esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 7 Numerical Simulation
### Simulation A  
In this simulation, several scenarios are constructed to show the performance of the VB algorithm on low traffic load. The MA algorithm and traffic lights are used as comparison. The green times of these traffic lights are 17 s, 22 s and 27 s, which are denoted by TLA (traffic light A), TLB (traffic light B) and TLC (traffic light C), respectively. The mean traffic load of each road varies from 100 vehicle/h to 600 vehicle/h in the simulation.  
Maximum travel time (\(t_{max}\)) is a commonly used index to compare the performances of different intersection management methods, which can be obtained by taking the maximum value of the travel times of all CAVs. Figure 23 shows the maximum travel times of the intersections managed by VB, MA, TLA, TLB and TLC in 1800 s. The maximum travel times of the scenarios using VB algorithm are further plotted in Figure 24. Figure 23 suggests that with the growing of traffic load, the maximum travel times of the intersection controlled by these algorithms do not show notable increasement. This figure indicates that, on low traffic load, the change of traffic load has little effect on the performances of these algorithms. The maximum travel times of the traffic lights methods are about 250 s on different traffic loads. The maximum travel times of the MA algorithm are higher than 150 s but lower than 200 s, which are better than these of the traffic lights methods. As a comparison, the maximum travel times of the intersection controlled by the VB algorithm are lower than 150 s, which are much less than these of the MA algorithm. These comparisons indicate that, compared with traffic lights and MA algorithm, the VB algorithm can increase traffic efficiency and save travel time for CAVs on low traffic load. The main reason is that the CAVs can be coordinated by the VB algorithm without waiting for the green light. Several candidate trajectories can be selected to find the optimal one for each CAV. Thus, they can pass the intersection with constant desired speed and little delay. For the traffic lights methods, the CAVs have to wait for green lights, which is low efficient and time consuming on low traffic load. For the MA algorithm, on low traffic load, there are a few conflicts among the trajectories of approaching CAVs. Most of these CAVs can pass the intersection without delay. While, when there is a conflict between the trajectories of two CAVs, one of these CAVs has to slow down and wait for permission next control circle. Thus, the MA algorithm shows better performance than the traffic light but worse than the VB algorithm on low traffic load.  
Figure 23: Maximum travel time of the intersection on low traffic load.  
Figure 24: Maximum travel time of the intersection controlled by the VB algorithm on low traffic load.
## 4 Numerical Results
### Influence of System Parameters
#### 4.1.3 Effect of Recharge and Unequal Initial Energies
As already mentioned, in practice such variations will always occur due to causes other than mobility; the spikes in the activity levels demonstrate the action of the optimization to compensate for these variations. Figure 9: Residual energies obtained by the proposed optimization method for \((w_{1},w_{2})=(1,0)\) (**a**) and \((w_{1},w_{2})=(0,1)\) (**b**) with recharge and unequal initial energies. The non-optimized network is also considered for reference.
# Manufacturing of Micro-Lens Array Using Contactless Micro-Embossing with an EDM-Mold
## Materials and Methods
### Ring Compression Test  
Figure 1a shows that the PMMA rings were fabricated with an internal diameter (ID) of 6 mm, an outer diameter (OD) of 12 mm, and a height (H) of 4 mm, which is a standard ring ratio of OD:ID:H = 6:3:2. Figure 1b shows that effect of friction magnitude on the PMMA deformation during the ring compression test. The high friction force leads to an inward flow of the PMMA material when a ring is compressed between flat molds. On the contrary, the inner diameter of ring increases when the friction is low. Therefore, we can obtain the friction coefficient between PMMA and mold by this relationship. The surface roughness of the PMMA ring is between Ra 5 to 7 nm (see Figure 1f). Our team conducted a series of ring compression tests on SZU's PGMM30 (see Figure 1c,d). The heating chamber provided a vacuum, or Nitrogen environment, for the embossing process. The upper mold remained stationary, while the lower mold was driven upward and downward by an AC servomotor system. The load of the system was monitored by a load cell with a resolution of 1 N. During the embossing stage, the lower mold position was recorded by a position sensor with a resolution of 1 \(\upmu\)m. To obtain  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **PMMA** & **SKD-11** \\ \hline Young’s Modulus (GPa) & 2.4 & 210 \\ Poisson ratio & 0.37 & 0.2 \\ Density (kg/m\({}^{3}\)) & 1185 & 7700 \\ Thermal conductivity (W/m\({}^{2}\cdot\)K) & 0.2 & 200 \\ Specific heat (J/kg\(\cdot\)K) & 1466 & 460 \\ Thermal expansion (K\({}^{-1}\)) & 4.4 \(\times\) 10\({}^{-4}\) & \(11\times 10^{-6}\) \\ Glass transition temperature (\({}^{\circ}\)C) & 105 & - \\ Melting temperature (\({}^{\circ}\)C) & 220 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The physical properties of polymethyl methacrylate (PMMA) and SKD-11 mold.  
the relationship between friction coefficient and temperature, a series of rings were compressed under different temperatures (90, 105, 120, 135, and 150 \({}^{\circ}\)C). Figure 1e shows the history of temperature, force, and position time. Figure 1f shows the compressed PMMA ring under different temperatures.
## 5 Conclusions  
In this work, we proposed a latency optimization design for low-latency MEC networks. We optimized the average AuD for tasks through UEs selection and blocklength  
Figure 11: The average task AuD under different ranges of data packet size.  
Figure 10: The average task AuD under a different number of UEs.  
allocation. Specifically, we considered random fading channels and packet transmission with FBL codes. We formulated an optimization problem to minimize the average task AuD under error probability constraints and, based on the FBL regime, transformed the joint optimization problem into an effective UE selection problem. Due to the dynamic task generation and random fading channels, the problem cannot be well solved analytically. We further proposed a DRL approach with the action-masked PPO method, which dynamically masks actions that cannot satisfy error probability constraints.  
Simulation results validated the effectiveness of our proposed EMPPO method. In particular, our method avoids exploring a large volume of the ineffective action space through the mask, significantly outperforming standard PPO methods. It also adapts better to scenarios with poor channel SNR and heavy task loads, reflecting the adaptability of DRL-based methods to dynamic environments. It is also worth mentioning that, although we assumed real-time CSI can be obtained, the proposed design can be extended to scenarios where only outdated or imprecise CSI is available, which is more realistic. Under such conditions, strict error probability constraints cannot be enforced, representing an exciting direction for our future work.  
[PERSON] designed the original idea, performed the simulations, and drafted the manuscript. [PERSON] and [PERSON] gave some suggestions and reviewed the manuscript. All authors have made substantive intellectual contributions to this study. All authors read and approved the final manuscript.  
This research was supported by the Key Research and Development Project in the Hubei Province of China under grant No. 2022 BCA035.  
Not applicable.  
All data generated or analyzed during this study are given in Section 4.  
We would like to acknowledge all the subjects and technicians for their participation in and support of this study.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MEC & Mobile Edge Computing \\ FBL & Finite Blocklength \\ UE & User Equipment \\ AoI & Age of Information \\ AuD & Age upon Decision \\ PPO & Proximal Policy Optimization \\ IoT & Internet of Things \\ URLLC & Ultra-Reliable Low-Latency Communications \\ MDP & Markov Decision Process \\ SNR & Signal-to-Noise Ratio \\ FIFO & First-In-First-Out \\ GAE & Generalized Advantage Estimation \\ EMPPO & Error Probability-Controlled Action-Masked Proximal Policy Optimization \\ \end{tabular}
## 4 Density for Clustering Calling Patterns and Detecting Outliers
### Recursive Density Estimation (RDE)  
According to Equation (4), all the calls data need to be stored to calculate the measure density. However, our proposal is related with the callers clustering and detection of outliers from continuous and real-time call data. For this reason, the use of a novel technique developed and published [41,42] to calculate this value (density) recursively (RDE--Recursive Density Estimation) in a very computational efficient manner is proposed. This way, it is not necessary to store all the calls data--what is essential in this research--since a recursive algorithm that gives exact (not approximate) expression for the density is used. This technique has been used for anomaly detection in different environments, such as video streams [43], recognition of human activities in an intelligent home environment [44], and many others.  
As it is detailed in [8], the density value can be calculated recursively using Equations (5)-(7):  
\[D(x_{k})=\frac{1}{1+\ \|x_{k}-\ \mu_{k}\|^{2}+\ \sum_{k}-\|\mu_{k}\|^{2}}, \tag{5}\]  
where both the mean \(\mu_{k}\) and the scalar product \(\sum_{k}\) can be updated recursively as follows:  
\[\mu_{k}=\frac{k-1}{k}\ \mu_{k-1}+\ \frac{1}{k}\ x_{k}\ \mu_{1}=x_{1}, \tag{6}\]  
\[\sum_{k}=\ \frac{k-1}{k}\ \sum_{k-1}+\frac{1}{k}\ \|x_{k}\|^{2}\ \sum_{1}=\|x_{1}\|^{2}, \tag{7}\]  
As it has been applied in other environments, the idea behind using RDE for outliers' detection is that a low density of an observation defines an outlier and a high density is related with the creation of a new prototype in the clustering task. Usually low density means lower than \(2\sigma\) or \(3\sigma\) where \(\sigma\)is the standard deviation, which measures the dispersion of the previous set of data from its mean. This threshold (2\(\sigma\) or 3\(\sigma\)) is needed since it is necessary to determine if an observation is sufficiently different than the previous ones [45].  
This idea will be applied in the proposed case study (Section 5) to cluster callers' behaviours (Sections 5.2.1 and 5.3.1), and to detect calls outliers (Sections 5.2.2 and 5.3.2).
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 4 Experiments  
In this section, we evaluate our audio module, visual module, and two-stage hybrid fusion strategy on a publicly available dataset for remote sensing audiovisual scene classification. We first introduce the dataset that will be used, as well as the experimental setup and evaluation metrics. Second, we compared these three modules to current methods and experimentally show their efficacy.
# Characterization of Graphene Carbon Produced by Pulsed Laser Ablation of Sacrificial Carbon Tapes
## 3 Results
The G band is a Raman-active E\({}_{\text{2g}}\) optical mode at the center of the Brillouin zone, and it indicates the presence of sp\({}^{2}\) carbon networks [45; 46; 47]. The D band is a defect-induced breathing A\({}_{\text{1g}}\) mode of six-member carbon rings. It originates from TO phonons near the BZ edge, and is active by double resonance [47; 48]. The 2D band corresponds to the overtone of the D band. It originates from two-phonon scattering, and no defects are required for its activation [47; 48]. Both D and 2D bands are due to the double resonance. The Raman spectra also feature the peak D + D\({}^{\prime}\) (\(\sim\)2940 cm\({}^{-1}\)), which is a product of the two-phonon scattering process in the presence of defects [48]. Figure 4b shows the first-order region of the Raman spectrum of the GLC/sapphire-1 sample with Lorentzian fitting. The fitted spectrum includes the following five components: G, D\({}_{1}\)(D), D\({}_{2}\)(D\({}^{\prime}\)), D\({}_{3}\), and D\({}_{4}\). The D\({}^{\prime}\) (\(\sim\)1620 cm\({}^{-1}\)) is a defect-activated peak induced by a double-resonance \begin{table}
\begin{tabular}{c c c c c c c} \hline
**C1s** & **A** & **B** & **C** & **D** & **E** & **sp\({}^{2}\)/sp\({}^{3}\)** \\ \hline \multirow{4}{*}{TCS-CARB-1} & C=C (sp\({}^{2}\)) & C-C (sp\({}^{3}\)), & C-O-C, & \multirow{4}{*}{C=O} & \multirow{4}{*}{O=C-OH} & \multirow{4}{*}{0.47} \\  & C=C-COOH & C-OH & C-OH & & & \\ \hline \multirow{2}{*}{TCS-CARB-1} & 21\% & 46.4\% & 22.2\% & 5.2\% & 5.2\% & 5.2\% \\  & (284.3 eV) & (285.6 eV) & (287 eV) & (288.3 eV) & (289.9 eV) & \\ \hline \multirow{2}{*}{GLC/Ag} & 59.3\% & 23.6\% & 12.4\% & 4.7\% & \multirow{2}{*}{-} & \multirow{2}{*}{2.51} \\  & (284.4 eV) & (285.3 eV) & (286.7 eV) & (288.5 eV) & & \\ \hline \end{tabular}
\end{table} 
Table 1: The percentage of carbon bonds in the target material and in the carbon film deposited on silver. Figure 4: Raman spectra of carbon films, deposited on sapphire substrates (**a**), and first-order fitted spectra of the samples GLC/sapphire-1 (**b**) and GLC/sapphire-2 (**c**). intra-valley process [44]. The assignment of the bands D\({}_{3}\) and D\({}_{4}\) is not obvious. The D\({}_{3}\) band is commonly assigned to the presence of amorphous phases, connected to the polyaromatic planes through carbon sp\({}^{3}\) bonds and out-plane defects [2; 31; 47], while the D\({}_{4}\) band is attributed to hydrocarbons, adsorbed on the basal planes of graphene layers [31; 49]. The experimental data [31] show that the D\({}_{3}\) band decreases to zero at a temperature above 1500 \({}^{\circ}\)C. It was also shown [31] that the D\({}_{4}\) band disappears with heat treatment above 1200 \({}^{\circ}\)C. The first-order spectrum of the GLC/sapphire-2 sample (Figure 4c) was fitted using only the following three Lorentzian components: G, D, and D\({}^{\prime}\).
## 4 Experimental Results
### Multi-Hop Scenario  
This scenario consists of a link between a sensor node N1 and a gateway node GW through another sensor device N2. In this case, N1 is also running a parallel processing task, but N2 only acts as a router between the other two. Figures 7 and 8 are analogous to Figures 5 and 6 respectively, but with the results of the multi-hop scenario.  
In Figure 7, we can observe how, as happened in the single-hop scenario, the communication performance decreases with the PTDC when PS3 is implemented, while it remains stable with PS1 and PS2. However, it should be noted that in this case, this effect does not have a direct relationship with the processing task type, i.e., its execution time. While in the single-hop scenario, the lowest value of the throughput corresponded to TT3, in this case it occurs with TT2, practically reaching a communication loss when the processing load is high (50%). As it will be detailed in the discussion, this reveals that what is critical is not the \(T_{ON}\) time itself (that only depends on the task type), but the relationship between the processing tasks times and the TDMA scheme of the MAC layer. Finally, in the lower graph of the figure, the observed behavior of the wait time is analogous to that of the single-hop scenario.  
In Figure 8, as was the case in the single-hop scenario, we can see that for PS1 and PS2, the wait time of the processing task increases with the RDC and that the type of task causes great differences in the results. In addition, regarding the two upper graphs, we observe that the RDC value has a greater impact on the communication performance than it had on the single-hop scenario.  
Figure 6: Single-hop scenario with a fixed Processing Task Duty Cycle of 5%.  
Figure 8: Multi-hop scenario with a fixed Processing Task Duty Cycle of 5%.  
Figure 7: Multi-hop scenario with a fixed RDC of 10%.
## 4 Experimental Results and Discussion
### Parameters of the Simulated Annealing Algorithm  
Due to the stochastic nature of the SA algorithm, 30 independent runs are performed on the three test scenarios to obtain experimental results. The values of the algorithm control parameters are shown in Table 4.  
The convergence test applying the tuned values of the control parameters for problem one is shown in Figure 9. Once the control parameters are tuned with the best results, the experiments for the three previously described instances are carried out.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(T_{0}\) & 0.9 \\ \(T_{f}\) & 0.0001 \\ \(\beta\) & 0.98 \\ _Lmarkov_ & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tuned values of the algorithm control parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Penalty** & **Layer** & **Penalty** \\ \hline Aqueduct & 5 & Ducts & 5 \\ Road track & 6 & Urban areas & 8 \\ Streets & 5 & Buildings & 8 \\ Bridges & 10 & Green areas & 2 \\ Water channel & 8 & Communication Inst. & 7 \\ Sub. its T. electric & 9 & Water bodies & 8 \\ Roads & 5 & Port Inst. & 9 \\ Railways & 9 & Ins. industrial & 10 \\ Roads & 7 & Communication lines & 9 \\ Airport & 10 & Water ponds & 8 \\ Cemeteries & 8 & Locations & 6 \\ Growing areas & 1 & Sports Inst. & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Penalization of the topographic layers of problems 1 and 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Penalty** & **Layer** & **Penalty** \\ \hline Aqueduct & 7 & Villages & 9 \\ Railroad & 9 & Communication paths & 10 \\ Streets & 8 & Sports Inst. & 7 \\ Sub. its T. electric & 8 & Aviation runway & 9 \\ Cemetery & 9 & Miscellaneous Inst. & 5 \\ Green areas & 1 & Locations & 8 \\ Ducts & 1 & Industrial Inst. & 10 \\ Water bodies & 8 & Bridges & 7 \\ Buildings & 9 & Growing areas & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Penalization of the topographic layers of problem 3.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 2 Method
### Frequency Bands Overview  
National and international regulations reserve several frequency bands for air-traffic surveillance radars (see Reference [15] for a complete list). Among them are the bands from 1250-1260 MHz and from 1340-1350 MHz. In Germany, several frequency bands in the lower MHz range (46-48 MHz) up to nearly 10 GHz (9300-9600 MHz) are reserved for meteorological purposes (international allocations also in Reference [15]). The World Meteorological Organization describes the use of weather radars in the frequency range from 3-10 GHz in detail in Reference [16].
# Deep Eutectic Solvent Pretreatment and Green Separation of Lignocellulose
## 5 Effect of DES Treatment on the Chemical Structure of Lignocellulosic Separation Products

Table 3: The effect of DES pretreatment on the internal chemical structure of lignocellulosic was studied by comparing the characteristic absorption peaks of chemical bonds with FT-IR spectroscopy. damage to cellulose, indicating that adding FeCl\({}_{3}\) can improve the intensity of biomass pretreatment [151]. [PERSON] et al. added hydrogen peroxide to industrial xylose residue after ChCl/Isopropanolamine pretreatment. SEM images showed that as the hydrogen peroxide content increased, the pores in the pretreated substrate also increased. This indicated that hydrogen peroxide had destroyed the morphology of the residue while removing lignin, which helped improve the yield of fermentable glucose [16]. [PERSON] et al. explained the effect of microwave-assisted treatment on the microstructure of lignocellulosic through SEM analysis. The results showed that microwave-treated biomass particles had larger porous structures on their surfaces, which exposed more cellulose structures. The pore structure and cellulose exposure contribute to the hydrolysis efficiency of cellulase [152]. Chemical treatment usually achieves the effect of separating components through targeted chemical bonding. However, ultrasonic and microwave treatments tend to destroy the biomass structure with physical impact. Both of them can increase the porosity of lignocellulosic and improve the efficiency of enzymatic hydrolysis. Overall, FT-IR spectral analysis can be utilized to compare the presence or absence of characteristic peaks associated with specific functional groups before and after DES pretreatment. This alteration is employed to ascertain the chemical bonds that have been broken and new bonds that have been formed during the process. The side chain and aromatic regions of the 2D-HSQC spectra were analyzed. Trends in the increase and decrease in chemical bonds and units in polymers during DES treatment were determined by evaluating the sizes of the corresponding functional group graphs. SEM analysis can effectively demonstrate the effect of DES pretreatment on the morphology of lignocellulosic biomass. It can be observed that most DESs can cause the surface of the biomass to form porous and non-uniform structure by destroying the bonding bonds in the biomass. Figure 4: (**a**) Side-chain regions in 2D-HSQC spectra of the lignin, (**b**) aromatic regions in 2D-HSQC spectra of the lignin, (**c**) the identified main structures in the lignin (Reprinted/adapted with permission from Ref. [144]. 2023, [PERSON]) [144]. Moreover, the addition of auxiliary solvents in the DES pretreatment process can also enhance the damage ability of the biomass surface, which can also be confirmed in the improvement effect of lignocellulosic separation ability corresponding to this method in the previous section.