esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Response Spectrum Method under Coherent Ground Excitations  
The differential equation of motion of a lumped mass system with \(n\)-degrees-of-freedom under a coherent ground motion can be written as  
\[\mathbf{M}\ddot{\mathbf{X}}+\mathbf{C}\dot{\mathbf{X}}+\mathbf{K}\mathbf{X}=- \mathbf{M}\ddot{\mathbf{x}}_{g}(t), \tag{1}\]  
where, \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) are the \(n\times n\) mass, damping, and stiffness matrices; \(\mathbf{X}\), \(\dot{\mathbf{X}}\) and \(\ddot{\mathbf{X}}\) are the displacement, velocity, and acceleration vectors; \(\mathbf{I}\) is the influence vector, which represents the displacements to the structural degrees of freedom; and \(\ddot{\mathbf{x}}_{g}(t)\) as a stochastic process is the time-history of the ground acceleration motion. Then, the solution of Equation (1), the structural displacement, \(\mathbf{X}\left(t\right)\), is written as  
\[\mathbf{X}(t)=\sum_{j=1}^{n}\boldsymbol{\varphi}_{j}u_{j}(t)=\boldsymbol{ \Phi}\mathbf{u}(t), \tag{2}\]  
where \(\mathbf{u}(t)=[u_{1}(t),u_{2}(t),\cdots u_{n}(t)]^{T}\), in which \(u_{j}(t)\) is a random excitation corresponding to the \(j\)th mode; \(\boldsymbol{\Phi}=[\boldsymbol{\varphi}_{1},\boldsymbol{\varphi}_{2},\cdots \boldsymbol{\varphi}_{n}]\) is the mode matrix, in which \(\boldsymbol{\varphi}_{j}\) is the \(j\)th mode vector. Here, structural damping is supposed as proportional damping. After substituting Equation (2) into Equation (1), Equation (3) for the \(j\)th modal is obtained by using the orthogonal condition of vibration modes  
\[\ddot{u}_{j}(t)+2\zeta_{j}\omega_{j}\dot{u}_{j}(t)+\omega_{j}^{2}u_{j}(t)= \beta_{j}\ddot{x}_{g}(t)j=1,2,\cdots,n, \tag{3}\]  
where \(\omega_{j}\) and \(\xi_{j}\) denote the natural frequency and damping ratio corresponding to the \(j\)th mode; \(\beta_{j}\) is the \(j\)th mode participation factor, which is obtained from Equation (4)  
\[\beta_{j}=-\frac{\boldsymbol{\varphi}_{j}^{T}\mathbf{M}\mathbf{I}}{ \boldsymbol{\varphi}_{j}^{T}\mathbf{M}\boldsymbol{\varphi}_{j}}. \tag{4}\]  
Introduce the differential equation of motion of the standard single-degree-of-freedom system  
\[\ddot{\delta}_{j}(t)+2\zeta_{j}\omega_{j}\dot{\delta}_{j}(t)+\omega_{j}^{2} \delta_{j}(t)=\ddot{x}_{g}(t)j=1,2,\cdots,n, \tag{5}\]  
where, \(\zeta_{j}\), \(\omega_{j}\), and \(\ddot{x}_{g}(t)\) are the damping ratio, natural frequency, and ground motion, respectively. Then,  
\[u_{j}(t)=\beta
# High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure
## 2 Signal Model  
The monostatic stepped-frequency radar [18] is employed to image indoor stationary scenes. A uniform linear array (ULA) is deployed using \(N\) wideband transceivers with known positions \(x_{n}\), \(n=1,\cdots,N\), and an inter-element interval of \(d\). The stepped-frequency signal, \(f_{m}\), \(m=1,\cdots,M\), with a uniform distribution over the signal bandwidth \(B\), is transmitted by each transceiver. Since most of the front wall returns are assumed to be eliminated, only the target echoes are analyzed here [19]. Considering the monostatic radar scenario, the target echoes \(y(m,n)\) can be formulated as [20]  
\[y(m,n)=\sum_{l=1}^{N_{x}N_{y}}w_{l}\exp(-j2\pi f_{m}\tau_{ln}), \tag{1}\]  
where \(N_{x}N_{y}\) denotes the total number of pixels in the region of interest, \(\tau_{ln}\) denotes the time delay from the \(l\)th scatterer to the \(n\)th transceiver, and \(w_{l}\) denotes the complex reflectivity of the \(l\)th pixel. Assume the thickness and permittivity of the wall are already known or can be accurately estimated, thus the effects of propagation through the wall can be well-compensated, and \(\tau_{ln}\) can be calculated from geometric considerations [21].
# Emerging Biomedical Applications of Carbon Dot and Polymer Composite Materials
## 4 Emerging Applications of Carbon Dots and Their Composite Materials
### Anti-Counterfeit and Security  
CDs have been applied in forensic medicine, a science that provides scientific evidence with respect to anti-counterfeits [106; 107; 108; 109]. A previous study reported that scientists were able to use orange-emitting CDs composed of the rhodamine B substance to rapidly and efficiently visualize the latent fingerprints even after four months under ultraviolet (UV) light and in bad environments, such as bent and colored surfaces [72]. In addition, CDs have been employed in anti-counterfeit systems for detecting forgery in banknotes. A CD-based printable ink made from luminescent m-phenylenediamine and poly(vinyl alcohol) allowed for the detection of cheating in banknotes by displaying triple-mode photoluminescence when excited with a 365 nm UV light [108]. Several studies regarding the use of CDs for the detection of drugs, narcotics, and explosive compounds have been performed [109]. Figure 5 demonstrates the possible role of CDs in anti-counterfeit detection. CDs are inevitable in the world of counterfeit detection. Hence, developing portable carbon-dot devices or sensors that are resistant to heat and water will facilitate the role of counterfeit investigators. Colloid photonic crystal (CPC)/CD beads prepared with poly(styrene-butyl acrylate-acrylic acid) (P(St-MMA-AA) microspheres were recently constructed by combining environmentally friendly fluorescent CDs (derived from turtle shell); fluorescent CPC/CD patterned films were then created and used as a multi-signal anti-counterfeiting device [12].  
Figure 4: The thearanostics role of carbon dots (CDs) in coronavirus infection. CDs prevents coronavirus receptor attachment and, by its fluorescence property, allow for bioimaging of the infected lung. The Figure was created with BioRender.com (accessed on 1 September 2022).
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 6 Discussion
### Comparison with State-of-the-Art Methods
#### 6.1.5 Scalability  
As demonstrated in Figure 6, Adaptive-DecayRank scales linearly with increasing edge updates, making it suitable for real-time applications. On the DARPA dataset, it efficiently processes small updates in **microseconds**, moderate graphs in **milliseconds**, and \(1M\) edges or large-scale datasets in **14.23 s**. Even on RTM-30, which contains **19M edges**, our model maintains a processing speed of **0.08 s per 1M edges**, demonstrating its capability for near real-time anomaly detection. This scalability is essential for applications in cyber-security, where rapid identification of network threats is critical. It is also beneficial in financial fraud detection, where monitoring evolving transaction patterns in real time can help detect coordinated fraudulent activities.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Aggregation-Based Source Location Protection Scheme (ASLP)  
The _ASLP_[46] technique uses similar filtration techniques as _OFS_. _ASLP_ has three phases: fake packets, packet encryption, and data aggregation. In _ASLP_, the _WSN_ is divided into clusters, and each cluster has a cluster head. Cluster heads follow a tree structure scheme to reach the sink. Every cluster member shares a key with its cluster head to encrypt the traffic between them. In addition, nodes communicate with their cluster head using the exponential distribution to reduce the delay and overhead. The exponential distribution is only controlled by one parameter \(\lambda\), which is the transmission rate. Cluster nodes report the event to their cluster head, which also can be the sink node periodically; this period is decided by the value of the period time for reporting the event, \(\tau\). In the second phase, values of \(\lambda\) and \(\tau\) are distributed throughout the network by the sink. In the third phase, the actual data aggregation and reporting take place. A node transmits a real packet whenever it detects a real event to the sink using the exponential distribution. Otherwise, the node transmits a fake packet instead. The cluster heads receive the packet from cluster members and forward it to the sink using an encrypted channel. Values of \(\lambda\) and \(\tau\) are utilized in this technique to trade-off between latency and power consumption. This trade-off can be adjusted according to the requirements of the applications.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 2 Literature Review
[34] introduced a hybrid topology that merges star and mesh structures to enhance the coverage and fault tolerance in wildfire-prone areas, optimizing energy use and communication efficiency for reliable fire detection and fast data transmission. Recent advances in wildfire detection have increasingly leveraged emerging technologies such as LoRa/LoRaWAN-based WSNs and AI, which complement traditional methods in terms of scalability, cost-efficiency, and responsiveness. For instance, a low-cost WSN deployed in the New Forest, UK, used LoRaWAN to transmit over 7500 environmental measurements at 30 min intervals, demonstrating enhanced spatial and temporal monitoring capabilities [35]. In a separate deployment in India's Ettmagaram Wildlife Sanctuary, a Forest Protection System (FPS) integrating multiple sensors with Arduino Nano boards and LoRaWAN communication achieved 97.14% accuracy in intrusion detection and 100% accuracy in fire detection [36]. Moreover, [PERSON] et al. [37], proposed an energy-efficient LoRaWAN system using filtering algorithms (e.g., Exponential Smoothing, Moving Averages, Least Squares), with their combined filter (DROWSIER) reducing energy usage by 53% while maintaining timely fire detection. On the other hand, AI has proven particularly effective in enhancing the early detection of wildfires. A system using 44 CO\({}_{2}\) sensorsand AI models such as AutoEncoders and LSTM networks significantly improved sensor activation and detection performance, outperforming basic threshold-based methods by up to 56% [38]. These technologies provide robust foundations for real-time analysis, the prediction of fire front propagation, and improved decision-making in fire management. Accordingly, they have been discussed and cited in this work to broaden the technological context and reinforce the relevance of the proposed system. In WSNs for wildfire detection, efficient energy use is vital due to remote deployment locations. Recent research has focused on techniques like duty cycling, adaptive transmission power control (ATPC), and renewable energy harvesting. [PERSON] et al. [39] advanced this area by integrating hybrid solar and wind energy into a WSN model, significantly extending sensor node lifespan in remote forest areas where long-term deployment is essential.
# Intermediate Isolation System with Nonlinear Lower Structure and Isolation System
## 6 Time History Analyses on 3D Models and Comparison to IIS Design Spectrum
### Modelling Approaches and Analysis Types
Moreover, the possible couplings between the two horizontal components and between the horizontal and vertical components of the ground motion have been neglected. Soil-structure interaction has also been neglected. The results of linear and nonlinear THA are compared to the RSA results. The object of comparison is threefold, and concerns:  
* modelling of the as-is and retrofitted building configurations: lumped mass (3 DOF IIS and SDOF LS) vs. three-dimensional (3D FE IIS and 3D FE LS) models;
* assumption of the mechanical behaviour of the existing structure and of the isolation system: equivalent linear vs. nonlinear behaviour; \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Earthquake** & \begin{tabular}{c} **Emilia** \\ **Pianura** \\ **Padana** \\ \end{tabular} & **Darfield** & **Christchurch** & **Christchurch** & \
\begin{tabular}{c} **Hyogo-Ken** \\ **Nanbu** \\ \end{tabular} &
\begin{tabular}{c} **Superstitution** \\ **Hills** \\ \end{tabular} & **Northridge** \\ \hline Date & 29 May 2012 & 3 September 2010 & 21 February 13 June 2011 & 16 January 1995 & 1987 & 1994 \\ Waveform ID & 317 & 335 & 341 & 389 & 411 & 449 & 459 \\ Earthquake ID & 133 & 137 & 142 & 149 & 34 & 93 & 99 \\ Station ID & MOGO & TPLC & RHSC & RHSC & TKS & WSM & ST\_24389 \\ Fault Mech. & reverse & strike-slip & reverse & reverse & strike-slip & strike-slip & reverse \\ Ep. Distance & 16.43 & 23.58 & 13.73 & 14.76 & 17.45 & 19.5 & 20.19 \\ [km] & 0.17 & 0.19 & 0.25 & 0.19 & 0.18 & 0.21 & 0.22 \\ EC8 Site class & C * & C * & C * & C * & C * & C & C \\ \hline \hline \end{tabular}  
* based on geological information alone.  
\end{table} 
Table 3: Major data of the ground motion records. Figure 7: Set of spectrum-compatible acceleration response spectra of Pozzuoli site. * seismic input and analysis type: code elastic spectrum and Response Spectrum Analysis (RSA) vs. ground motion acceleration records averagely matching the elastic spectrum and Time History Analyses (linear and nonlinear THA). All in all, the final aim is the evaluation of step 2, i.e., parametric RSA on the lumped mass model and the resulting _IIS design spectrum_, as a reliable prediction of the actual response of IIS. That is, though some slight scatter between the values of response parameters is allowable, the behaviour trend should be confirmed. In the following, linear time history analyses on 3D FE building models are first carried out in order to establish the effects of modelling (lumped mass vs. three-dimensional models) and seismic input  
The results in Figures 8-11 are shown as a function of the fundamental period of the IIS (masonry structure plus isolated addition), \(T_{1}\), while the results of the RSA on lumped mass models, in Figure 3b, have been plotted as a function of the ratio \(T_{ISO}/T_{LS}\).
## 4 Simulation Results
### Comparison of the Network Energy Consumption
#### 4.6.2 Comparison of Network Energy Consumption Uniformity  
It can be seen from Figures 20 and 21 that the protocol can effectively balance the energy consumption of nodes in the network compared to LEACH, DEEC, and GSEN.  
In the network iteration, the maximum energy range of LEACH reaches approximately 0.13 J, that of DEEC and GSEN both reach approximately 0.10 J, that of EECRP-HQSND reaches approximately 0.028 J, and that of EECRP-HQSND-ICRM reaches a maximum of 0.01564 J.  
As for the energy variance, EECRP-HQSND EECRP-HQSND-ICRM achieve variances of 0-0.000036. The maximum variance of LEACH reaches 0.0010, that of DEEC reaches 0.0003, and that of GSEN reaches 0.0004.  
Based on the comprehensive energy range and variance, it can be determined that the protocol in this paper can achieve a good balance of energy consumption among the nodes in the network.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 4 Countermeasures against Covert Channel Attacks
### Runtime Detection of Covert Channels
#### 4.3.2 Leveraging Machine Learning Techniques  
Machine learning has been used as a method to detect anomalies and classify whether processor activity is suspicious (i.e., the presence of a covert channel communication), particularly for cases where signal amplitudes are smaller and threshold-based techniques would not be sufficient, as described above [62]. It was found that TCC signals have multiple side lobes of high amplitudes that can be used for detection [66]. An artificial neural network classifier was developed and trained for TCC detection. The training data consisted of thermal signals over a period of 2 s, sampled at 1000 Hz, that were then transformed into the frequency domain (10 Hz to 500 Hz) with a discrete Fourier transform [66]. After training, this classifier was used during runtime to infer TCCs. The global manager ran a detection cycle to check for a TCC, where the spectrum of the IPC signals of each logical core was extracted. The IPC spectrum was used during detection, instead of actual thermal signals, since they were correlated, i.e., an increase in IPC resulted in an increase in temperature. The proposed detection method using artificial neural networks was able to achieve a detection accuracy of 99%, even for TCCs with the lowest amplitudes (stealthiest) [66]. Additionally, it was shown to cost less in runtime overhead (<0.187%) and energy overhead (<0.072%), as compared to jamming-based countermeasures [66]. While this work demonstrated high detection accuracy, the effect of the location of the receiving and transmitting cores on the accuracy was unexplored. Additionally, the effect of varying amounts of noise from other cores performing normal or CPU intensive operations was not quantified. Finally, an analysis of how accuracy changes with varying system size (total number of cores) would provide a metric on how generalized the proposed neural network classifier is.  
Similarly, a three-layer convolutional neural network (CNN) was developed to detect electromagnetic covert channels in [57]. The CNN was trained by using labeled EM spectra and legitimate/expected system processes. To test the capability of the neural network, white noise was added to the testing spectrum to simulate a jamming-based countermeasure (to increase attenuation). It was shown that the CNN could identify covert channel signals with 99% accuracy when there was less than 12 dB of attenuation. After this threshold, there was a drastic decrease in accuracy. For example, detection accuracy dropped to 60% at 16 dB attenuation.
# Direct Fabrication of Ultrahydrophobic Laser-Induced Graphene for Strain Sensors
###### Abstract  
Laser-induced graphene (LIG) has garnered tremendous attention in the past decade as a flexible, scalable, and patternable alternative for fabricating electronic sensors. Superhydrophobic and superhydrophilic variants of LIG have been demonstrated by previous studies. However, stability analysis of the superhydrophobic surface property has not been explored. In this study, we use an infrared nanosecond laser to fabricate reduced graphene oxide (rGO)-based strain sensor on a carbon fiber reinforced polymer (CFRP) composite substrate. The fabricated sensor is characterized to determine its gauge factor using a three-point bend test demonstrating a gauge factor of 40. The fabricated LIG exhibits excellent superhydrophobic properties with a high contact angle (\(>\)160\({}^{\circ}\)). Both superhydrophobicity and piezoresistivity of the LIG under water immersion are studied for 25 h, demonstrating high stability. The obtained results could be of interest to several sectors, especially for maritime and high humidity applications.  
superhydrophobic; laser-induced graphene; strain sensors; carbon fiber composites; nanosecond laser +
Footnote †: journal: Applied Physics Letters
## 3 Results
### Quantitative Results  
#### 3.3.1 Variation of MBN Architectures  
To evaluate the effect of the proposed mask branch network (MBN), we tested the main network as a baseline and compared the results from the main network with MBN. We employed two networks as the main network: DenseNet-BC [21] and ResNetV2-101 [29]. As shown in Table 1, the proposed networks with MBN outperform the baselines in every performance measure in both kinds of main networks. While all versions of our method achieve higher performance overall compared with the baseline, the version of best performance is different depending on the baseline. In the case of DenseNet, MBN-V2 achieved the best performance for all metrics. In the lower part of Table 1, the result of our method with ResNet is shown for performance comparison.  
Similar to cases with DenseNet, all of the proposed methods outperform the plain ResNet model. Unlike experiments based on DenseNet, the mask branch network achieved the best performance by using MBN-V2 for sensitivity and MBN-V3 for specificity and AUC. Figure 5 compares ROC curves for the mass classifier with the mask branch network.
## 2 Principles and Model Architecture of KANs  
### Fundamental Principles  
Kolmogorov-Arnold Networks (KANs) are neural architectures grounded in the Kolmogorov-Arnold representation theorem, which asserts that any multivariate continuous function can be decomposed into a finite composition of univariate functions and additive operations. Unlike conventional Multilayer Perceptrons (MLPs) that employ fixed nonlinear activation functions at nodes, KANs parameterize learnable activation functions on edges. They offer enhanced interpretability and flexibility.  
KART offers a theoretical foundation for expressing a high-dimensional function \(f(\mathbf{x})\) as a superposition of univariate functions, as shown in Formula (1). \(\phi_{q,p}\) and \(\Phi_{q}\) are univariate functions. KANs operationalize this by structuring networks to learn these inner and outer functions adaptively.  
Edges (rather than nodes) hold trainable activation functions. They are typically parameterized via B-splines. Nodes aggregate inputs linearly, while edges apply nonlinear transformations. The inversion of roles compared to MLPs enables fine-grained control over function approximation.  
Activation functions on edges are represented as linear combinations of basis functions (B-splines) with learnable coefficients. For an edge connecting node \(i\) to \(j\), the activation is shown in Formula (2).  
\[f(x)=\sum_{q=1}^{2n+1}\Phi_{q}\left(\sum_{p=1}^{n}\phi_{q,p}(x_{p})\right) \tag{1}\]  
\[\sigma_{ij}(x)=\sum_{k}c_{ijk}\cdot B_{k}(x) \tag{2}\]  
where \(B_{k}\) are basis functions and \(c_{ijk}\) are trainable parameters. It allows the KANs to dynamically adjust activation shapes during training. KANs often use grid structures to discretize the input domain of basis functions. The gradients are computed via automatic differentiation. The parameters are optimized using gradient descent with regularization to improve smoothness and prevent overfitting.
# Analysis of AMI Communication Methods in Various Field Environments
## 2 Power Line Communication for AMI  
In this section, we first introduce a modem that is based on HS PLC in order to examine the performance of the PLC method for practical AMI environments. We then describe measurement methods and linear regression analyses to develop communication models for various environments from the field measurements.