esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 4 Numerical Validation
### Modeling of the Academic Electromagnetic Device
#### 4.2.3 Evolution of the Impedance Versus the Frequency  
The evolution of the impedance modulus and its phase, according to the frequency obtained from EQS, MQS, and Darwin models, are presented in Figure 15. At low frequencies, the impedance Z corresponds to the DC resistance of the winding. Then, for all models, we obtain \(Z=R_{\text{DC}}=3\) m\(\Omega\) when the frequency tends towards zero. When the frequency increases, the skin effect appears in the winding; then, the value of the resistance of the winding increases. This effect is not taken into account with the EQS model. For \(f\in[0:10^{3}]\) Hz, the MQS and Darwin models give a similar evolution of the impedance. Indeed, the capacitive effect is negligible compared with the inductive and resistive effects. For \(f>1\) kHz, the influence of the the capacitive effects appears on the evolution of the impedance for both the EQS and Darwin models. A similar behavior can be observed for both models in Figure 15.  
Figure 14: Distribution of the magnetic flux density **B** (T) in the magnetic core at \(f=1\) Hz.
# A Feasibility Study of a Novel Piezo MEMS Tweezer for Soft Materials Characterization
## 3 Modeling Piezoelectric Actions on the Microbeams
### Static Model  
The value of the stiffness \(K_{ST}\) and of the equivalent Young's modulus \(E_{ST}\) can be established by a static test with a constant voltage \(V_{0}\) applied to the piezoelectric plates: \(V(t)=V_{0}\). In this case, the bending of the beams will cause the compression of the soft tissue and consequently a reaction force \(F_{\mathrm{ST}}\) will be applied from the soft tissue to the beams, through the clamp teeth (see Figure 5).  
Considering that the Euler Bernoulli model is appropriate for the present case because of the high length-to-thickness aspect ratio of the beams, the tip deflection:  
\[w_{L}=\frac{M_{a}L^{2}}{2E_{b}l_{b}}-\frac{F_{\mathrm{ST}}L^{3}}{3E_{b}l_{b}}\, \tag{3}\]  
is due to a combination of the loads \(F_{\mathrm{ST}}\) and \(M_{a}\). Therefore, the first load  
Figure 4: PiezoMEMS grip of the soft tissue.  
Figure 5: Scheme for the calculation of the tip displacement.  
\[F_{ST}=\frac{3E_{b}I_{b}}{L^{3}}\Big{(}\frac{M_{a}L^{2}}{2E_{b}I_{b}}-w_{L}\Big{)} \tag{4}\]  
can be obtained by measuring \(w_{L}\).  
Due to the high value of the axial stiffness of the clamp teeth, the deflection \(w_{L}\) can be considered equal to the axial displacement of the soft tissue (see Figure 5) so that the axial strain  
\[\epsilon_{ST}=\frac{2w_{L}}{L_{ST}} \tag{5}\]  
is calculated.  
Under the assumption of linear elastic behavior for the soft tissue (ST), the relationship between the normal stress \(\sigma_{ST}\) applied to the cross section of the soft tissue and \(\epsilon_{ST}\) can be written as:  
\[\sigma_{ST}=\frac{F_{ST}}{S_{ST}}=E_{ST}\frac{2w_{L}}{L_{ST}} \tag{6}\]  
from which the equivalent Young's modulus  
\[E_{ST}=\frac{F_{ST}L_{ST}}{2S_{ST}w_{L}} \tag{7}\]  
is evaluated. Moreover, the stiffness  
\[K_{ST}=\frac{2E_{ST}S_{ST}}{L_{ST}} \tag{8}\]  
is calculated given the dimensions of the ST sample.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 2 Ship Wake Bubbles Laser Detection Characteristics and Analysis
### Bubble Distribution Characteristics and Analysis of Ship Wake  
The characteristics of ship wake bubbles are related to many factors, such as propeller type, propeller speed, draft depth, heading (straight line, turning maneuver, etc.), meteorological, and hydrological conditions, which are the basis of the engineering application of laser wake detection technology. The ship wake is divided into a short-range wake and long-range wake. The short-range wake is generally the distance from the ship to three times the length of the ship [10]. There are obvious differences in bubble characteristics between the short-range ship wake and the long-range ship wake. The bubbles in the wake field of the short-range ship are greatly affected by the cavitation force formed by the propeller and the cavitation force after the ship is driven. The bubble scale is large, generally below 1000 \(\upmu\)m, mainly concentrated in 100-500 \(\upmu\)m, and the bubble number density can reach 10\({}^{6}\)-3 \(\times\) 10\({}^{8}\) m\({}^{-3}\). The larger the bubble scale, the faster the bubble floats, and the larger the bubble scale, the lower the bubble number density [11; 12; 13; 14]. Bubbles in the wake field of long-range ships are mainly affected by their buoyancy. Large-scale bubbles rise to sea level and die out. Small-scale bubbles stay in seawater for a long time due to their slow rise. The scale is generally 10-300 \(\upmu\)m, of which 40-80 \(\upmu\)m is the majority [15]. According to the actual measurement, the bubble size in the seawater background is generally 20-200 \(\upmu\)m, the bubble number density is generally 10\({}^{2}\)-10\({}^{5}\) m\({}^{-3}\), and the bubble number density of the remote ship wake is generally 10\({}^{5}\)-6 \(\times\) 10\({}^{6}\) m\({}^{-3}\). With the increase in time, the number density of the wake of the long-range ship gradually decreases to the number density of bubbles in the seawater background. The number density of 10-20 \(\upmu\)m bubbles is the largest, and the thickness of the bubble layer is generally 2-4 m.  
From the long-range wake to the short-range wake, the distribution characteristics of the ship wake bubbles and the characteristics of the bubble target are as follows:  
* the wake width decreases gradually;
* the concentration scale of the bubble group gradually increases;
* the bubble number density gradually increases;
* the thickness of the bubble layer gradually increases and begins to decrease after reaching the maximum thickness.  
In summary, the bubble scale of the ship wake can be obtained from \(\upmu\)m level to \(\upmu\)m level; the bubble thickness varies from 10 cm to more than ten meters. The bubble number density varies from 10\({}^{2}\) m\({}^{-3}\) to 10\({}^{9}\) m\({}^{-3}\), the magnitude span is large, the distribution is wide and uneven, and the target characteristics are weak.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 8 Challenges and Future Directions
### Promoting the Integration of Sensing and Communications  
The proposal of sensing and communication integration provides a new development opportunity for CML-based rainfall measurement. On the one hand, monitoring rainfall by using CMLs is a typical application of communication-assisted sensing, especially driven by the deep integration of information and communication technology (ICT), AI, and BD technologies. The absorption spectrum characteristics of water molecules based on 6G terahertz and the \"fingerprint spectrum\" characteristics of chemical information will achieve more accurate and wide-area real-time monitoring of rainfall, atmospheric humidity, and air quality in the near future. On the other hand, CML-based rainfall sensing can also assist the promotion and development of communication services. For example, sensing technologies such as beamforming can be used to assist the transmitting terminal of each node in the communication system to realize the parameter set selection or parameter configuration for transmission signals according to real-time environmental conditions, and thus to save energy, improve spectrum utilization, and so on. Therefore, in the future, the abilities of sensing and communications should be optimized jointly to realize the overall performance improvement
# A Spectrum Management Platform Architecture to Enable a Sharing Economy in 6G
## 2 Literature Review
### Our Motivations and Novel Contributions  
The efficient use of the spectrum will be a crucial problem in 6G and will face further challenges in comparison with current wireless technologies, mainly due to the targeted multi-Gigabit data rates and a broader set of potential spectrum bands and spectrum management strategies. Therefore, 6G will need novel and intelligent spectrum allocation and sharing models [23]. The introduction of virtualisation, softwarisation, and programmability technologies, including Artificial Intelligence (AI), will undoubtedly help address these challenges. This trend has already started in 5G, for example with the introduction of SDN and NFV in the core as we have previously mentioned, which made concepts such as Mobile Edge Computing (MEC) and network slicing possible. In this context, we believe that the spectrum-sharing mechanisms and platforms for 5G illustrated in Sections 2.1 and 2.2 present key limitations that make them unsuitable for upcoming 6G networks. These limitations can be summarised as follows:* Current spectrum-sharing mechanisms and platforms only acknowledge operators as service providers, thus eliminating other entities that own or manage a wireless network and that could offer wireless spectrum access;
* They lack flexibility as reflected in the architecture of current wireless systems and their radio access and allocation protocols, which are too elementary and do not consider the characteristics of the wireless devices and their data traffic requirements. For instance, an IoT device will always try to connect to its operator's base station although there may be closer Radio Access Nodes (RANs) which could save them energy.  
Therefore, in this paper, our aim is to address these limitations by proposing a novel spectrum-sharing model, applicable to 6G networks, which is completely based on the dynamic matching of offer and demand of network resources. It is based on the principles of the sharing economy paradigm. In this model, any entity that owns or manages a wireless device (mobile phones, Wi-Fi Access Points, etc.) can offer wireless spectrum access and hence could contribute to connecting wireless devices.
## 2 Materials and Methods
### Sensitivity Analysis  
Sensitivity analysis is a method to emphasize the contribution of independent input variables in the ANN model. We used Statistica v. 13 environments for the sensitivity analysis to provide information about the relative importance of the inputs. Here, the average values replaced the values of each input variable determined using the training dataset. A proportion of two errors is calculated in this method: the error with a certain input replaced by its mean and the error with the original input values. The error ratio can be interpreted as the importance of the input parameter and is used to estimate the degree of influence expressed in percentages the input parameters of an ANN model have on its output. [PERSON] et al. [58] used a similar sensitivity analysis in the prediction and analysis of structural damage after an earthquake.
## 4 Conclusions  
The primary tar formation mechanism via catalytic pyrolysis on AD was analyzed. Some conclusions are summarized as follows:  
Figure 11: Scheme 3 for the formation of phenols from lignin catalyzed by AD.  
Figure 12: Scheme 4 for the formation of phenols from cellulose and hemicellulose catalyzed by AD.  
1. AD accelerates the cleavage of C-O bonds to form a great amount of free radicals, which are confined in the aggregated structure of AD and biomass during pyrolysis.
2. Catalytic pyrolysis on AD to produce char, tar, and gas is initiated by the formation of active C-O-M intermediates. The broken C-O bonds linked to etherified guaiacyl and C1 of celluloses are accelerated; the glycosidic bonds linking with oxygen functional groups are activated to cleave at 700 \({}^{\circ}\)C. The alcohol hydroxyl and carbonyl groups remain in the residue to form aggregated structures in char.
3. AD enhances the selective formation of primary tar components such as ketones, furans, and phenols. It accelerates the cleavage of C-O bonds linked with C1 of celluloses and hemicellulose to form ketones and furans. Moreover, AD promotes the formation of phenols via the rupture of the etherified guaiacyl bond and anhydro-oligosaccharide at high temperatures.  
These results may facilitate the process optimization and reactor design of commercial-scale biomass gasification and provide a new direction for the reuse of organic and industrial wastes. The goal of simultaneous energy regeneration from biomass and comprehensive utilization of industrial waste may be achieved.  
Conceptualization, P.L. and Z.Z.; formal analysis, L.L.; investigation, L.L. and P.L.; resources, P.L.; data curation, L.L. and Q.W.; writing--original draft preparation, P.L.; writing--review and editing, Z.Z.; visualization, H.Y.; supervision, H.T. and T.Z.; funding acquisition, H.Y. and H.T. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Natural Science Foundation of China (51906021 and 51703014), National Key R&D Program of China (2018 YFC1901203), Natural Science Foundation of Colleges in Jiangsu Province (19 KJB480005), Changzhou applied basic research plan (CJ20190081), Research Foundation for Advanced Talents of and Changzhou University (ZMF17020034), and Mid Long-Term Development Strategy research on Engineering Technology of China (2019 ZCQ04).
# Stochastic Weight Averaging Revisited
## 3 Main Results
### Does SWA Always Identify Wider Optima Than SGD?
Even if the backbone SGD had converged to a local minimum, resulting in an update vector close to zero, the update vector of the previous iteration could be large, which could then introduce unwanted oscillations in the resulting weight samples. This could explain the phenomenon that a converged SGD enhanced by momentum only found weight samples located at the boundary of a flat valley, as reported by [5]. However, when we removed the momentum module, SWA could lose its advantages. As for our experimental results on the graph datasets, they were consistent with those reported in [30], which showed that the training loss minimizer and the test loss minimizer were not correlated in graph-based datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cora**} & \multicolumn{2}{c}{**Citeseer**} & \multicolumn{2}{c}{**Pubmed**} \\  & **[PERSON]** & **SWA** & **[PERSON]** & **SWA** & **[PERSON]** & **SWA** \\ \hline GCN & 81.2\({}_{\pm 0.47}\) & **81.3\({}_{\pm 0.21}\)** & 70.5\({}_{\pm 0.45}\) & **70.8\({}_{\pm 0.41}\)** & 79.5\({}_{\pm 0.29}\) & **79.6\({}_{\pm 0.26}\)** \\ GraphSAGE & **80.2\({}_{\pm 0.22}\)** & 79.3\({}_{\pm 0.49}\) & **69.8\({}_{\pm 0.54}\)** & 69.7\({}_{\pm 0.16}\) & **77.8\({}_{\pm 0.17}\)** & 77.7\({}_{\pm 0.29}\) \\ GAT & 81.6\({}_{\pm 0.43}\) & **81.7\({}_{\pm 0.52}\)** & **70.6\({}_{\pm 0.26}\)** & 70.4\({}_{\pm 0.25}\) & 76.1\({}_{\pm 0.43}\) & **76.2\({}_{\pm 0.28}\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test accuracy (%) comparison between Adam and SWA for the graph-node-classification task on datasets Cora, Citeseer, and Pubmed. Best results are **bolded**. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**NCII**} & \multicolumn{2}{c}{**D\&D**} & \multicolumn{2}{c}{**PROTEINS**} \\  & **[PERSON]** & **SWA** & **[PERSON]** & **SWA** & **[PERSON]** & **SWA** \\ \hline MinCutPool & 74.78\({}_{\pm 0.42}\) & **75.25\({}_{\pm 0.14}\)** & 79.67\({}_{\pm 0.69}\) & **80.86\({}_{\pm 0.85}\)** & 76.44\({}_{\pm 1.50}\) & **77.34\({}_{\pm 1.83}\)** \\ SAGPool & 71.63\({}_{\pm 0.88}\) & **72.57\({}_{\pm 0.49}\)** & **71.89\({}_{\pm 0.33}\)** & 70.87\({}_{\pm 0.45}\) & 77.73\({}_{\pm 1.16}\) & **78.37\({}_{\pm 1.10}\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test accuracy (%) comparison between Adam and SWA for the graph-classification task on datasets NCII, D&D, and PROTEINS. Best results are **bolded**.
## 2 Materials and Methods
### A Stochastic Algorithm  
We propose an iterative _stochastic_ agglomerative procedure to search for the local community of seed \(v_{0}\). As mentioned before, \(v_{0}\) could be a single vertex or a group of vertices. The local community is still denoted by \(\mathcal{C}\), whose element vertices are given by the index set \(S_{\mathcal{C}}\). The index sets for \(\mathcal{U}\) and \(\mathcal{B  
```
add initial seed \(v_{0}\) to \(\mathcal{C}\) add all neighbors of \(v_{0}\) to \(\mathcal{U}\) set \(\mathcal{B}\) to be the boundary of \(\mathcal{C}\) compute \(R(\mathcal{C})\), the local modularity of \(\mathcal{C}\) while stopping rules are not satisfied do  choose an adaptive value for \(\epsilon\)  Set \(S_{\text{Add}}\) and \(S_{\text{Remove}}\) to be empty sets for each \(v_{j}\in\mathcal{U}\)do  compute \(\Delta R_{j}^{A}=R_{\mathcal{C}+v_{j}}-R_{\mathcal{C}}\)  insert \(\Delta R_{j}^{A}\) into \(S_{\text{Add}}\) endfor for each \(v_{k}\in\mathcal{C}-v_{0}\)do if\(\mathcal{C}-v_{k}\) is connected then  compute \(\Delta R_{k}=R_{\mathcal{C}-v_{k}}-R_{\mathcal{C}}\)  insert \(\Delta R_{k}^{R}\) into \(S_{\text{Remove}}\) endif endfor merge \(S_{\text{Add}}\) and \(S_{\text{Remove}}\) to get a new set \(S_{\text{All}}=S_{\text{Add}}\cup S_{\text{Remove}}\) select one \(v_{m}\) from \(S_{\text{All}}\) with probability \(\frac{\max\{0,\Delta R_{m}+\epsilon\}}{\sum_{k}\max\{0,\Delta R_{k}+\epsilon\}}\) if\(v_{m}\in\mathcal{U}\)then  add \(v_{m}\) to \(\mathcal{C}\) else  remove \(v_{m}\) from \(\mathcal{C}\) endif  update \(\mathcal{U}\), \(\mathcal{B}\), and \(R(\mathcal{C})\) if stopping rules are satisfied then  break out of the while loop endif  endwhile
```  
**Algorithm 1** The algorithm for the stochastic maximization of local modularity.
## 3 Routing
### Link Quality Estimation  
Since CENet uses acknowledge and retransmission schemes, the decline of link quality increases the number of retransmissions and packet drops. The link quality is estimated by observing the number of packet retransmissions. CENet uses the window mean with EWMA (exponentially weighted moving average) as link quality estimator [11] which computes an average retransmission rate over a time period and smoothes the average by EWMA.  
Let \(i\) be the index of a time window, and \(t\) be the time window represented in the number of message transmissions and \(C_{i}\) be the number of packet retransmissions in the \(i^{th}\) time window. \(H_{i}\) is a value between \(C_{i}\) and history retransmissions, \(H_{i}\) can be expressed as:  
\[H_{i}= \left\{\begin{aligned} 0,& i=0\\ C_{i},& i>0\;and\;H_{i-1}=0\\ H_{i-1}*a+\left(1-a\right)*C_{i},& i>0\;and\;H_{i-1}>0 \end{aligned}\right. \tag{1}\] \[T_{i}= \left\lceil\;H_{i}*\left(1+b\right)\right\rceil \tag{2}\]  
where \(0<a<1\) and \(0<b<1\) both are tuning parameters, \(T_{i}\) is a threshold. In the time window \(t\), if \(H_{i}\) is greater than \(T_{i-1}\), the route maintenance is triggered and \(H_{i}\) will be reset to \(0\) in next round estimation. To understand (1) and (2), we give an example, let \(C_{i}\) be a random value between \(7\) and \(10\), \(a=0.5\) and \(b=0.1\). Table 2 shows the retransmission result of five time windows. In the 3 rd and 5 th windows, the number of retransmissions is greater than the threshold and routing maintenance is triggered.  
From Table 2, we can see a problem in the link estimator. When \(T_{i-1}=0\), \(C_{i}\) has a useless reference value and the estimator will never be able to detect the decrease of link quality in the current time window. To solve the problem, we set two criteria for routing maintenance. One is the estimator mentioned above. The other is data packet drop rate. For example, when the packet drop rate is greater than \(10\%\) (according to the reliability level of the user requirement) in a time window, the routing maintenance process will be triggered.
## 5 Performance Evaluation
### Handover Authentication
#### 5.3.3 Communication Cost  
The communication cost of the proposed protocol when compared with other existing protocols is as follows:  
* 3-Tier AKA: In lines 3 and 4 of Figure 9, there are a total of two AES encrypted messages transmitted.
* FogHA: The first fog access point initiates handover authentication communication by sending pre-negotiation information to the second fog access point in line 2 of Figure 16. Once this exchange is completed, the first fog access point proceeds to transmit the pre-negotiation temporary key to the mobile device after line 5 of Figure 16. Subsequently, in line 11 and line 15 of Figure 16, authentication messages are transmitted between the second fog access point and the mobile device. The total communication costs consist of five IDs, seven hash functions, one random number, and four timestamps.
* Quantum-resistant handover authentication protocol: The messages exchanged between entities primarily consist of hash function operations and NTRU encrypted messages. In lines 2, 4, 7, and 11 of Figure 17, there are total communication costs of five NTRU encrypted messages, nine hash function operations, and two timestamps.
* In [PERSON] et al.'s scheme, the authentication message is initially sent from the user to a low-Earth-orbit satellite (LEOS). Subsequently, the LEOS appends its ID to the message and forwards it to the network control center (NCC). In lines 8 and 9 of Figure 18, the response message is sent back to the user. Lines 12 and 13 of Figure 18 involve the session key agreement process. The protocol includes two symmetric encryption operations, four elliptic curve point multiplication operations, four timestamps, three IDs, and four hash function operations transmitted in total.
* 3-Tier AKA only performs two AES encryption operations. Hence, the overall communication cost of 3-Tier AKA amounts to 256 bits, significantly lower than that of other protocols. Table 18 and Figure 20 show the total communication cost of our protocol and other related schemes during the handover authentication phase.  
Figure 20: Communication cost comparison (handover) of 3-Tier AKA, FogHA [44], [PERSON] et al. [62], and [PERSON] et al. [55]
## 5 Albedo Inversion and Analysis
### Comparison of AFX with Vegetation Index  
The MODIS vegetation index product was chosen to discuss the relationship between the AFX and vegetation index (see Figure 20). The left picture in Figure 20 is a comparison between the normalized difference vegetation index (NDVI) and AFX, and the right picture is a comparison between the enhanced vegetation index (EVI) and AFX. Principal component analysis (PCA) was used to analyze the two sets of data, and the first two principal components are shown as straight lines in Figure 20. In the figure, the first principal component is represented by a nearly horizontal red line, and the second principal component is represented by a nearly vertical black line. The first principal component of NDVI and AFX accounts for 71.8362%, which is almost parallel to the NDVI axis, and the second principal component accounts for 28.1638%, which is almost parallel to the AFX axis. The first principal component of the EVI and AFX accounts for 80.4505%, which is almost parallel to the EVI axis, and the second principal component accounts for 19.5495%, which is almost parallel to the AFX axis. The analysis shows that NDVI explains more overall changes, while AFX reveals significant changes. AFX and NDVI/EVI are approximately orthogonal. For FY-2G visible light channel data, AFX contains other information related to vegetation structure. The results of this study show that the land surface albedo retrieved by the FY-2G visible channel can reveal changes in the surface vegetation structure.