esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis
### Frequency Analysis of Tilting Response  
Since the tilting and the axial movement are correlated in a certain level, this section only analyzes the tilting responses by transferring the time domain signal into frequency domain. In this paper, the wavelet transform is selected as suitable for nonstationary signals [32]. Time-frequency scalogram plots of the tilting response are obtained and shown in Figures 12-14 in accordance with Figures 9-11. The \(X\)-axis represents time, the \(Y\)-axis represents the frequency on a logarithmic scale, and the color represents the power amplitude.  
Some conclusions in the frequency domain are quite similar to those in the time domain. In all three Conditions, the frequency plots of the \(5^{\circ}\)-deformed disc and flat disc are quite similar, and both responses are almost invisible in Condition 1 (Figure 12) while becoming more intense with a larger impulse force or shorter boundary distance. Also, increasing the impulse force and reducing the boundary distance both enhance the power density of the tilting response. The power amplitude of the four discs in Figure 12 is less than those in the other two Conditions. Besides, impulse action could boost the magnitude level of the tilting frequency. In Figure 13, the initial tilting frequencies of four discs are up to 500 Hz, while the maximum frequencies in Condition 1 are about 100 Hz.  
Figure 9: Time response: BC = 7 mm, initial force = 5 N.  
Figure 10: Time response: BC = 7 mm, initial force = 7 N.  
Figure 11: Time response: BC = 6 mm, initial force = 5 N.  
Figure 12: Scalogram: BC = 7 mm, initial force = 5 N.  
Figure 13: Scalogram: BC = 7 mm, initial force = 7 N.  
Additionally, the rotation speed can increase the power intensity of the tilting. Basically, all the response amplitudes at 200 rad/s are higher than those at 40 rad/s. Examples can be seen in Figure 13, the response of the 5\({}^{\circ}\)-deformed disc at 200 rad/s is much brighter and last longer than it is at 40 and 120 rad/s, as well as the 10\({}^{\circ}\)-deformed disc. Another  
Figure 14: Scalogram: BC = 6 mm, initial force = 5 N.  
phenomenon is that after the impulsion, the frequencies in all figures start to decline until fade away, which means the tilting is a decay process as concluded in time-domain analysis.  
In the scalograms, less-deformed discs have quite clear shapes while larger deformed discs have rather distributed spectrum; for example, in Figures 13a and 14a, the responses of the flat disc are clear curves, which means that the frequency bandwidth of the flat disc tilting is concentrated during the process. However, the nonlinearity of the tilting increases with progressive deformation (taking the 15\({}^{\circ}\)-deformed disc in Figures 13d and 14d as examples), and the responses are distributed within a relatively large bandwidth, which means that the tilting is more chaotic and does not maintain at any specific frequency.
# Sensitivity Analysis of Numerical Coherency Model for Rock Sites
## 2 Numerical Coherency Evaluation Methodology
### Random Field Generation  
To simulate the site effect representing the spatial variability of the target site, the Gaussian model was used. MATLAB R2022a, a numerical computation platform for data analysis and algorithm development, was employed to generate the Gaussian random field [15]. For random field generation, the target reference \(V_{s}\) profile is defined, and the CV is calculated using the standard deviation of the obtained \(V_{s}\) profiles. Horizontal and vertical CL are also used as input parameters. The Gaussian random field is generated using these variables and follows a Gaussian correlation function \(\rho(\tau)\), which depends on the separation distance \(\tau\), as expressed below [16]:  
\[\rho_{i,j}(\tau)=exp\Big{\{}-\pi((|\tau|)/CL)^{2}\Big{\}} \tag{1}\]  
The random field was generated using the reference \(V_{s}\) profile, CV profile, and bidirectional CL profile, following a normal probability distribution. In this study, a 2D random field was generated using the reference \(V_{s}\) profile from the Pinyon Flat array [1]. Figure 2 shows the reference \(V_{s}\) profile at the Pinyon Flat array based on down-hole measurements. Figure 3 illustrates an example of the random field generated for the Pinyon Flat array.  
Figure 1: Distribution of selected earthquake records in terms of epicentral distances, M, and PGV.  
Figure 2: Reference \(V_{s}\) profile at the Pinyon Flat array.
## 2 Methodologies
### Feature Extraction
#### 2.2.2 Multi-Scale Entropy  
Sample entropy may fail to interpret the signal complexity in multiple time scales. MSE was, thus, proposed [16]. MSE accounts for complex dynamics of time sequence over multiple scales by introducing the coarse-graining process directly before entropy calculation. For discrete time sequence \(X=\{x(1),x(2),...,x(N)\}\), the coarse-grained time sequence \(\{y^{(\tau)}\}\) can be calculated by the following formula:  
\[y^{(\tau)}=\frac{1}{\tau}\sum_{i=(j-1)\tau+1}^{j\tau}x(i),1\leq j\leq\frac{N}{ \tau}, \tag{6}\]  
where \(\tau\) is the scale factor. Then, we can obtain the sample entropy of the coarse-grained time sequence \(\{y^{(\tau)}\}\) as MSE in scale \(\tau\).
# Deep Neural Networks Classification via Binary Error-Detecting Output Codes
## 2 Methods and Materials
### Decision Method
Proof of Theorem 2.: According to Theorem 1, for any \(\widetilde{\mathbf{y}}\) there exists a unique winner codeword \(\widetilde{\mathbf{c}}\) with \(t(\widetilde{\mathbf{y}}\ =\ \widetilde{\mathbf{c}})>\
icefrac{{1}}{{2}}\), i.e., \(t(\widetilde{y}_{i}\ =\ \widetilde{c}_{i})>\
icefrac{{1}}{{2}}\), \(i\ =\ 0,\ldots,n-1\) and for any \(\mathbf{r}\ =\ \widetilde{\mathbf{y}}\mathbf{H}^{T}\) there exists a unique winner syndrome \(\widetilde{\mathbf{s}}\) with \(t(\mathbf{r}\ =\ \widetilde{\mathbf{s}})>\
icefrac{{1}}{{2}}\) i.e., \(t(r_{i}\ =\ \widetilde{s}_{i})>\
icefrac{{1}}{{2}}\), \(i\ =\ 0,\ldots,n-k-1\). The winner codeword \(\widetilde{\mathbf{c}}\) is correct if \(\widetilde{\mathbf{c}}\mathbf{H}^{T}\ =\ \mathbf{0}\), the winner syndrome is zero if \(t(\mathbf{r}\ =\ \mathbf{0})>\
icefrac{{1}}{{2}}\). According to Equation (14), \(t(\mathbf{r}\ =\ \mathbf{0})\ =\ t\big{(}\widetilde{\mathbf{y}}\mathbf{H}^{T}\ =\ \mathbf{0}\big{)}\). Due to the identity submatrix within the check matrix, each column in the check matrix has at least one \"\(1\)\". Then,  
\[\begin{array}{l}t\big{(}\widetilde{y}_{i}\ =\ \varsigma_{i}\big{)}> \
icefrac{{1}}{{2}}\ =\ 0,\ldots,n-1\Rightarrow t(\varsigma_{i}\ =\ 0)\ =\ \max_{j\ =\ 0,\ldots,n-1}\left\{\min\{t\big{(} \widetilde{y}_{j}\ =\ \varsigma_{i}\big{)},h_{ij}^{T}\}\right\}>\
icefrac{{1}}{{2}}\\ \The direct calculation of the syndrome truth-value is suitable for classification tasks with many classes. In the one-hot encoding/softmax approach, we need network output for each class, while with binary coding, the minimum number of outcomes required to encode \(n\) classes will not exceed \(\left[\log_{2}(n)+1\right]\). This paper tested a fuzzy Zadeh decision on top of the binary codes against the softmax decision in one-hot encoding. To directly compare both methods, we use the same number of outputs (one output per class), and Zadeh fuzzy logic is applied to find the truest class. To compare softmax with the Zadeh fuzzy logic, we also apply a threshold \(\Delta\) to accept the decision. In the softmax case, a threshold can be used on the confidence value. In the Zadeh decision, according to Equation (11), we can also use the threshold to accept the selected codeword only if \(t(\boldsymbol{r}\ =\ \boldsymbol{0})\geq\Delta\), \(\Delta\in[0,1]\), similar to the softmax threshold. We have also tested the product t-norm, and the probabilistic t-conorm for strong conjunction and disjunction in the fuzzy logic we applied [37]. It seems that it is not suitable for gradient backpropagation. Conversely, in the Zadeh fuzzy logic, the backpropagation is straightforward. The worst neural network output in the best codeword contributes to learning the most (a Zadeh approach from the error viewpoint).
## 4 Experiments
### Compared with Advanced Methods  
[PERSON] et al. [14] provide the telemetry dataset of SMAP&MSL and propose an anomaly detection method using LSTM and non-parametric dynamic threshold. Reference work [49] verifies that the method of generating adversarial networks can be used for unsupervised anomaly detection, and adopts LSTM network structure and cyclic consistency loss training. The SVM method proposed in [50] also improves the anomaly detection of LSTM. This section compares the proposed method with these advanced anomaly detection methods.  
The anomaly detection results are shown in Figure 5. In Figure 5a, Y and y_hat represent the original data and reconstructed sequences, respectively, and the abnormal sequences are marked. In Figure 5b, the red line represents the smoothed result of the difference between the original data and the reconstructed sequence, and marks the anomaly detection result judged by the threshold. The anomaly detection effects of different methods are shown in Table 3. The different methods are all simple parameter debugging. Due to the small capacity of the CMG anomaly detection dataset, the network models adopted using the advanced methods in comparison have used the MRO dataset for a stage of pre-training. At the same time, the learning rate and dropout are adjusted to be consistent with the proposed method.  
It can be seen from Table 3 that the proposed method is superior to other methods in CMG telemetry data anomaly detection task.
# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
## 2 Literature Review of Opportunistic Networks  
OppNets are a specialized class of DTNs characterized by their ability to facilitate communication in environments where continuous end-to-end paths are unavailable. Unlike traditional networks, where routing relies on stable connections, OppNets exploit intermittent contacts between nodes to achieve data delivery.  
OppNets have diverse applications, spanning both civilian and critical domains. In vehicular ad hoc networks (VANETs), OppNets are employed to enable communication in sparsely connected or highly dynamic traffic scenarios, supporting applications such as traffic management, safety alerts, and infotainment [12, 13]. They are also utilized in disaster recovery operations, where infrastructure may be compromised, allowing emergency teams to exchange vital information [14, 15]. Other applications include wildlife monitoring, rural connectivity, and military operations, where the environment imposes constraints on traditional networking approaches [3].  
The core characteristics of OppNets distinguish them from other types of networks. These include intermittent connectivity, dynamic topology, and resource constraints such as limited buffer space and energy [16, 17]. Unlike traditional deterministic routing, OppNet routing must account for the stochastic nature of node mobility and contact patterns, often relying on probabilistic or heuristic methods. Epidemic and spray-based protocols, for instance, aim to maximize delivery probability by replicating messages, though at the cost of increased overhead.  
Routing in OppNets is different from that in mobile ad hoc networks (MANETs) [18]. While both network types share the property of dynamic topology, MANETs assume the existence of relatively stable paths between nodes [19]. They utilize a range of routing protocols, including proactive protocols such as the Optimized Link State Routing Protocol (OLSR) and Destination-Sequenced Distance Vector (DSDV); reactive protocols like the Ad Hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR); and hybrid protocols that combine these approaches, such as the Zone Routing Protocol (ZRP). In MANETs, routing focuses on maintaining and updating paths efficiently. In OppNets, however, the absence of persistent paths necessitates delay-tolerant strategies that prioritize message survival over immediate delivery. This fundamental difference makes OppNets more suited for scenarios where conventional MANET protocols fail due to severe disconnection or mobility constraints [20].
## 2 Background and Related Work
### Mobile Stream Processing  
Using mobile devices to execute tasks taking advantage of available idle resources has been used in the last decade in different contexts considering limited characteristics of the mobile nodes [22; 23]. [PERSON] et al. [24] propose offloading a heavy computation task to mobile devices to reduce latency and provide location awareness for end-users. This approach considers the limited computational resources of mobile devices as well as the short battery life. The authors develop a system that minimizes energy consumption and reduces response times by delegating the pre-processing to mobile devices. [PERSON] et al. [4] propose an energy-aware scheduling algorithm that prolongs the network's lifetime, maximizing the number of alive nodes for ad hoc wireless networks. The authors use two-phased heuristic-based scheduling that uses a local and a global assignment in the wireless network.  
Several authors have proposed to execute an SPS in smartphones, which is called Mobile Stream Processing [5; 6; 20; 25].  
[PERSON] et al. focus on the implementation of Storm over Android devices and proposes Mobile Storm [5], a system that leverages mobile node clusters to process data in real-time, without offloading computation to remote servers. Other works put the main focus on resilience and fault tolerance. Mobistreams [6] is an SPS that reduces the load in servers and network congestion in the cellular network by offloading computation towards mobile devices. The devices build an ad hoc network using WiFi, and the graph topology is mapped over the available devices. This work proposes a broadcast-based approach and token-triggered checkpointing. Similarly, our previous work, Symbiosis [25], is concerned with fault tolerance when using mobile resources as processing operators. It proposes a checkpointing approach triggered by the signal strength and the energy level of the devices. [PERSON] and [PERSON] proposed R-MStorm [20], which aims to make a mobile stream processing system more resilient by providing better survivability. R-MStorm provides diversity on the stream paths and assigns tasks to the nodes that provide better availability to the whole system. Our work complements those proposals by focusing on scheduling and load balance. In [26], the authors propose an optimization problem for the placement of stream processing operators. This problem was tailored to consider fog computing environments.  
Our proposal's main difference is that we use a hybrid graph topology that integrates the mobile resources into the conventional static resources, producing a more resilient network that can execute without mobile nodes. Mobile nodes' processing resources allow for a reduction of the latency compared to a cloud-based processing system while increasing the system's resiliency.
# Key Roles of Plasmonics in Wireless THz Nanocommunications--A Survey
## 5 Plasmonic THz Wireless Nanoscale Link Components
### THz Band Nanotransceivers
#### 5.2.3 Graphene Based THz Transceiver Components
Chalcogenides compounds containing at least one of the chalcogen elements, namely, sulfur, selenium or tellurium, and specifically, chalcogenide glasses are characterized as phase-change active plasmonic devices appropriate for active plasmonic switching/modulation functionality for future nanophotonic device applications. Experimental demonstrations employing gallium anthanum sulfide as a photo-active medium, implement that CMOS/SOI-compatible, chalcogenide glasses used for such functionalities [174]. They may be also used as topological insulators for protecting metallic surfaces of plasmonic structures, as they are immune to scattering from disorder and defects, they can be dynamically controlled via external electric, magnetic or optical excitations, and their optoelectronic response is highly sensitive to the polarization state of incident light.
# Influence of Applied Loads on Free Vibrations of Functionally Graded Material Plate-Shell Panels
## 5 Applications
### Critical Loads of Hinged and Clamped FGM Cylindrical Panels under Uniaxial Compression  
To validate the present model, a buckling analysis of a square FGM panel made of a mixture of aluminum and zirconia with the following properties is considered: \(\mathrm{E_{m}}=70.0\times 109\) N/m\({}^{2}\), \(v_{m}=0.3\), \(\mathrm{E_{c}}=151.0\times 109\) N/m\({}^{2}\), \(v_{c}=0.3\). The geometry of the panel is as follows: length L = 0.1 m, angle 20 = 0.2 rad, radius R = 0.5 m, and thickness h = 0.1 m. The FGM panel is assumed to have the curved edges fixed and the straight edges simply supported (FSFS). The panel is under uniaxial compression applied on the curved edges. A 20 \(\times\) 20 finite element mesh is used (800 triangular elements). The results of the first three buckling load parameters \(\overline{N}_{cr}=N_{cr}R/E_{m}h^{2}\) are given in Table 5a for the different power law indexes considered. From Table 5a, a good agreement with the results obtained by [PERSON] and [PERSON] [23] is observed for the first buckling load, but with discrepancies varying from 4.5% to 9.5% for the second and third buckling loads, respectively. These discrepancies can be related to the different models--HSDT and a classical finite element used by present model, and FSDT and a mesh-free method used by [PERSON] and [PERSON].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & & & & & \\ \hline
**Source** & **Mode** & **p = 0** & **p = 0.5** & **p = 1.0** & **p = 2.0** & **p = 5.0** \\ \hline Ref. [23] & 1 & 1.7195 & 1.3700 & 1.2229 & 1.1025 & 0.9949 \\ PM & 1 & 1.7380 & 1.3884 & 1.2316 & 1.1065 & 0.9973 \\ Ref. [23] & 2 & 1.8416 & 1.4575 & 1.3001 & 1.1796 & 1.0750 \\ PM & 1 & 1.9480 & 1.5385 & 1.3669 & 1.2365 & 1.1247 \\ Ref. [23] & 3 & 2.3913 & 1.8863 & 1.6837 & 1.4017 & 1.4018 \\ PM & 3 & 2.2300 & 1.7310 & 1.5331 & 1.3836 & 1.2675 \\ \hline  & & & & & & \\ \hline  & & & & & & & \\ \hline
**Mode** & **Ceramic** & **p = 0.5** & **p = 1.0** & **p = 5.0** & **Metal** \\ \hline
1 & 0.1330 & 0.1137 & 0.1060 & 0.0943 & 0.0836 \\
2 & 0.1595 & 0.1369 & 0.1277 & 0.1132 & 0.1004 \\
3 & 0.3294 & 2.9664 & 0.2680 & 0.2405 & 0.2073 \\ \hline \end{tabular}
\end{table}
Table 5: (**a**) The critical buckling load \(\overline{N}_{cr}\) of an FGM cylindrical panel under uniaxial compression and FSFS boundary conditions. (**b**) Non-dimensional buckling loads for the hinged cylindrical panel with different gradient indexes. (**c**) Non-dimensional buckling loads for the clamped cylindrical panel with different gradient indexes.
# Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
## 6 Research Results
From the above table, it can be seen that after using the nearest neighbor segmentation algorithm, there was significant improvement in the error value under normal conditions. After Kalman filtering, the error value was further reduced. After correcting the error value and comparing it with the normal value, there were obvious corrections. The improvement rate can be calculated as follows:  
Figure 16.1  
where original weather represents the RMSE of the original weather, and after improvement represents the RMSE with improvement results. This equation was used to calculate the improvement rate under all three weather conditions. The two axes were corrected by about 30% for the rainy weather simulation, by about 15% in the case of heavy smoke, and by about 11% in the rainy weather and heavy smoke simulation, as shown in Figure 16.2
## 3 Experimental Results and Discussion
### The Porosity and Microstructure  
As shown in Figure 4, the porosity of sprayed UHPC was found to be greater than that of cast UHPC at 28 days. The porosity values for sprayed UHPC at different locations were 8.6%, 8.6%, and 8.8%, while this was 7.4% for the cast UHPC. As the curing age increased from 28 days to 90 days, the porosity of the sprayed UHPC at different locations decreased to 7.7%, 7.9%, and 8.1%, while that of the UHPC decreased to 6.2%. This observation further explained the higher 90-day strength of the UHPC compared to the sprayed UHPC.  
Figure 3: (**a**) Mechanical properties of sprayed UHPC at 28 days. (**b**) Mechanical properties of sprayed UHPC at 90 days. (**c**) sprayed UHPC specimen failure modes at 28 days and 90 days.  
Although the sprayed UHPC had a slightly higher porosity at 28 days, its mechanical properties were not negatively affected due to its lower proportion of large harmful pores compared to the cast UHPC. Specifically, the proportions of the large pores (>10 \(\upmu\)m) in the sprayed UHPC at S-F, S-W, and S-V were 14%, 20%, and 17%, respectively, which were significantly lower than the 32% observed in the cast UHPC. Meanwhile, the sprayed UHPC had a higher proportion of harmless pores smaller than 100 nm. Their values at S-F, S-H, and S-V were 70%, 65%, and 65%, respectively, while this was 53% for the cast UHPC. At 90 days, the proportion of harmful pores (>10 \(\upmu\)m) in the UHPC was 6%, which was lower than the evaluated 8% for the sprayed UHPC across the different locations. This reduction in porosity correlated with superior mechanical properties.  
As depicted in Figure 5A, the SEM images revealed that the sprayed UHPC possesses a denser microstructure. The surface bubbles were independent and isolated rather than interconnected. A low porosity, adequate pore distribution, and dense structure were critical factors contributing to the excellent mechanical performance of both the sprayed UHPC and the cast UHPC. The high pressure air not only brings about a dense structure for the sprayed UHPC but it also enhances fiber-matrix interfacial bonding. Figure 5B reveals that the slurry adhering to the sprayed UHPC was more abundant than on the cast UHPC surface. Therefore, the sprayed UHPC exhibits a stronger bond with the cementing material, resulting in a denser formed matrix. This characteristic enables it to dissipate more energy during the pull-out slip friction process compared with the cast UHPC.  
Figure 4: Pore size characteristics of sprayed UHPC: Distribution and proportion at 28 and 90 days. (a,b) 28-day Pore Distribution Diagram, (c,d) 90-day Pore Distribution Diagram.
## 3 Experiments
### Ablation Study
#### 3.4.4 Statistical Significance Analysis  
To further validate the statistical significance of the performance improvements achieved by the proposed HoRFNet model in BCHI classification, this study conducts a t-test comparing SeONet, RFNet, and HoRFNet against the Backbone model across four magnification levels (40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\)). As shown in Table 2, HoRFNet consistently achieves the highest mean improvement (Mean) at each magnification, with values of 7.515, 6.560, 5.710, and 4.395, respectively. The corresponding \(p\)-values are 0.044, 0.010, 0.005, and 0.011, all of which are significantly below the 0.05 threshold, indicating that the performance gains of HoRFNet over the Backbone model are statistically significant. In contrast, although SeONet and RFNet show some performance improvements at certain magnifications, most of their \(p\)-values exceed 0.05. Notably, RFNet yields a \(p\)-value of 0.836 at 400\(\times\) magnification, failing the significance test. These results demonstrate that HoRFNet not only outperforms the Backbone in classification accuracy but also exhibits statistically more significant and robust advantages, validating the effectiveness of its architectural design.