows a mapping of the ESA gain that would be obtained according to the locations and heights of buildings, taking a 40-story SB located 0.5 km from the BS as a reference. To determine the size and location of an SB that has a gain of two times the ESA of the reference, there are several options: locating a 40-story SB 32 km from the primary BS, locating a 50-story SB at \(d\) = 8 km, or using another 80 floors located 1 km from the BS. Figure 13: ESA behavior for different \(H_{SB}\) and \(d\).