esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 5 Conclusions  
In this paper, we analyze the factors that affect the channel perception capability of node devices and set the characteristic parameters accordingly. We employ the Dempster-Shafer theory to calculate the credibility and falsity of these characteristic parameters, providing a scientific evaluation of the node devices' channel perception capability. Based on this evaluation, we propose a data fusion weight allocation strategy to calculate channel occupancy statistics and accurately perceive the true state of the channel. This method can effectively eliminate the influence of node characteristic parameter fluctuation on channel occupation statistics and improve the accuracy of channel state perception. Under the same channel occupation statistics, compared to the local perception method and the existing cooperative perception, the network throughput is effectively improved and the transmission delay is reduced.  
Conceptualization, Z.Z. ([PERSON]); methodology, Z.M.; software, Y.P.; formal analysis, Z.Z. ([PERSON]); resources, J.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author. Due to policy and legal restrictions, data requests will be limited.  
The authors declare no conflicts of interest.
## 5 Results and Discussion
### Comparison of Integrated Controller Actions
For the LCC agent, it allows prioritizing the extension of the control range length to better optimize the congested traffic flow in the ramp merge areas. The experimental results support this hypothesis: under medium traffic demand, the speed limits set by the VSL agent generally do not reach the minimum speed limit of 40 km/h, thereby avoiding secondary congestion. When traffic demand changes, dynamically configuring the control area range enables the algorithm to flexibly decide whether to activate alternative control areas to optimize speed limits or lane transfer coefficients. This flexible control strategy is difficult to achieve with traditional traffic control strategies like those primarily based on SPSC. However, deciding whether to adjust a larger control area or set more extreme control commands presents a challenge for the agents. When the number of training iterations is insufficient, or the training falls into a suboptimal solution, the agents in FM-ITC-Darea might issue less effective control commands compared to the static control area strategy, FM-ITC. Additionally, although using MARL-based integrated control strategies in multi-ramp merging scenarios can effectively handle randomly changing traffic conditions, it sometimes struggles to balance the optimization of local ramp areas with overall traffic flow. Determining the control priorities of the agents can also be challenging, potentially affecting the convergence of training. To address these shortcomings, future research could further explore the trade-offs between integrated control strategies for overall and local traffic optimization and the feasibility of implementing dynamic control areas.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM  
### Architecture  
The schematic and layout of the proposed RHRSE-20T SRAM are shown in Figures 1 and 2, respectively. The RHRSE-20T features two storage nodes, Q and QB, and four internal nodes, S0, S1, S2, and S3. With a total of six nodes, it is composed of 20 transistors. Transistors N4 and N5 have a width that is 2.5 times the minimum transistor width in the process, while transistors N9 and N10 have a width that is approximately 1.7 times the minimum width. The remaining transistors maintain the minimum width and length specified by the process. The widths of the transistors were determined through a series of experiments to ensure complete immunity to SNU and to enhance certain performance aspects over existing designs. The transistor widths of N4 and N5 strengthen the pull-down of the main storage node QB, ensuring that the values of Q and QB are immune to SEU. Figure 1 shows the scenario when the cell stores a '1'. In this state, Q = S1 = S3 = '1', and QB = S0 = S2 = '0'. The read operation is performed through access transistors N11, N12, N13, and N14, while the write operation is carried out using a total of six access transistors, including N9 and N10. In Figure 3, it can be seen that the write, read, and hold operations of the RHRSE-20T are functioning normally.  
Figure 1: Proposed RHRSE-20T SRAM schematic.  
Figure 2: Layout of RHRSE-20T SRAM.
## 2 System Model
The transmission signal is  
\[X_{T_{2}}=\sqrt{\alpha_{1}P_{1}}x_{1}+\sqrt{\alpha_{2}P_{1}}x_{2}, \tag{3}\]  
where \(P_{1}\) is the transmission power of UR. The MCSs are the same as the first stage. D2D multicast (D2 MD) transmission mode is introduced into the second stage. Hence, UR must locate within the ETR of USU. To make sure that the USU served by UR can successfully receive, even if USU only has one UR, SINR of signal \(x_{2}\) provided by UR should be larger than threshold \(\sigma_{0}\)[10], and \(\sigma_{0}\geq SINR_{\mathcal{S}_{2}}\). The UR set of USU is denoted by \(\mathcal{R}_{u}=\{SU_{i,k_{i}}|\gamma_{k_{i,u}}\leq R_{0},SINR_{k_{i},u}^{x_{2} }\geq\sigma_{0}\}\). \(\gamma_{k_{i},u}\) is the distance from \(SU_{i,k_{i}}\) to USU \(u\). \(SINR_{k_{i},u}^{x_{2}}\) is the reception SINR of \(x_{2}\) from \(SU_{i,k_{i}}\). \(R_{0}\) is the radius of ETR. \[SINR_{k_{i},u}^{x_{2}}=\frac{\alpha_{2}P_{D}\big{|}h_{k_{i},u}\big{|}^{2} \gamma_{k_{i},u}^{-\beta}}{\alpha_{1}P_{D}\big{|}h_{k_{i},u}\big{|}^{2}\gamma _{k_{i},u}^{-\beta}+2N_{0}}. \tag{4}\]  
When multiple USUs select the same one UR, they comprise a D2D MG.
## 4 Mixture Design and Specim Preparations  
Porous asphalt concrete (PAC) is widely used as the material of the asphalt pavement top layer in high-grade expressways. In China's expressways, asphalt pavement usually adopts a three-layer structure, and the most commonly used materials are AC-13, AC-20 and AC-25. AC stands for asphalt concrete, and the number represents the maximum nominal particle size of aggregate in the mixtures. When PAC is applied in the pavement, PAC-13 replaces AC-13 as the top layer material. Therefore, four types of asphalt mixtures, namely PAC-13, AC-13, AC-20 and AC-25, were chosen as research objects. The gradations, optimum asphalt contents and void rates of the four asphalt mixtures are shown in Table 1. Because only the thermal properties were of concern in this paper, and the mechanical properties were not involved, all the AC mixture used PG76-22 asphalt as the binder, while PAC-13 used high-viscosity asphalt (HVA) as the binder. HVA is typically used in China to improve the raveling resistance and strength of PAC [32]. HVA is characterized by extremely high kinetic viscosity and softening point. The kinetic viscosity at 60 \({}^{\circ}\)C of HVA used in this study was 143,202 Pa-s and the softening point was 91 \({}^{\circ}\)C.  
Four specimens of each mixture type were fabricated by a Superpave gyratory compactor (SGC) with 50 gyrations, with 100 mm in diameter and 63.5 mm in height.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sieve Size (mm)** & **AC-13** & **AC-20** & **AC-25** & **PAC-13** \\ \hline
31.5 & 100 & 100 & 100 & 100 \\
26.5 & 100 & 100 & 100 & 100 \\
19 & 100 & 96.8 & 84.5 & 100 \\
16 & 100 & 88.7 & 74.9 & 100 \\
13.2 & 96.2 & 78.8 & 67.9 & 95.7 \\
9.5 & 76.1 & 64.8 & 59.5 & 64.8 \\
4.75 & 52.7 & 40.5 & 42.7 & 21.4 \\
2.36 & 37.2 & 27.3 & 31.5 & 13.5 \\
1.18 & 25.1 & 18.1 & 22.6 & 10.9 \\
0.6 & 18.1 & 12 & 15.8 & 8.9 \\
0.3 & 13.3 & 8.4 & 10.6 & 7.3 \\
0.15 & 10.6 & 6.6 & 7.4 & 6.2 \\
0.075 & 6.7 & 5.4 & 5.4 & 4.5 \\ Optimal binder content (\%) & 4.9 & 4.5 & 4.2 & 4.8 \\ Aggregate & Basalt & Basalt & Basalt & Basalt \\ Void content (\%) & 4 & 4 & 4 & 20.4 \\ \hline \end{tabular}
\end{table}
Table 1: Gradation of four types of asphalt mixtures.  
Figure 1: Image-based meso-structure modeling, simulation and results verification.
## 2 Methods
### Time Dependency Studies  
Batch studies were performed to determine time dependency of the binding of either As(III) or As(V) to the synthesized ZnO nanomaterial. Solutions of either 30 ppm of As(III) or 30 ppm As(V) were prepared at the optimum binding pH of 4.0. The samples were prepared by adding 4 mL of the pH-adjusted solutions of either As(III) or As(V) to 10 mg of ZnO in 5 mL polyethylene test tubes. Control samples consisted of only As(III) or As(V) ions in solution without the ZnO nanoparticles. The reaction and control samples were performed in triplicate for statistical purposes and were then equilibrated at time intervals of 15, 30, 60, and 90 min on a nutating mixer at a mixing speed of 24 rpm. Furthermore, the reactions were performed at temperatures of 4, 22, and 45 \({}^{\circ}\)C. After equilibration, the samples were centrifuged at 3500 rpm for 5 min, and the supernatants were decanted and stored in clean test tubes for analysis using ICP-OES.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Setting** \\ \hline L & 193.696 nm \\ RF power & 1500 W \\ Nebulizer & Gemcone (low flow) \\ Plasma flow & 15 L min\({}^{-1}\) \\ Auxiliary flow & 0.2 L min\({}^{-1}\) \\ Nebulizer flow & 0.55 L min\({}^{-1}\) \\ Sample flow & 1.50 L min\({}^{-1}\) \\ Injector & 2.0 mm Alumina \\ Spray chamber & Cyclonic \\ Integration time & 20 s \\ Replicates & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Operational parameters for ICP-OES.
# Apollonian Packing of Circles within Ellipses
## 2 Materials and Methods
### Tangent Circles in the Elliptic Domain
#### 2.3.2 Parametrization of the Hyperbola  
The geometrical tangency problem has been reduced to the determination of hyperbola \(\mathcal{I}_{1,2}\) having foci in centers \(C_{1}\) and \(C_{2}\) and passing through tangency point \(T_{1,2}\) of circles \(\mathcal{C}_{1}\) and \(\mathcal{C}_{2}\). A parametrization of hyperbola \(\mathcal{I}_{1,2}\) suitable for our purposes must take into account the general case, shown in Figure 4a, in which the hyperbola is defined by any two points \(F_{1}\) and \(F_{2}\) of the plane (with \(r_{1}\) representing centers \(C_{1}\) and \(C_{2}\)) and by a real number \(r_{1}\) such that \(r_{1}<\overline{F_{1}F_{2}}\) (representing the radius length of circle \(\mathcal{C}_{1}\)). The \(r_{1}\) number defines a point \(T\), on segment \(C_{1}C_{2}\), such that \(\overline{TC_{1}}=r_{1}\) (with \(T\) representing tangency point \(T_{1,2}\) between circles \(\mathcal{C}_{1}\) and \(\mathcal{C}_{2}\)). In Appendix A, details are given for the complete derivation of parametrization formulas for a point \(P(x_{P},y_{P})\) of the hyperbola, which result to be  
\[x_{P}=x_{T}\pm a\cos\varphi\cosh t-b\sin\varphi\sinh t,\qquad y_{P}=y_{T}\pm a \sin\varphi\cosh t-b\cos\varphi\sinh t \tag{10}\]  
where  
\[a=r_{1},\quad b=\sqrt{c^{2}-a^{2}},\quad c=\overline{F_{1}F_{2}}/2,\quad \varphi=\arctan\biggl{(}\frac{y_{2}-y_{1}}{x_{2}-x_{1}}\biggr{)} \tag{11}\]  
\[x_{C}=(x_{1}+x_{2})/2,\quad y_{C}=(y_{1}+y_{2})/2,\quad F_{1}(x_{1};y_{1}), \quad F_{2}(x_{2};y_{2}). \tag{12}\]  
The hyperbola branch may be chosen by means of the \(\pm\) sign, and the formula for tangency point \(T\) results to be  
\[x_{T}=x_{1}+k(x_{2}-x_{1}),\quad y_{T}=y_{1}+k(y_{2}-y_{1}),\quad k=\frac{r_{ 1}}{2c}. \tag{13}\]
## 3 Conventional Geolocation Techniques
### Time Difference of Arrival (TDOA)  
TDOA involves two separate stages of the hyperbolic position estimation. In the first stage, TOA estimation is measured for a signal transmitted between the emitter and sensor (\(i\)). In the second stage time difference of signal arrive (TDOA) between sensors will be calculated. In this study we considered sensor #1 as a reference. An algorithm is used to estimate the emitter location in the second stage by applying the non-linear hyperbolic equations resulting from the first stage, as illustrated in Figure 6a.  
Mathematically, the TDOA measurement model is formulated as a summation of \(\tau_{i1}\), where  
\[\tau_{i1}=\frac{(d_{i}-d_{1})}{c},\hskip 28.452756 pti=2,3,..,L \tag{4}\]  
An important benefit of TDOA is that the processing gain of correlation leads to the enhanced geolocation of signals with suppressed noise even under the receiver noise. In addition, some drawbacks occur in the TDOA method. Firstly, it is hard to achieve synchronization accuracy between all emitters and sensors, in addition to the synchronization error, which can easily translate into 300 meters of range error [36,45].  
In [44], six (06) stationary sensors were considered to geolocate a far-field stationary source based on the TDOA technique in the presence of sensor position errors. From the simulation studied, the performance of the proposed algorithm was perfect. In [46], Geometric Dilution of Precision (GDOP) was studied for a multi-station using the TDOA passive localization technique; in this study, precision GDOP with three (03) stationary sensors in a \"Y\" formation was considered. Various emitter localization scenarios were implemented based on choosing the best sensor distribution. This study concluded that the \"Y\" formation was the best one for ensuring localization accuracy.  
Figure 6: The geometry of the TDOA measurement and the performance of the proposed algorithm verified by CRLB and MSE. From (**b**), it can be noted that the performance of the proposed algorithm is very asymptotical to CRLB [44].
## 3 Results
### Engine Performance Characteristics
#### 3.1.3 Brake Thermal Efficiency (BTE)  
At full load conditions, the BTE increases, but it declines with an increasing compression ratio; it acts similar to the indicated thermal efficiency. Figure 4 illustrates the engine brake thermal efficiencies for MOB10, MOB20, and diesel fuels. According to our observations, the average brake thermal efficiency for MOB10 was 2% higher than pure diesel. However, the average brake thermal efficiency for MOB20 was 3.45% lower as compared to pure diesel. The curves were plotted by averaging three readings. Various researchers have found similar results whereby the brake thermal efficiency of the biodiesel blends was comparable with pure diesel's thermal efficiency [55; 56]. In addition, they have found that preheating biodiesel fuel before injection increases the brake thermal efficiency.  
Figure 3: Variation in BSFC with varying engine speed for biodiesel blends and diesel.
## 3 Results and Discussion
### Removal of Subsurface Impurity Defects by HF Acid Etching
#### 3.3.1 Etching Rate of Fused Silica  
Figure 10 presents the measured variation of etching rate as a function of etching time and etching depth using a stylus profilometer on the basis of a test scheme shown in Figure 3. One can see that in the initial etching process, the etching rate exhibits a sharp decrease from 46.45 nm/min to 28.81 nm/min. As the etching proceeds, the etching rate gradually descends to 23 nm/min and remains roughly stable. This phenomenon can be ascribed to the different speeds of chemical reaction of HF acid with body material (SiO\({}_{2}\)) and hydrolysis product in form of hydrated silica gel (\(\equiv\)Si-OH). The product of hydrated silica gel is formed following the process below:  
\[\equiv\text{Si-O-Si}\equiv\,+\text{H}_{2}\text{O}\,\rightarrow\,2\equiv\text{ Si-OH} \tag{5}\]  
Figure 10: The evolution of etching rate with respect to the etching time (**a**) and etching depth (**b**).  
Besides, the re-deposition layer produced in the grinding and polishing processes has a loose structure, which would enlarge its contact area with BOE and acid solvents. Hence, the etching rate appears very large in the initial etching phase. However, the curve shows a downtrend behavior because of the decreasing concentration of hydrated silica gel as the etching proceeds. When the re-deposition layer is totally removed, and the pure substrate of fused silica is exposed to the HF-based solvent, the etching rate would change slightly and keep almost constant as shown in Figure 10. Owing to the random motions of grinding and polishing particles, the density and depth of SSD defects on fused silica optics are somewhat different, even though the same processing parameters are applied. For this reason, the etching rate fluctuates with 10% amplitude in the steady etching process as shown in Figure 10. By polynomial fitting the etching rate data, we can conclude that a 200-nm etching depth and an 8-min etching time are required for steadily etching the SSD layers of polished silica optics. Based on the aforementioned discussions, it can be also inferred that the thickness of ground and polished silica optics is roughly 200 nm, which is consistent with most of the previously reported experimental results [24; 26; 45].
## 2 Dynamic Model of Robots  
The dynamic model of an n-degree-of-freedom flexible-joint robot driven by a permanent magnet synchronous motor can be expressed as  
\[\left\{\begin{array}{l}M\left(q\right)\ddot{q}+C\left(q,\dot{q}\right)\dot{q }+G\left(q\right)=K(r\theta-q)+D(r\dot{\theta}-\dot{q})\\ J_{m}\ddot{\theta}+K(r\theta-q)+D(r\dot{\theta}-\dot{q})=r\tau_{m}\\ L_{d}\frac{d\dot{q}}{dt}+R_{s}i_{d}-n_{p}\dot{\theta}L_{q}i_{q}=u_{d}\\ L_{q}\frac{d\dot{u}_{d}}{dt}+R_{s}i_{q}+n_{p}\omega L_{d}i_{d}+n_{p}\omega \Phi=u_{q}\end{array}\right. \tag{1}\]  
\[\tau_{m}=n_{p}(L_{d}-L_{q})i_{d}i_{q}+\Phi i_{q} \tag{2}\]  
where the mechanical subsystem dynamics model is derived from the model proposed in [23], and the electrical subsystem model is the electrical equation of the PMSM in the d-q coordinate system. \(q\) is the vector of joint positions, \(\theta\) is the motor position vector, \(M\left(q\right)\in R^{n\times n}\) is the inertial matrix, \(C\left(q,\dot{q}\right)\in R^{n\times n}\) is the Coriolis force and centripetal force matrix, \(G\left(q\right)\in R^{n}\) is the gravity torque vector, \(K=diag\left\{K_{i}\right\}\in R^{n\times n}\) is the robotic joint stiffness matrix, \(D=diag\left\{D_{i}\right\}\in R^{n\times n}\) is the robotic joint damping matrix, \(J_{m}=r^{2}J_{m}^{\prime}=diag\left\{J_{mi}\right\}\in R^{n\times n}\) is the constant diagonal matrix, \(J_{m}^{\prime}\) is the motor inertia matrix, \(L_{d},L_{q},R_{s},\Phi\), and \(n_{p}\) are constant diagonal matrices, which represent the inductance, stator resistance, flux linkage, and pole pair of PMSM, \(u_{q}=[u_{q1},u_{q2},...,]^{T}\in R^{n}\), \(u_{d}=[u_{d1},u_{d2},...]^{T}\in R^{n}\) are the stator voltage at the q and d axes, respectively, \(i_{q}=[i_{q1},i_{q2},...]^{T}\in R^{n}\), \(i_{d}=[i_{d1},i_{d2},...]^{T}\in R^{n}\), are the stator voltage at the q and d axes, correspondingly, and \(r\) is the reduction ratio.  
According to [19], the basic properties of some mechanical subsystems can be obtained to facilitate control the design.  
**Property 1**: _The matrix is symmetric and positive definite._  
**Property 2**.: \(\hat{q}^{T}[\hat{M}(q)-2C(q,\dot{q})]\dot{q}=0,\forall q,\dot{q}\in R\)_._
## 3 Results  
### Microstructure and Crack Deflection  
Figure 1 shows a comparison of microstructure of samples with 15 vol% alumina (15A) and 15 vol% strontium hexaaluminate (15 SA6) material sintered at 1500 \({}^{\circ}\)C and 1550 \({}^{\circ}\)C. In the 15A composites, the alumina is homogeneously distributed and most alumina grains occupy the junctions between zirconia grains. Some intragranular alumina grains trapped inside the TZP matrix grains  
were detected. In the 15A materials, the alumina grains retain their original grain size (starting powder: d\({}_{50}\) = 300 nm), while the average size of TZP grains grows from 1.2 \(\upmu\)m at 1500 \({}^{\circ}\)C to 1.6 \(\upmu\)m at 1550 \({}^{\circ}\)C. The 15 SA6 materials show plate shape SA6 precipitates which can be either single or multiple stacked lamellae (see Figure 1b,d). The average size of the SA6 precipitates does not change with sintering temperature. SA6 platelets have an aspect ratio of \(\sim\)3-5, a length of 3-6 \(\upmu\)m, and a thickness of 0.5-1 \(\upmu\)m. Zirconia grains inside SA6 precipitates are more frequently observed at low sintering temperature. Grain growth in the TZP matrix is more pronounced in SA6-rich composites due to the larger size and lower number of SA6-grains compared to alumina grains.  
Wing cracks originating from the HV10 indents were studied to observe how the cracks interact with the different elements of the microstructure, especially with the SA6 platelets. Some examples are shown in Figure 2. Evidently, crack deflection does not only happen at SA6 precipitates. The TZP matrix typically shows intergranular crack propagation. Fracture of alumina grains is not observed (Figure 2a). Depending on the angle of incidence, the orientation of adjacent zirconia grains and grain boundaries either debonding (D) of the alumina/TZP boundary occurs (the most frequent effect), the crack is stopped (S) or enters adjacent zirconia grains causing transgranular fracture (T) (Figure 2a). Moreover, cracks can be stopped in the tough matrix and be reinitiated in a distance of a few \(\upmu\)m (RI). In the 15 SA6 composites, crack deflection at TZP/SA6 boundaries (D) is observed (Figure 2b). SA6 grains as obstacles in the crack paths may, moreover, lead to more complex interaction (Figure 2c).  
Figure 1: SEM images of polished and thermally etched surfaces of (**a**) 15A-1500, (**b**) 15 SA6-1500, (**c**) 15A-1550, and (**d**) 15 SA6-1550.  
Here, the crack first breaks through a TZP grain (T), is then deflected (D) for a short distance along the TZP/SA6 boundary. Then, the crack breaks through the SA6 platelet (T), exits along the SA6/SA6 lamella boundary (D), and is stopped in the TZP matrix. Approximately 2 \(\upmu\)m away from this location, the crack is reinitiated (RI) along a TZP/TZP grain boundary until it is finally terminated within a transgranularly fractured TZP grain (T).