esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
## 4 Investigated Methods to Support RTA  
Consider a Wi-Fi network consisting of an AP, RTA STAs, and non-RTA STAs. Let all non-AP STAs generate uplink traffic. In this case, we distinguish the following components of the RTA frame transmission delay: waiting for the channel to become idle, competing with RTA and non-RTA STAs, and transmission of the data frame, including ACK reception. If the RTA traffic intensity is low and the RTA transmissions are short, then the main contribution to the delay is made by contention with non-RTA STAs and waiting for the end of their transmissions. To minimize the delay component associated with contention with non-RTA STAs, we set the EDCA parameters for ACs corresponding to RTA STAs and non-RTA STAs as follows. The minimum and maximum size of the contention window will not exceed the difference between the smallest AIFSN for non-RTA traffic and AIFSN[RTA]:  
\[W_{min}[\text{RTA}]\leq W_{max}[\text{RTA}]\leq\text{min}\{\text{ AIFSN}[\text{non-RTA}]-\text{AIFSN}[\text{RTA}]\}.\]  
Such parameters guarantee that RTA STAs always win the contention with non-RTA ones.  
Next, we consider three methods to reduce the delay component associated with waiting for the end of a non-RTA transmission.
# Computational Modeling of U-Shaped Seismic Dampers for Structural Damage Mitigation
## 2 Materials and Methods
### Boundary Conditions and Meshings  
The complete damping system may be composed of multiple USSDs. However, the evaluation of the damper is considered embedded between a lower plate and an upper plate, which is fixed to the structure through bolts. Ansys Workbench 2022 R2 software is used for the simulations. As a boundary condition, the fixed support is applied on the lower bores of the damper, as shown in Figure 1a. The dimensions of the UD-40 model selected are given in Figure 1b. The loads are applied as remote displacement on one axis (Figure 1a) with a 10-cycle load protocol with a maximum displacement of 100 mm in both directions, starting at 10 mm and increasing by 10% per cycle (Figure 1c).
# A Method of Predicting Critical Derailment Speed and Analysis of Derailment Process
## 3 Calculation Methods of Critical Speed
### Comparison of Results of Different Calculation Methods
#### 3.3.2 Limit Cycle Method and Progressive Trial Method  
The nonlinear critical speed is close to 320 km/h. When the train runs on the lines with curve radii of 800 m, 1000 m and 1200 m, the critical derailment speeds calculated by the progressive trial method are 255 km/h, 285 km/h and 313 km/h, respectively. The nonlinear critical speed calculated by the progressive trial method is lower than that calculated by the limit cycle method. Vehicle derailment will occur before serpentine instability, and the three lines are set as group A. When the curve radii are 1400 m, 1600 m and 1800 m, the critical speeds of derailment are 341 km/h, 365 km/h and 383 km/h, which are higher than those calculated by the limit cycle method; the three curves are set into group B.  
The running state of trains at different speeds is shown in Figure 6. It means that the vehicle could run stably over the nonlinear critical speed. According to the derailment mechanism [18], the train derails when the vehicle system loses stability in its lateral vibration state. The reason why the rolling stock can still run smoothly under various external disturbance conditions is that the system can generate a sufficient resistance increment after the disturbance, which exceeds or balances the load increment due to the disturbance in order to maintain the original system state [19].  
The stability conditions of the equilibrium state of the train system are shown in Table 4. When the speed increases, the transverse vibration input energy \(\sigma_{\rm P}\) and the transverse vibration ultimate resistance work \(\sigma_{\rm c}\) increase. When the speed of the vehicle is under the non-derailment speed, \(\sigma_{\rm P}\) and \(\sigma_{\rm c}\) have an inconsistent growth trend. However, when the speed of the vehicle reaches the critical derailment speed, \(\sigma_{\rm P}\) and \(\sigma_{\rm c}\) approach each other [20]. That is, the train derails when the maximum energy of the transverse vibration input is greater than the ultimate minimum resistance.  
Figure 5: The critical derailment speeds under different radius curves.  
The load energy increment in the limit cycle method mainly comes from the work performed by the excitation force caused by the hunting motion. When the vehicle runs over the nonlinear critical speed, the excitation vibration input work decreases. The vehicle can be shifted from an unstable state to a stable state, and the vehicle does not derail. With the progressive trial method, excluding the change in transverse vibration input energy caused by the change of train speed, the load energy increment comes entirely from the work performed by the rail on the wheel when the vehicle passes the curve. When it exceeds the work performed by the resistance force, the train will derail.
# Analysis of a Three-Level Bidirectional ZVS Resonant Converter
## 3 Circuit Operation
Figure 14 gives the measured PWM waveforms of \(Q_{1}\)-\(Q_{4}\) under 20% load. It can observe that \(Q_{1}\)-\(Q_{4}\) can turn on under zero voltage at 20% load. For forward power operation (buck mode), the measured circuit efficiencies are 89.7% at 20% load, 92.1% at 50% load and 91.8% at 100% load under 800 V input. For reverse power operation (boost mode), the measured circuit efficiencies are 86.3% at 20% load, 89.4% at 50% load and 88.9% at 100% load under 40 V input case. Figure 15a gives the picture of the prototype circuit and the experimental setup is given in Figure 15b. \begin{table}
\begin{tabular}{c c} \hline
**Items** & **Parameter** \\ \hline High voltage \(V_{H}\) & 750 V\(\sim\)800 V \\ \hline Low voltage \(V_{L}\) & 48 V \\ \hline Rated power \(P_{o}\) & 1440 W \\ \hline Resonant frequency \(f_{r}\) & 100 kHz \\ \hline High-side capacitances \(C_{H1}\), \(C_{H2}\) & 330 \(\mu\)F/400 V \\ \hline Low-side capacitance \(C_{L}\) & 4400 \(\mu\)F/100 V \\ \hline Resonant capacitance \(C_{r}\) & 47 nF \\ \hline Flying capacitance \(C_{f}\) & 2.2 \(\mu\)F \\ \hline Resonant inductance \(L_{r}\) & 54 \(\mu\)H \\ \hline Parallel inductance \(L_{b}\) & 230 \(\mu\)H \\ \hline Power switches \(S_{1}\)-\(S_{4}\) & IRG4 PC40W (600 V/20 A) \\ \hline Power switches \(Q_{1}\)-\(Q_{4}\) & IRFB3307 (75 V/150 A) \\ \hline Power switch \(S_{ac}\) & G20N50C (500 V/20 A) \\ \hline Clamp diodes \(D_{a}\), \(D_{b}\) & HFA15 TB60 PBF (600 V/15 A) \\ \hline Winding turns of \(T\): \(n_{H}\), \(n_{L}\) & 48, 6 \\ \hline Magnetizing inductance \(L_{m}\) & 540 \(\mu\)H \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters and specifications of the presented converter. Figure 6: Measured PWM signals of \(S_{1}\)–\(S_{4}\) at full load (a) \(V_{H}\) = 750 V (b) \(V_{H}\) = 800 V (\(v_{S1,g}\)–\(v_{S4,g}\): 10 V/div; time: 2 \(\upmu\)s). Figure 10: Measured PWM waveforms of \(S_{1}\)–\(S_{4}\) at 20% load (**a**) \(S_{1}\) waveforms (**b**) \(S_{2}\) waveform (**c**) \(S_{3}\) waveform (**d**) \(S_{4}\) waveform (\(\tau_{S1,g}\)–\(v_{S4,g}\): 10 V/div; \(v_{S1,d}\)–\(v_{S4,d}\): 200 V/div; \(i_{S1}\)–\(i_{S4}\): 2 A/div; time: 1 \(\upmu\)s). Figure 11: Measured PWM signals of \(Q_{1}\)\(\sim\)\(Q_{4}\) for reverse power operation and full load (a) \(V_{L}\) = 36 V (b) \(V_{L}\) = 52 V (\(v_{Q1,g}\)\(\sim\)\(v_{Q4,g}\): 10 V/div; time: 2 \(\upmu\)s). Figure 12: Measured results of \(i_{Lr}\), \(i_{Lb}\) and \(v_{Cr}\) for reverse power operation and full load (a) \(V_{L}\) = 36 V (b) \(V_{L}\) = 52 V (\(i_{Lr}\), \(i_{Lb}\): 10 A/div; \(v_{Cr}\): 500 V/div; time: 4 \(\upmu\)s).
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 3 Optically Interconnected Multi-Chip Machine-Learning System
### Design Objectives  
To fairly demonstrate the potential of optical interconnects for the machine-learning architecture, the design objectives should be aligned to designing a real product. Our implementation is based on the following design objectives:  
* Reasonable cost: Although additional cost can be justified by achieving better performance, optical components are still expensive in terms of PHY sizes on silicon and link costs (cable or other forms of waveguides). We keep interconnects from becoming a major factor of the entire system cost.
* Feasibility with current technologies: DaDianNao chip was implemented with a 28 nm process [7]. As discussed in Section 3.2, 100 Gbps of bandwidth can also be achieved by current photonics technology. Our first goal is to apply 100 Gbps interconnects to the current DaDianNao.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Parameter** & **AlexNet** & **VGG** & **VGG** & **Inception** & **Inception** & **ResNet** & **ResNet** & **ResNet** & **DenseNet** \\ \hline  & [1] & **16**[2] & **19**[2] & **- v1**[4] & **-v3**[5] & **50**[3] & **101**[3] & **152**[3] & **121**[6] \\ \hline Year introduced & 2012 & 2014 & 2014 & 2015 & 2015 & 2015 & 2015 & 2015 & 2016 \\ \# of layers & 14 & 22 & 25 & 73 & 111 & 125 & 244 & 363 & 306 \\ (convolution/ & (5/3) & (13/3) & (16/3) & (20/1) & (56/1) & (49/1) & (100/1) & (151/1) & (120/1) \\ fully connected) & & & & & & & & & \\ Total synapse [MB] & 233 & 528 & 548 & 27 & 105 & 97 & 170 & 229 & 30 \\ Total neuron \({}^{*}\) [MB] & 172 & 1860 & 2019 & 587 & 1446 & 3290 & 4956 & 7014 & 4061 \\ Ratio in size & 1.35 & 0.28 & 0.27 & 0.05 & 0.07 & 0.03 & 0.03 & 0.03 & 0.01 \\ (synapse/neuron) & & & & & & & & & \\ \hline \hline \end{tabular}  
* Input image size and batch size are assumed as 224 \(\times\) 224 \(\times\) 3 and 32, repectively.  
\end{table}
Table 2: Summary of the evaluated CNN models.  
* One step forward with the next generation of optical interconnects: In a few years, optical components for 400 G standards will be available. Beyond 100 Gbps, we apply 400 Gbps interconnects to DaDianNao and compare its performance with the existing DaDianNao system.
## 5 Results
### Experimental Results  
In Figure 4 and 5, we calculated the errors of different ranges: 0-5, 0-10, 0-15, 0-20, 0-25, etc. Each range has two characteristics of the normal distribution and mean distribution. Figure 4 reflects the absolute error in the process of UAV course correction, and it describes the relative error in the process, that is, the ratio of absolute error to its expectation. We can clearly see that although position error after iterations will increase accordingly; they all converge in the end and stabilize below two unit lengths. At the same time, combined with the error ratio curve in Figure 5, we can see that the ratio between final error and distance is stable at about 0.1, so the algorithm for adjusting position is considered to be excellent.  
Then, we compared the errors of other algorithms. After our many experiments, we obtained the performance trend of the commonly used DV algorithm and TDOA algorithm. The performance of the above algorithm is independent of the number of iterations, and multiple trials are only to avoid accidental events. Detailed experimental results are found below. Figure 6 shows three improved DV-hop positioning algorithms, among which the error of the algorithm with the best performance is still above 0.2. Figure 7 is the TDOA algorithm based on Chan's law. We have calculated the error under different numbers of positioning points. It can be seen that the error ratio of the algorithm fluctuates between 0.1 and 0.7. From the trend in the graph, we can see that the error fluctuates between 0.1 and 0.7, and only a few specific experiments have an error below 0.1.  
Figure 3: Circular formation signal reception strategy.  
Figure 4: Positioning error at different degrees of offset.  
Figure 5: Proportion of positioning error.  
Figure 6: Positioning error of multiple DV algorithms.  
Last, we chose a case where the initial error is between 0 and 15 unit lengths and which follows normal distribution to show the iterative process. Figure 8-11 shows that when we transmit signals according to the rule of Figures 2 and 3, after the ninth group of UAVs completes launch, the flight array basically returns to its correct position, so the algorithm can realize the position adjustment within one complete iteration.  
Figure 8: Formation situation after one iteration.  
Figure 7: Positioning error of TDOA.  
Figure 9: Formation situation after three iterations.
# Response Analysis of the Tristable Energy Harvester with an Uncertain Parameter
## 4 Parametric Uncertainty Analysis
### Combined Influence of the Damping Coefficient and Electromechanical Coupling Coefficient  
In order to show the combined influence of uncertain parameter \(\alpha\) on the damping coefficient and electromechanical coupling coefficient, the TLEs with the variation of \(\zeta\) and \(\alpha\) under different intensities \(\sigma\) are presented in Figure 12. Other parameters in system (19) are set to \(\bar{\lambda}=-4.5\), \(\mu=2.5\), \(\zeta\)= 0.2, \(\alpha\)= 0.9, \(A=1.2\), \(\omega=0.9\) and \(\beta=0.5\).  
For \(\sigma=0.0\), the TLEs keep negative at most points of the region \(\big{\{}(\zeta,\alpha)|0<\zeta<2,\ 0<\alpha<2\big{\}}\), which means that responses of harvester are almost always in the state of periodic motion. As \(\sigma\) increases to 0.01, there are some more positive TLEs for smaller damping coefficient \(\zeta\) and smaller electromechanical coupling coefficient \(\alpha\) occurred in Figure 12b, where the fluctuation of TLE is obvious. Furthermore, when \(\sigma\) increases from 0.01 to 0.05, the surface of the TLE becomes more unsmooth and more positive TLEs can be found in Figure 12c. These illustrate that the motion of harvester becomes more complex with the increasing of \(\sigma\). Thus, the uncertainty could not be ignored in the TEH and we should control the randomness at a rather smaller level to reduce the influence of the uncertain parameter.
# A Review of Geometry, Construction and Modelling for Carbon Nanotori
## 5 Physical Roles, Properties and Applications
### Electronic Properties  
Electrical properties of carbon nanotori can be classified into three categories depending on the energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). For those obtained by experimental techniques, the classification can be simplified with the following rules. Given \((m,n)\) as the components of the chiral vector \(C\) and \((p,q)\) as the components of the transverse vector \(T\) on a graphene sheet as displayed in Figure 13, the carbon nanotori have metallic characteristics if the values of \(m-n\) and \(p-q\) are both divisible by three. If only the difference \(m-n\) is divisible by three, they are semiconductor tori, while the nanotori are insulator tori if only \(p-q\) is divisible by three, otherwise they do not exist [53].  
The deformation of the carbon nanotube occurring in the bending construction also affects the electronic properties of the resulting carbon nanotori. Localised deformations lead to a dramatic reduction in the electrical conductance, while delocalised deformations only slightly lower the conductance [54]. Deformations also play an important role in the energy gap between HOMO and LUMO. As the size of the carbon nanotori increases, the oscillation fluctuates more wildly in the energy gap and converges to that of the corresponding infinite nanotube when the size is sufficiently large [55]. Further, the electronic features of the carbon nanotori can be altered depending upon the external electric and magnetic fields [56].  
Figure 12: Schematic showing offset fullerene and nanotorus.  
For those carbon nanotori with pentagon-heptagon pairs, the energy gap between HOMO and LUMO may be calculated using the extended-Huckel calculations and the tight binding approach. The density of states distribution shows that the number of mobile electrons is directly proportional to the number of carbon atoms of the tori. In addition, the charge density distribution in the interior cavity of the larger carbon nanotori is less than that of the smaller tori, implying an almost charge free region (white area) inside the larger carbon nanotori which may be used for encapsulation applications [57; 58]. Figure 14 shows the charge distributions of typical carbon nanotori C\({}_{170}\), C\({}_{250}\), C\({}_{360}\), C\({}_{520}\) and C\({}_{750}\) which possess five-fold symmetry [18].  
Figure 14: Charge distributions of carbon nanotori C\({}_{170}\), C\({}_{250}\), C\({}_{360}\), C\({}_{520}\) and C\({}_{750}\) obtained from [18]. Reprinted from Journal of Molecular Structure (Theochem), 681, [PERSON] and [PERSON] and [PERSON] and [PERSON], Electronic properties of carbon nanotoroidal structures, 231–234, Copyright (2004), with permission from Elsevier.  
Figure 13: Chiral (solid) and transverse (dotted) vectors.
# LEO-Based Satellite Constellation for Moving Target Detection
## 2 Materials and Methods
### Pre-Compensation of Spaceborne Echo for LEO Satellite
However, the echoes related to multiple satellites cannot be directly integrated due to the distinct orbit parameters in respect of the self-transmitting and self-receiving operation mode, and the approximate Equation (4) is not valid here. To realize the universality of the multi-source fusion detection in respect of LEO-based satellite constellation, the robust fusion detection method should be presented to meet the application requirements of communication, navigation and radar with different orbital altitudes. In this section, the pre-compensation method of multi-source echoes is introduced based on the prior information, i.e., high-speed motion characteristic and spaceborne observation configuration. By applying the Fast Fourier Transform (FFT) operation to the range-compressed signal, the echo of the \(k\)th satellite can be expressed as:  
\[S_{k}(f_{\tau},\eta)=\sigma_{\eta}P(f_{\tau})rect(\frac{\eta}{T_{a}})\exp[-j2 \pi\frac{f_{\tau}+f_{c}}{c}R_{k}(\eta)] \tag{6}\]  
where \(f_{\tau}\) and \(\eta\) represents the fast-frequency and slow-time, \(T_{a}\) is the dwell time, \(f_{c}\) is the carrier frequency. \(P(f_{\tau})\) denotes the Fourier transform of the range envelope. \(R_{k}(\eta)\) stands for the monostatic range of the \(k\)th satellite. Since the ephemeris and the antenna beam directivity of each satellite are known, the pre-compensation of the whole observation scene can be carried out according to the range and Doppler history of the cell where the beam center is pointed. The pre compensation function is given by:  
\[H_{k}(f_{\tau},\eta)=\exp[-j2\pi\frac{f_{\tau}+f_{c}}{c}R_{k}(\eta)] \tag{7}\]  
Based on the above pre-compensation operation, the Doppler band broadening caused by the high-speed motion platform as well as the DFR offset resulted from the observation configuration can be effectively alleviated.
# Effects of Climate Change on 2-Methyllisoborneol Production in Two Cyanobacterial Species
###### Abstract  
2-Methyllisoborneol (2-MIB) is a common off-flavor metabolite, and it has a very low odor threshold value. 2-MIB is produced by a variety of cyanobacteria and heterotrophic bacteria. In this study, _Dolicospermnioides (Anabaena spiroides_, FACHB 498) and _Planktonrix_ sp. (FACHB 1371) were investigated. The influence of high temperature (27, 35 and 40 \({}^{\circ}\)C) and dissolved inorganic carbon (DIC, 18.4, 27.5 and 36.7 mg/L) on the two 2-MIB-producing cyanobacteria were simulated to study the effect of climate change on 2-MIB production. DIC had an effect on 2-MIB production by _Planktonrix_ sp. The results showed that the highest DIC concentration (36.7 mg/L) led to the fastest algal growth, which increased 2-MIB production by _Planktonrix_ sp. However, 40 \({}^{\circ}\)C killed all the cyanobacteria. The higher temperature (35 \({}^{\circ}\)C) shortened the lifecycle of the two cyanobacteria because it moved forward their logarithmic phase compared to 27 \({}^{\circ}\)C. The 2-MIB-producing potential of _Dolicospermnioides_ may increase at 35 \({}^{\circ}\)C. The _Dolicospermnioides_ results showed that the fluorescence intensity and the concentration of extracellular 2-MIB were highly correlated (\(r^{2}\geq 0.90\)). At 35 \({}^{\circ}\)C, there was a stronger correlation between extracellular dissolved organic matters' (DOMs, fulvic acid (P3 (\(r^{2}\) = 0.90)), humic acid (P4 (\(r^{2}\) = 0.92)) and the soluble microbial metabolites' (P5 (\(r^{2}\) = 0.71)) fluorescence peak intensities and 2-MIB concentration. The _Dolicospermnioides_ 2-MIB-producing potential was affected by extracellular humus. The _Planktonrix_ sp. 2-MIB-producing potential declined in general over the growth period. In addition, temperature had no significant effect on the _Planktonrix_ sp. 2-MIB-producing potential and the extracellular 2-MIB component.  
Keywords:2-methyllisoborneol; dissolved inorganic carbon; _Dolicospermnioides_; _Planktonrix_ sp.; climate change +
Footnote †: journal:
## 4 Multi-Scale High-Dimensional Mapping Entropy (MHDME)
### Refined Composite Multi-Scale Studies
#### 4.1.2 Refined Composite Multi-Scale High-Dimensional Mapping Entropy (RCMHDME)  
The RCMHDME algorithm improves the accuracy of entropy estimation by summing all coarse-grained sequences under the scale factor \(\tau\) and then calculating the high-dimensional mapping entropy, and the probability of undefined entropy appearing in MHDME is effectively reduced. The steps are as follows:  
(1)  
The Equation (26) is used to preprocess the original time series \(\{x_{1},x_{2},\cdots,x_{n}\}\) and obtain \(\tau\) a coarse-grained time series of \(y_{k}^{\tau}=\left\{y_{k,1}^{(\tau)},y_{k,2}^{(\tau)},\cdots,y_{k,p}^{(\tau)}\right\}\):  
\[y_{k}^{\tau}=\frac{1}{\tau}{\sum_{i=(j-1)\tau+k}^{j\tau+k-1}x_{i}}\ \ \ (1\leq j\leq\frac{N}{\tau},1\leq k\leq\tau) \tag{28}\]  
(2)  
Given a statistically determined scale factor \(\tau\), under which all coarse-grained time series satisfy the conditional vector pair \(N_{k,\tau}^{m}\) and \(N_{k,\tau}^{m+1}\).  
(3)  
Calculate the mean \(\bar{N}_{k,\tau}^{m}\), \(\bar{N}_{k,\tau}^{m+1}\) of individual \(N_{k,\tau}^{m}\) and \(N_{k,\tau}^{m+1}(1\leq k\leq\tau)\), respectively. To log the ratio of \(\bar{N}_{k,\tau}^{m}\) to \(\bar{N}_{k,\tau}^{m+1}\), define RCMHDME:  
Figure 6: Comparison of entropy for different lengths \(N\).  
\[CMHDME(x,\tau,m,r,sigma)=-ln\frac{\bar{N}_{k,\tau}^{m+1}}{\bar{N}_{k,\tau}^{m}} \tag{29}\]  
Collating the above equation yields:  
\[RCMHDME(x,\tau,m,r,sigma)=-ln\frac{\sum_{k=1}^{\tau}N_{k,\tau}^{m+1}}{\sum_{k=1 }^{\tau}N_{k,\tau}^{m}} \tag{30}\]  
Only when both \(N_{k,\tau}^{m}\) and \(N_{k,\tau}^{m+1}\) are 0, the entropy presents undefined results, and compared with the multi-scale coarse-grained process, the refined composite process is more suitable for the analysis of short-time series, the computational accuracy is improved, and the obtained entropy results are better in terms of stability and consistency.
## 4 Verification of Repair Effect
### Water Absorption  
The water absorption of the concrete specimens before and after repair was tested in accordance with GB/T 11969-2020 [35], the process for the water absorption test is shown in Figure 14, and the results are shown in Table 11.  
It can be seen from Table 11 that at 300, 400, 500, and 600 \({}^{\circ}\)C, the water absorption of the repaired specimens was 22.59%, 23.97%, 28.93%, and 34.32% lower than that of the damaged specimens, and 1.05%, 1.57%, 4.46% and 11.02% higher than that of the undamaged specimens, respectively. Under high temperatures, both the surface and interior of the specimens were seriously damaged, which was more conducive to the mineralizing system entering the specimens through the surface defects and flowing into the interior through the penetrating cracks.  
In conclusion, the compressive strength showed a certain degree of recovery, while the water absorption exhibited a certain degree of reduction at each damage temperature. Overall, the higher the damage temperature, the more obvious the repair effect.