esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Implementation  
The proposed optimization approach based on the hybridization of the CBGA and the SOCP, used to solve the problem of the optimal placement and sizing of D-STATCOMs in electrical distribution grids, was performed using MATLAB software version 2020\(b\) in a PC with an AMD Ryzen 7 3700 2.3-GHz processor and 16.0 GB RAM, running on a 64-bit version of Microsoft Windows 10 Single Language.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Hour (h)** & **Ind. (p.u)** & **Res. (p.u)** & **Com. (p.u)** & **Hour (h)** & **Ind. (p.u)** & **Res. (p.u)** & **Com. (p.u)** \\ \hline
1 & 0.56 & 0.69 & 0.20 & 13 & 0.95 & 0.99 & 0.89 \\
2 & 0.54 & 0.65 & 0.19 & 14 & 0.96 & 0.99 & 0.92 \\
3 & 0.52 & 0.62 & 0.18 & 15 & 0.90 & 1.00 & 0.94 \\
4 & 0.50 & 0.56 & 0.18 & 16 & 0.83 & 0.96 & 0.96 \\
5 & 0.55 & 0.58 & 0.20 & 17 & 0.78 & 0.96 & 1.00 \\
6 & 0.58 & 0.61 & 0.22 & 18 & 0.72 & 0.94 & 0.88 \\
7 & 0.68 & 0.64 & 0.25 & 19 & 0.71 & 0.93 & 0.76 \\
8 & 0.80 & 0.76 & 0.40 & 20 & 0.70 & 0.92 & 0.73 \\
9 & 0.90 & 0.90 & 0.65 & 21 & 0.69 & 0.91 & 0.65 \\
10 & 0.98 & 0.95 & 0.86 & 22 & 0.67 & 0.88 & 0.50 \\
11 & 1.00 & 0.98 & 0.90 & 23 & 0.65 & 0.84 & 0.28 \\
12 & 0.94 & 1.00 & 0.92 & 24 & 0.60 & 0.72 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Types of load profile.  
Figure 4: Type of load: residential, commercial, and industrial curves.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Par.** & **Value** & **Unit** & **Par.** & **Value** & **Unit** \\ \hline \(C_{\text{kWh}}\) & 0.1390 & US\$kWh & \(T\) & 365 & Days \\ \(\Delta_{h}\) & 1.00 & h & \(\alpha\) & 0.30 & US\$/MVAr\$ \\ \(\beta\) & \(-305.10\) & US\$/MVAr\$ & \(\gamma\) & 127380 & US\$/MVAr \\ \(c_{1}\) & 6/2190 & 1/days & \(c_{2}\) & 10 & Years \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters associated with the objective function calculation.
## 6 Prestall Disturbances
### Prestall Propagating Disturbances  
The propagation of the identified disturbances was analysed by applying coherence and phase spectra on the data of the pressure transducers over the rotor blade tips. Corresponding analysis methods are described precisely by [PERSON] and [PERSON] (2012) [PERSON] and [PERSON] (2012). Results are shown for an operating  
Figure 7: Analysis of the Power Spectral Density (PSD); Development of prestall instability at various operating points for different speeds (BPF: blade passing frequency) (Experiment).  
point with intense prestall activity but without stall. At a constant speed of 5500 rpm, the pressure rise coefficient and the flow coefficient have been set to \(\Psi=0.45\) and \(\varphi=0.43\). The same operating point was chosen for CFD calculations (see Figure 8).  
Again, the mean power spectrum comprises the typical spectral signature with side-by-side peaks centered on approximately 45% of the BPF. In the same frequency range, increased values of the coherence show that disturbances propagate around the circumference of the rotor. Generally, the coherence function allows to verify if the signals of two pressure sensors are correlated. The phase angle provides additional information about the relationship between the relative phase of frequency components of two correlated signals. The linear phase response (re-drawn with a red solid line at 360\({}^{\circ}\)) covers nearly 85% of the BPF corresponding to 1090 Hz. Using the circumferential distance \(b\) between the sensors, it is now possible to calculate the propagation velocity. The latter is  
\[1090\ \mathrm{Hz}\cdot b=1090\ \mathrm{Hz}\cdot 0.0162\ \mathrm{m}=17.66\ \mathrm{m/s} \tag{1}\]  
and thus 49% of the blade tip speed. Using the spectral characteristics as an unmistakable identification feature, the well-known discrete propagating prestall disturbances can now clearly be attributed to RL.
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 2 Material and Methods
### Internet of Things  
The IoT is a network composed of interconnected nodes/devices that send, receive, and store data. Those nodes can use edge, fog, and cloud computing (see Figure 1). The communication protocols define the communication rules between nodes, and the network topology defines the nodes' distribution and communication links [13].  
Depending on the purpose, environmental, hardware or communication constraints, IoT networks can use different types of computation, communication protocols, and network topology. Computation types mainly influence computational power, storage capacity, and latency. Communication protocols mainly influence the security and payload size of communications. The network topology mainly influences the reliability and scalability of the network [13].
# Analytical Pyrolysis and Mass Spectrometry to Characterise Lignin in Archaeological Wood
## 3 Results and Discussion
### Double-Shot Py(HMDS)-GC/MS  
The high chemical complexity of archaeological wood, as emerged in the previous sections, can be further enhanced when wood is treated with consolidating materials, a situation often encountered when waterlogged wood artefacts are displayed in museums. It is common knowledge that consolidating materials, such as polyethylene glycol (PEG), do not terminate the natural degradation of wood and can sometimes accelerate it [106; 107; 108]. It is therefore particularly useful to examine the preservation state of archaeological wood in the presence of consolidating agents.  
Single-shot Py(HMDS)-GC/MS is not the most suitable approach in these cases, as the pyrograms are very complex and wood pyrolysis products can co-elute with those deriving from the consolidating material. In the case of the Lyon samples, an alternative approach was suggested by the results obtained in the EGA-MS analysis. In particular, for sample Ly-A1, treated with PEG 4000 and disodium sebacate, three regions of thermal degradation were observed, as shown in Figure 9. The first region (up to 350 \({}^{\circ}\)C) was found to mainly contain wood pyrolysis products, with a predominance of lignin fragment ions in the mass spectra, which highlighted an advanced state of carbohydrate depletion. The second region (between 350 and 420 \({}^{\circ}\)C) corresponded mainly to the thermal degradation of polyethylene glycol (PEG). The last region (above 420 \({}^{\circ}\)C) featured fragment ions derived from both PEG and disodium sebacate. PEG and disodium sebacate could thus be partially thermally separated in sample Ly-A1 by EGA-MS.  
On the basis of these results, double-shot Py(HMDS)-GC/MS was experimented with, and 320 \({}^{\circ}\)C (slightly below 350 \({}^{\circ}\)C) was selected as temperature for the first shot and 600 \({}^{\circ}\)C for the second shot. Figure 9 reports the corresponding pyrograms. The pyrolytic profile obtained at 320 \({}^{\circ}\)C is mainly rich in lignin pyrolysis products and only a few peaks are related to holocellulose fraction with negligible abundance. The pyrolytic profile obtained at 600 \({}^{\circ}\)C showed PEG pyrolysis products in groups of n-mers (n = 1-8). This example demonstrates that double-shot Py(HMDS)-GC/MS enabled the separation of wood and consolidation material pyrolysis products to be separated in two different chromatographic profiles, leading to less complex data to be analysed. When suitable comparisons with reference materials are performed, this approach can also achieve some preliminary information on the degradation of the consolidation materials.
## 2 Materials and Methods
### Model of Radiometric Calibration and Infrared Thermal Imager  
One of the most commonly used approaches of radiometric calibration for infrared thermal imager is that setting a blackbody source of large area to completely cover the field-of-view (FOV) of the infrared thermal imager [20; 21; 22], as shown in Figure 2.  
Since the calibration is usually carried out in the laboratory and the thermal imager is very close to the blackbody, the influence of the atmosphere can be ignored, \(\epsilon_{\lambda}=\alpha_{\lambda}=1\), \(\tau_{a\lambda}=1\) and \(\epsilon_{a\lambda}=0\). As a result, Equation (11) is simplified as:  
\[G_{s}=kf(T_{0})+G_{0} \tag{13}\]  
where \(f(T_{0})\) can be calculated according to [PERSON]'s law of radiation:  
\[f(T_{0})=\frac{1}{\pi}\int_{\lambda_{1}}^{\lambda_{2}}\frac{C_{1}\lambda^{-5} }{e^{C_{2}/\lambda T_{0}}-1}d\lambda \tag{14}\]  
In Equation (14), \(C_{1}\) and \(C_{2}\) are called the first and second radiation constants, respectively, with values \(C_{1}=2\pi hc^{2}=3.742\times 10^{-16}\) W \(\cdot\) m\({}^{2}\) and \(C_{2}=hc/k_{B}=1.4388\times 10^{-2}\) m \(\cdot\) K, where fundamental constants (\(2.997\times 10^{8}\) m/s), \(h\) (\(6.626\times 10^{-34}\) J s), and \(k_{B}\) (\(1.381\times 10^{-23}\) J/K) are the speed of light in vacuo, [PERSON]'s constant, and Boltzmann's constant, respectively.  
In the process of radiometric calibration, it is necessary to keep laboratory conditions and thermal imager parameters unchanged. Nonuniformity correction (NUC) is often applied to make the response of each pixel identical. The procedure of radiometric calibration for an infrared thermal imager is as follows.  
1. Turn off the auto gain function of the infrared thermal imager, and set focus to infinity;
2. Apply NUC to the blackbody source;
3. Save frames of image when the temperature of blackbody source is completely stable, then average the saved images to reduce random noises;
4. Collect DN value for the averaged images of different temperatures;  
Figure 2: Schematic diagram of the radiometric calibration.  
Calculate the radiance according to Equation (14);
* Change the temperature of the blackbody and repeat the steps 3-5 until all temperatures are measured;
* Fit DN value and the radiance in Equation (13) by the least square method. The infrared thermal imager and the blackbody source used for radiometric calibration are shown in Figure 3.  
The properties of the infrared thermal imager and the blackbody source are listed in Tables 1 and 2, respectively.
## 5 Conclusions  
In this paper, we proposed a novel self-intersection penalty term for statistical body shape models to remove the self-intersection of the mesh by gradient-based optimization. Unlike most traditional approaches, our method does not require a hard-to-obtain differentiable penalty term, but instead gradients are manually calculated. In the course of analysis, we have demonstrated that it is not necessary to derive differentiable expressions of a penalty term and gradients can be manually calculated from the perspective of geometry. Since no approximation is used in our method, self-intersection can be strictly removed. The highlight of our work is that our method applies to general meshes with different shapes and topology without the need to design a set of appropriate proxy geometries. Despite the fact that our proposed self-intersection penalty term is more time consuming than traditional approaches, the elapsed time of one iteration is totally acceptable according to the experimental results. The applications of our method are not limited to the statistical body shape models presented in this paper. Our proposed self-intersection penalty term can be incorporated into other 3D reconstruction problems based on a triangular mesh, such as the mesh-based 3D reconstruction described in [7; 16; 17].  
Our proposed approach has its limitations. When there are some triangles happened to be parallel to the detection rays, these triangles will not intersect with any detection rays no matter how dense the detection rays are. That is to say, vertices of triangles parallel to the detection rays may be mistakenly classified, and further the gradients with respect to these vertices will be incorrect. We assume that each triangle and its vertices are located in the same region, this assumption does not apply to the situation where the mesh is sparse and in this situation undesirable consequences may be caused. Another limitation is that it is difficult to manually set the number of detection rays appropriately, such that all vertices in self-intersection can be detected and classified correctly with minimum memory consumption.  
Future research directions of this work may include modifying the way detection rays are emitted to avoid incorrect results when there are triangles parallel to the detection rays. It may also include developing a strategy to set the number of detection rays appropriately and automatically. We are also interested in reducing the time complexity of our proposed method to make this approach more suitable for gradient-based optimization.  
Conceptualization, Z.W. and H.L.; Methodology, Z.W., H.L. and W.J.; Writing--original draft preparation, Z.W.; Writing--review and editing, Z.W., H.L. and W.J.; Visualization, Z.W. and H.L.; Supervision, W.J.; Funding acquisition, [PERSON], H.L. and L.C. This research was funded by [the National Natural Science Foundation of China] grant number [61633019], [the Public Projects of Zhejiang Province, China] grant number [LGF18F030002] and [Huawei innovation research program] grant number [HO2018085209]. The authors declare no conflict of interest.
## 4 Construction of the UA-FedRL Method
### Uncertainty-Aware Reinforcement Learning-Based Optimal Epoch Selection
#### 4.1.2 Proposed Method
The weights of the neural network \(\theta\) are then updated using backpropagation with a loss function that incorporates the log-likelihood of the action, the difference between the observed reward and the estimated action value, and the KL divergence between the approximate posterior distribution and the prior distribution. The hyperparameter update of \(\beta\) is performed using the calculation method given in (14). Figure 3: The overall workflow of UA-FedRL for adaptive epoch selection of heterogeneous industrial IoT devices. ```
1: Initialize the neural network weights \(\theta\) with Gaussian prior \(p(\theta)\)
2: Initialize the hyperparameters \(\alpha\), \(\beta\), \(\gamma\), and \(N\)
3:for each episode do
4:for each time step \(t\)do
5: Observe state \(s_{t}\)
6:for each agent \(i\)do
7: Sample \(\theta_{i}\) from the approximate posterior distribution \(q_{\theta_{i}}(\theta_{i})\)
8: Compute the action value estimates \(Q_{i,t}(s_{t},a;\theta_{i})\) for all actions \(a\)
9: Compute the upper confidence bounds \(U_{i,t}(s_{t},a)\) for all actions \(a\)
10: Select action \(a_{i,t}=\arg\max_{a}U_{i,t}(s_{t},a)\)
11:endfor
12: Perform action \(a_{t}=[a_{1,t},a_{2,t},\ldots,a_{N,t}]\)
13: Observe reward \(r_{t}\)
14: Update the neural network weights \(\theta\) using backpropagation with the loss equation: \[L= -\frac{1}{N}\sum_{i=1}^{N}\sum_{t=1}^{T_{i}}\log\pi_{\theta n}( ai,t\mid si,t)\bigg{[}\frac{1}{\sigma_{i,t}}(\sigma_{i,t}-\mu_{i,t})^{2}\] \[+\log\sigma_{i,t}\bigg{]}\] \[e_{i}\in E\text{ for }i=1,2,\dots,N,m\in M,\]0
15: Update the hyperparameter \(\beta\)
16:endfor
17:endfor
```  
**Algorithm 1** UA-FedRL with UCB-based epoch selection to maximize performance in heterogenous IoT.
## 1 Introduction
This problem can be efficiently solved by using the topology optimization method, which is widely employed in aircraft and aerospace structures [35]. A review of the application of cellular materials and topology optimization in customizing mechanical properties was presented in [36]. Reference [37] summarized the topology optimization of heat transfer systems. Under the combination of mechanical and thermoelastic loads, the topology optimization method was adopted by [PERSON] et al. to reduce thermal expansion with prescribed stress limits [38; 39], by [PERSON] et al. to reduce thermal stress [40] and by [PERSON] et al. to minimize the mean compliance and elastic strain energy [41]. For TPS configuration, [PERSON] [42] et al. presented a topology optimization methodology that couples frequency and stress analysis, and [PERSON] et al. [43] derived a bridge-like ITPS design by minimizing equivalent thermal conductivity and total strain energy of the core area. Structural responses of the optimal designs were effectively improved. However, these designs are usually too complicated to be manufactured and the risk of buckling could be increased due to the existence of thin plates. This paper focuses on the application of the topology optimization method in the ITPS and the re-design of core structures. The objective of the current work was to develop techniques to optimize the core of ITPS panels. Inspired by the topology optimization result, an innovative core configuration was extracted and modified. With the application of this core layout, thermal insulation capacity of the ITPS is expected to be improved without weakening its strength. Thermal buckling failure was also taken into account in this work. This paper is organized as follows: in Section 2, the background of thermo-mechanical analysis is described, and the topology optimization method considering both thermal and mechanical performance is established. In Section 3, an innovative ITPS model with cell configuration obtained from previous topology optimization is built and further optimized considering its thermal buckling characteristics. In Section 4, comparisons of the structural responses between the original and optimized designs are presented. Finally, conclusions and future research recommendations are given.
## 4 Losses Analysis
### Eddy Current Loss  
Figure 1 and Table 2 show the bulk conductivity of rotor material. The rotor eddy current can be induced in the PM, sleeve, and rotor core, which is caused by space and time harmonics. Many methods were developed to reduce eddy current loss, including slotting sleeve, adding shield, and adopting a hybrid sleeve. An FEM is used to calculate rotor eddy current distribution, as shown in Figure 9. When the titanium alloy sleeve was employed, M1 had the highest eddy current loss, and the maximal eddy current loss occurred on  
Figure 8: \(k_{sp}\) versus ratio of wire thickness to skin depth at different frequencies.  
the sleeve surface due to its high electrical conductivity. This is greatly challenging for developing cooling systems. M3 had the lowest eddy current loss on the PMs. Moreover, the eddy current loss of M2 and M4 was prominent in PMs. Maximal eddy current loss occurred at the end of PM, which increased the demagnetization risk to the PM.
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
###### Abstract
Due to the slow motion of the target, the impact of target motion Doppler on echo compression gain was very small. In reference [21], a mixed mode of coherent integration and noncoherent integration is proposed, and noncoherent integration is used to solve the influence of the Doppler frequency shift of moving targets on signal coherence. Since noncoherent integration produces a square loss [22], the gain improvement is very limited. In references [23; 24; 25], Radon Fourier transform and fractional Fourier transform are used to estimate the Doppler frequency shift of the moving target echo, and the estimated motion parameters are used to compensate the echo phase to achieve long-term coherent accumulation. However, it is mainly applied to the phase compensation of the chirp signal of the traditional radar without considering the amplitude loss when the GNSS signal is demodulated and compressed. In our previous works, a novel demodulation and compression method for GNSS echo was proposed to solve the problem of weak signal detection, which is shown in reference [26]. In this paper, a dual-frequency GNSS-S signal echo processing method is proposed to solve the problems of energy loss and phase fluctuation caused by the residual Doppler frequency after demodulation and compression of the GNSS-S echo signal of a moving target. First, the phase relationship model of the GNSS dual-frequency echo signal is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The following parts of this paper are structured as follows: Section 2 introduces the GNSS-S dual-frequency differential processing method; Section 3 presents the simulation and the experiment results; Section 4 discusses the results; and finally, a conclusion is given in Section 5.
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 5 Analysis of the Channel Access Prioritization of Packets
### Conditions for TSN-Compliant Scheduling
#### 5.3.3 Introduction of Blocking Priority  
Assuming that updating is applied to the LCP at every TTI, the functionality of the TAS transmission gates for every traffic class needs to be adopted by the scheduling mechanism. In order to enable the migration of the GCL controlled traffic shaping into native NG-RAN functions, a novel kind of logical channel priority has to be introduced. A logical channel of this blocking priority is not served at all for the respective TTI. In combination with a time variant LCP, this enables the realization of the gating mechanisms requested by the GCL.
## 2 A2S Integrated MIoT
### A2S Integrated Technology
ABSs on military aircraft provide military communications capabilities, support command and control, and share battlefield information. This is essential for coordination and decision-making in military operations. * Emergency rescue and disaster relief: ABSs provide critical communications support during emergency rescue and relief operations. They allow rescuers to maintain contact with ground command centers and share rescue information. This helps to improve rescue efficiency and the safety of rescue operations [83]. * UAV applications: ABSs are also widely used in UAV applications. The BS equipped with the UAV can realize the communication and control between the UAV and the ground control station, as well as the collaborative operation among UAVs. This allows UAVs to play an important role in surveillance, logistics, agriculture, security, and other fields [84]. * Satellite communications: ABSs are used in conjunction with SC systems to provide long-range communication connectivity in aviation services. Through SC, ABS can realize broadcasting, DT, and remote control on a global scale to meet the communication needs of aircraft and passengers [85]. 3. The role and advantages of ABSs in the MIoT * ABSs can be used as wireless communication BSs to provide stable networking connectivity for MIoT devices. By incorporating radio equipment and antennas, they enable efficient communication with IoT devices far from shore [86]. * Air-based warfare can provide greater coverage. Their height and flexibility allow them to provide the signal coverage over a wider range than land BSs or SCs. As a result, more MIoT devices can be connected, enabling a wider range of data transmission and communication. * Air-based warfare also offers the advantages of rapid deployment and flexibility. They can move from one place to another at a relatively fast pace, providing communication support in different areas at sea depending on demand [87]. This flexibility can play an important role in situations of emergency, temporary assignment, or specific needs [88]. In summary, the role of ABSs in the MIoT is to provide stable communication connectivity, extended coverage, rapid deployment, and flexibility. These advantages make them an important part of enabling efficient MIoT communications.