esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application to Model Updating of an In-Service Bridge with Extra-Width
### Model Updating Using Proposed Hybrid Method  
In the hybrid method, a particle is described as \(\mathbf{X}_{i}=[x_{i1},x_{i2},\cdot\cdot\cdot,x_{i10}]\) with 10 parameters aforementioned of the ratio coefficients (real value/original value). For example, the parameter vector of initial FE model is [1,1,1,1,1,1,1,1,1,1]. The lower and upper boundary of each parameter is 0.5 and 1.5, and 400 input samples are yielded with the LHS technique. After the structural analyses, the output datasets of the aforementioned five natural mode frequencies are achieved. Then based on the input and output datasets, the Kriging meta-models of those five frequencies can be yielded. Finally, the objective function is the sum of residuals of mode frequencies of the first five modes between explicit expression value and experimental value. For GMPSO, the amount of particles \(N\) is 200, the inertia weight \(w\) is 0.55, the positive learning coefficients \(c_{1}\) and \(c_{2}\) are both 2, the probability of \(P_{\text{m}}\) is set as 0.1 and the maximum iteration number is 200, the optimized boundary is 0.6 to 1.4. The final updated parameters of the model of the self-anchored suspension bridge are [1.394,1.324,1.100,1.166,1.281,1.262,1.038,1.240,0.703,0.803].  
The mode frequencies results of updated model and experimental data are compared in Table 4. Figure 7 shows the comparison of vibration mode among initial FE value, updated value, and experimental value. The right column of Figure 7 is the 3-D image of mode shapes from ambient vibration test. It can be seen that the differences of frequencies in Table 3 dropped sharply in Table 4, all of the differences are below 6%. In addition, the values of MAC of vibration mode of No. 1 to No. 5 between test and initial FE model are 0.979, 0.973, 0.915, 0.894, and 0.849 respectively in Figure 7 while the MACs between test and modified FE model are 0.983, 0.974, 0.916, 0.873, and 0.882, respectively. This shows that the vibration modes of the updated bridge model have a higher agreement with the experimental value shown in Figure 7, although the increases in MACs are insignificant compared to that in mode frequencies. The whole changes indicate that the relatively precise FE model of this self-anchored suspension bridge with extra-width can be achieved with high efficiency without losing accuracy and the proposed hybrid method does have significant value in model updating of engineering applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Experimental Frequency/Hz** & **Updated Frequency/Hz** & **Difference/\%** \\ \hline
1 & 0.9030 & 0.9161 & 1.45 \\
2 & 1.3670 & 1.3427 & 1.78 \\
3 & 1.3790 & 1.4600 & 5.87 \\
4 & 1.9290 & 1.8246 & 5.41 \\
5 & 2.4170 & 2.2810 & 5.63 \\ \hline \end{tabular} \({}^{1}\) Difference \(=\) \(\frac{\text{Updated Frequency}-\text{Experimental Frequency}}{\text{Experimental Frequency}}\)\(\times\) 100%.  
\end{table}
Table 4: The comparison between modified and real frequencies.  
Figure 7: _Cont._
# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 2 AC/DC Distribution Network Model
### DC Distribution Network Power Flow Model  
According to the branch flow model of the AC distribution network, the branch flow model of the DC distribution network can be derived as follows:  
\[P_{\text{dc},jk}=P_{\text{dc},ij}-\frac{r_{\text{dc},ij}(P_{\text{dc},ij})^{ 2}}{{(V_{\text{dc},i})}^{2}}-P_{\text{dc},j} \tag{7}\]  
\[(V_{\text{dc},j})^{2}=(V_{\text{dc},i})^{2}-\frac{r_{\text{dc},ij}P_{\text{dc },ij}}{V_{\text{dc},i}} \tag{8}\]  
\[P_{\text{dc},j}=P_{\text{dc},j,L}-P_{\text{dc},j,DG} \tag{9}\]  
where \(V_{\text{dc},i}\) is the voltage amplitude of the DC node \(i\); \(r_{\text{dc},ij}\) is the resistance between the branches \(ij\); \(P_{\text{dc},ij}\) is the active power between the branches \(ij\); \(P_{\text{dc},j,L}\) and \(P_{\text{dc},j,\text{DG}}\) are the load active power and DG active power at the node \(j\), respectively. The VSC model is shown in Figure 1. Where \(V\angle\theta_{V}\) is the voltage at the junction of the converter and the AC distribution system, \(R_{\text{VSC}}\) and \(X_{\text{VSC}}\) are the equivalent resistance and reactance inside the converter, \(V_{\text{VSC}}\angle\theta_{\text{VSC}}\) is the phase voltage of the input converter, \(P_{\text{VSC}}+jQ_{\text{VSC}}\) is the power of the input converter, \(P_{\text{dc}}\) and \(V_{\text{dc}}\) are the output power and voltage of the converter.  
The branch flow model of \(R_{\text{VSC}}+jX_{\text{VSC}}\) is as shown in Equation (10). The AC three-phase active power of the input VSC is equal to the DC power of the VSC output. The input voltage and the output voltage satisfy the following relationship:  
\[V_{\text{VSC}}=\frac{\sqrt{3}}{3}\mu V_{\text{dc}} \tag{10}\]  
where \(\mu\) is the DC voltage utilization under SPWM modulation, which is 0.866.
## 3 Related Works
They applied the graph convolution network (GCN) [15] on the generated graph, extracted an embedding vector for each node using an autoencoder, and clustered the nodes using the k-means algorithm based on their embedding vectors. In 2024, [PERSON] et al. [16] expanded on our preliminary study [1] by tuning the hyperparameters to optimize the decomposition of monolith to microservices. In 2024, [PERSON] et al. [17] also extended our preliminary study [1] to optimize microservices. [PERSON] et al. [18] proposed a similar approach to [14] but used the heterogeneous graph neural network [19] to handle multiple types of nodes and edges. [PERSON] et al. [20] optimized the hyperparameters of existing machine learning-based monolith-to-microservices methods, using the work in [14] as an example. In 2024, [PERSON] et al. [21] also used the graph neural network to perform microservice decomposition. However, our approach differs from theirs because we use a variational autoencoder instead of the standard autoencoder. In 2025, [PERSON] et al. [22] proposed an approach to decompose microservices using graphs and K-means, unlike our C-means for clustering. Moreover, several systematic mapping studies and literature reviews [9; 23; 24; 25; 26; 27] have investigated the challenges and opportunities associated with decomposing monolithic systems into microservices. Collectively, these works report that while microservice migration offers notable benefits such as improved scalability, agility, and maintainability, it remains a technically complex and evolving area. Common motivations identified across these studies include the need to modernize legacy systems and address performance limitations, but persistent issues such as tightly coupled architectures, shared databases, and insufficient tool support continue to hinder migration efforts. Many approaches and frameworks have been proposed for microservice identification and decomposition, though most are still in early stages and often lack automation or standardized evaluation metrics. Some studies have introduced decision-support frameworks based on empirical evidence to guide organizations beyond intuition-based approaches. The recent literature has also pointed to the potential of AI-assisted methods to automate aspects of the migration process. However, these approaches are still emerging, and challenges related to tool maturity, dataset availability, and the human factors of migration remain underexplored. Overall, while microservices adoption continues to grow, these studies emphasize that substantial gaps persist in automation, evaluation, and practical guidance.
# The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
## 1 Introduction
* Due to the inhomogeneity of network topology, we put forward a novel spheroidal percolation model for the analysis of intra-cluster traffic, based on which the achievable rate of intra-cluster transmission is derived. * While for the inter-cluster traffic, \"information pipes\" are built among the clusters to obtain the achievable rate of inter-cluster transmission. The rest of this paper is organized as follows: Section 2 gives the network model and transmission model. In Section 3, we establish a spheroidal percolation model and give a routing strategy with five phases to derive the achievable rate of intra-cluster transmission. In Section 4, we construct some \"information pipes\" to get the rate of the inter-cluster transmission and then get the lower bound of the throughput capacity in Section 5. We finally conclude this paper in Section 6.
## 4 Conclusions  
WSR is significantly enhanced by M-DCPD resin with MAH content of around 4 wt% to 6 wt% from the two following aspects. On the one hand, more energy dissipation during disintegration and reconstruction of the filler aggregates under filler-resin interaction lead to a high hysteresis. On the other hand, anhydride plays a dominant role in balancing filler-filler interaction with filler-lubricant interaction, boosting surface polarity of vulcanizates within an adequate proportion. Consequently,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Hardness** & **Cross-Linking** & **100\%** & **300\%** & **Tensile** & **Elongation** & **Tear** & \multirow{2}{*}{**Resilience** **(\%)**} \\  & **(shore A)** & **Density** & **Modulus** & **Modulus** & **Strength** & **at Break** & **Strength** & **(\%)** \\  & **(shore A)** & **(\%)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(\%)** & **(\%)** \\ \hline R0 & 54 & 20.8 & 1.6 & 10.6 & 17.6 & 414 & 33.1 & 40 \\ R2 & 53 & 22.0 & 1.6 & 11.2 & 17.7 & 404 & 32.1 & 41 \\ R4 & 54 & 21.2 & 1.7 & 12.6 & 16.7 & 352 & 29.9 & 39 \\ R6 & 54 & 21.4 & 2.1 & 14.4 & 16.0 & 320 & 29.7 & 40 \\ R8 & 55 & 21.4 & 1.9 & 13.3 & 16.4 & 339 & 30.6 & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical-mechanical properties of silica-filled solution-polymerized styrene-butadiene rubber /butadiene rubber (SSBR/BR) vulcanizates.  
Figure 9: SEM images of the vulcanizates after etching of the resins.  
with the addition of DCPD resin modified with MAH in 4 wt%-6 wt%, the BPST index and the value of tan\(\delta\) at 0 \({}^{\circ}\)C increased by around 16% and 15%, respectively, as well as a decrease in contact angle by around 10%, indicating that both external and internal friction are obviously improved, the surface polarity of vulcaniates are also enhanced. As a result, the modification of DCPD resin with appropriate dosage of MAH could give a great improvement in WSR of the silica-filled SSBR/BR. Moreover, the enhancement of WSR is valid to improve driving safety and desirable duration of tire. Our work proposes a detailed strategy to use modified DCPD resin as antiskid resin by introducing polar groups, and we anticipate further efficient methods to explore more multifunctional resins.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We gratefully acknowledged the valuable materials supported by Lianyungang Rebo Chemical Co.  
The authors declare no conflict of interest.
## 2 Proposed Methodology
### Soft Voting Ensemble Approach  
The soft voting approach considers the confidence levels of each classifier's predictions. It converts each classifier's predictions into probabilities for the respective classes and then aggregates these probabilities to determine the final classification result. Using a voting ensemble approach often results in better performance than a single model. In this study, DCNN models were ensembled and the final decision was determined using a soft voting technique, as given by Equation (4).  
\[\varepsilon_{j}=\text{arg}max_{m}\sum_{i=1}^{n}\alpha_{i}Y_{j,m}^{i} \tag{4}\]  
where \(n\) denotes the total number of classifiers, \(\alpha_{i}\) represents the weight of the \(i\)th classifier, and \(Y_{j,k}^{i}\) is the prediction probability of the \(i\)th classifier for the \(j\)th sample of the \(m\)th class.
# An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD
## 4 Application Example  
In order to illustrate the accuracy and superiority of the proposed algorithm, the public bearing data set of the University of Cincinnati was first used to verify the effectiveness of the algorithm.  
Figure 3 is the corresponding experimental setup. Selecting the outer ring fault data of bearing 1 to be analyzed. According to the parameters of the experimental device (\(z=16\), \(d=0.331\) in., \(D=2.815\) in., \(\alpha=15.17^{\circ}\), \(n=2000\) rpm) and formula (14), it can be calculated that the fault frequency of the outer ring is: \(f_{ep}=236.4\) Hz.  
\[f_{ep}=\frac{z}{2}\textbf{e}\frac{n}{60}\bigg{(}1-\frac{d}{D}\cos\alpha\bigg{)} \tag{14}\]  
where, \(z\) is the number of rolling bodies; \(d\) is the diameter of the rolling body; \(D\) is the bearing pitch; \(\alpha\) is contact angle; and \(n\) is the rotation speed.  
Taking (std_max) + (std_min), which are shown in the Equations (12) and (13), as the objective function, Equation (15) as the fitness function, optimizing the white noise amplitude coefficient according to Section 3 to get \(e=0.342819\). According to literature [18], the minimum value of \(N\) is 20. After the EEMD of the original signal, 13 IMF components can be gotten. Figure 4 is a Fourier transform spectrum of the maximum kurtosis IMF component obtained by the proposed algorithm in this paper. In order to show the superiority of the decomposition, the Fourier transform spectrum of the original signal is shown in Figure 5.  
\[fitness=1/(1+f(\text{obj})), \tag{15}\]  
Figure 3: The experimental setup.  
where \(f(\text{obj})\) is the objective function.  
It can be seen that the fault frequency can be easily found in the IMF of EEMD optimized by the improved ABC proposed in this paper. However, in the Fourier transform spectrum of the original signal (Figure 5), the fault frequency cannot be found.  
In order to illustrate the superiority of the proposed algorithm, the orthogonality of the IMF components (index of orthogonality, referred as IO) and the time indicators are used in this paper. The IO calculation formula is shown in Equation (16) [26], the smaller the value, the higher the accuracy. The specific values of the indicators after decomposition using EMD, experienced EEMD, EEMD of literature [13; 16; 18], EEMD optimized by ABC and EEMD optimized by IABC are listed in Table 2.  
\[IO=\sum_{t=0}^{T}\left(\sum_{i=1}^{n+1}\sum_{j=1}^{n+1}c_{i}(t)c_{j}(t)/x^{2}(t )\right)(i\
eq j), \tag{16}\]  
where \(T\) is the total length of the signal.  
Figure 4: The Fourier transform spectrum of the IMF component with the maximum kurtosis.  
Figure 5: The Fourier transform spectrum of original signal.  
It can be seen from Table 2 that the proposed EEMD, whose amplitude parameter \(e\) was obtained through the improved artificial bee colony optimization algorithm, has a better decomposition effect after a small number of decompositions.
# Probabilistic Mixture Model-Based Spectral Unmixing
## 2 Related Work
Our approach is probabilistic and demonstrates the ability of a hierarchical Bayesian mixture model to account for these uncertainties intrinsic to the unmixing problem fully. We retain the conceptual simplicity of a simplex method, but unlike N-FINDER, which relies on the assumption of fixed endmembers, our Bayesian mixture model explicitly accounts for the uncertainty in both individual observations and underlying endmember signatures, leading to more realistic and informative unmixing results. The intent is to highlight the advantage of probabilistic modeling over point-estimate approaches, as the Bayesian approach explicitly acknowledges and incorporates the epistemic uncertainties inherent to the unmixing process. Additionally, Bayesian approaches can incorporate aleatoric uncertainties in the problem arising from (for example) noisy measurements. The final solutions engendered by such a Bayesian approach reflect the model's confidence in its prediction. Having access to such predictive uncertainties can assist in informed decision-making under uncertainty.
## 5 IEEE 802.15.4 Technology
### 6 LowPAN  
In 2007 the Internet Engineering Task Force (IETF) has released an open standard called 6 LowPAN in order to use IPv6 over 802.15.4. Abbreviation _6 LowPAN_ stands for IPv6 over Low-Power WPANs. IP for Smart Objects (IPSO) Alliance is promoting the use of 6 LowPAN and embedded IP solutions in smart objects. As Zigbee bottom two layers in 6 LowPAN are the 802.15.4 layers.  
6 LowPAN is a protocol definition describing how to utilize IPv6 on top of low power, low data rate, low cost personal area networks. The charter of 6 LowPAN working group is to define how to carry IP-based communication over IEEE 802.15.4 links while conforming to open standards and assuring interoperability with other IP devices. Some key technologies of 6 LowPAN are as follows [33]. (i) The coordination of IPv6 and IEEE802.15.4: by defining an adaptation layer, 6 LowPAN compresses the 60 bytes long headers in IPv6 to 7 bytes and fragments the 1280 bytes long IPv6 packets to fit 127 bytes long 802.15.4 packets. (ii) Address assignment and management: one of the distinctive features of 6 LowPAN is the capability of the dynamic assignment of 16-bit short addresses; by using this short address, hierarchical routing can be employed. (iii) Network management: owing to the large scale of network and the distribution of place, LR-WPAN should possess self-healing ability, and LR-WPAN management technology is requested to be able to manage highly dense deployment equipments with a very low expense. 6 LowPAN is apt to use SNMPv3 (Simple Network Management Protocol) in LR-WPAN to progress network management.  
The fundamental difference between 6 LowPAN and Zigbee is the IP interoperability of the first. 6 LowPAN devices are capable of communication with other IP-enabled devices whereas Zigbee node needs an 802.15.4/IP gateway to interact with an IP network. The decision to select one standard versus another should be determined by the target application. For an application in which there is no need to interface with IP devices or the packet size is small, it is not necessary to implement 6 LowPAN, which performs fragmentation. Zigbee can achieve better overall performance in such an application.  
Figure 10: The superframe structure used in the tree-based topology.
## 6 Conclusions  
The evolution of non-terrestrial network (NTN) systems for direct-to-device (D2D) services brings significant advantages, leveraging unmodified handsets for seamless integration. However, the competitive pursuit of frequency bands compatible with smartphones has led to potential interference issues. Specifically, the overlap of Starlink in frequency band 1990-1995 MHz with existing MSS systems and future NTN systems in the n256 band in, poses challenges with space-to-space link interference. Our study demonstrates that this interference can have severe implications; specifically, our results indicate up to a 40% reduction in uplink throughput for future NTN systems and a significant degradation of the services for the currently existing MSS systems.  
It is essential to address this issue, given its significant impact on users and the potential depletion of valuable spectrum resources. Considering the inherent challenge of avoiding space-to-space interference, especially given the anticipated high number of active Starlink satellites, there are only two feasible solutions for interference mitigation. The first involves using non-overlapping bands within the n25 band. The second entails separating the MSS/NTN and Starlink service areas. This separation can be implemented by countries aiming to provide satellite services within their territories, requiring explicit agreements and coordination between different administrations to prevent mutual interference.  
This research received no external funding.  
Not applicable.  
Figure 11: CDF of aggregate interference from Starlink to the NTN satellite receiver: (**a**) carrier-to-interference reduction; (**b**) throughput loss of the NTN uplink.
## 3 ME-SSA Transmitter Design and Waveform Definition
### MASSIVE Waveform Definition  
Capitalizing on the technical innovations described above (i.e., the support of QPSK modulation, TDM between pilot and data symbols and CE spreading) with respect to the heritage waveforms of [5], a new family of waveforms named MASSIVE waveforms were designed, implemented and tested in the project [10].  
The MASSIVE waveforms parameters were designed to support, in particular, the satellite IoT scenarios as described in previous sections. These parameters include the support of a lower chip rate (160 kcps), several spreading factors (16, 32, 64, 128 and 256), data packet sizes (38 or 150 byte) and coding rates (1/3, 1/2, 2/3).  
Table 1 summarizes a sub-set of such waveforms as already tested in the MASSIVE test-bed. The modulation and coding (ModCod) pairs and spreading factor (SF) is specified for each case
# M2 ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios
## 2 Detailed Design
### Multimodal Emotion Recognition Based on Attention Mechanism
#### 2.2.2 Multimodal Attention Fusion  
People often express emotions in different ways in reality. Some people prefer to express their emotions through various facial expressions while others through different tones of voice. Based on this phenomenon, it can be inferred that different modalities of emotional features contribute differently to the final emotion classification. Therefore, in our fusion model, an attention mechanism was adopted to determine the importance of each modality in the final classification. Specifically, the attention mechanism is used to allocate attention weights to the emotional features \(F^{T}\), \(F^{A}\), and \(F^{V}\) obtained in _Part 1_. Finally, these weighted features are summed to obtain the fused emotional feature, denoted as \(F^{*}\). The calculation process is illustrated in Equations (10)-(12):  
\[H_{X}=\tanh(W^{X}_{att}F^{X}+b^{X}_{att}), \tag{10}\]  
\[\beta_{X}=softmax(H_{X}), \tag{11}\]  
\[F^{*}=\sum_{X}F^{X}\beta^{T}_{X}, \tag{12}\]  
where \(X\) represents the modality, which can be text, video, or audio. \(H_{X}\) represents the hidden unit state, \(W^{X}_{att}\) represents the weights, and \(b^{X}_{att}\) represents the biases. Equation (11) is used to normalize the weight vector. The resulting \(F^{*}\) is then fed into the fully connected layer and the softmax classification layer for emotion classification.