esa-sceva/satcom-chunk-collection huggingface dataset:
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 4 Experiments
### Few-Shot Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Labeled Data** & \multicolumn{4}{c}{**10\%**} & \multicolumn{4}{c}{**40\%**} & \multicolumn{4}{c}{**80\%**} \\
**Data Size** & \multicolumn{4}{c}{**10\%**} & \multicolumn{4}{c}{**40\%**} & \multicolumn{4}{c}{**80\%**} \\ \hline
**Method** & **AC** & **PR** & **RC** & **F1** & **AC** & **PR** & **RC** & **F1** & **AC** & **PR** & **RC** & **F1** \\ \hline MT-FBERT & 0.9987 & 0.9992 & 0.9989 & 0.9987 & 0.9992 & 0.9992 & 0.9992 & 0.9998 & 0.9954 & 0.9961 & 0.9985 \\ FlowPrint & 0.1455 & **1.0000** \({}^{*}\) & 0.1132 & 0.2034 & 0.9123 & 0.9630 & 0.9455 & 0.9541 & 0.9667 & 0.9667 & **1.0000** & 0.9831 \\ TFE-GNN & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ YaTC & 0.9996 & 0.9815 & 0.9808 & 0.9808 & 0.9996 & 0.9985 & 0.9985 & 0.9985 & 0.9996 & 0.9960 & 0.9960 & 0.9960 \\ \hline \hline \end{tabular} \({}^{*}\) A number in bold means that the corresponding method performs better than its counterpart does, for the same metric and the same labeled sample size.  
\end{table} 
Table 7: Comparison results on IoT-22 dataset using different labeled sample sizes. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Labeled** & \multicolumn{4}{c}{**10\%**} & \multicolumn{4}{c}{**40\%**} & \multicolumn{4}{c}{**80\%**} \\
**Data Size** & \multicolumn{4}{c}{**10\%**} & \multicolumn{4}{c}{**40\%**} & \multicolumn{4}{c}{**80\%**} \\ \hline
**Method** & **AC** & **PR** & **RC** & **F1** & **AC** & **PR** & **RC** & **F1** & **AC** & **PR** & **RC** & **F1** \\ \hline MT-FBERT & 0.9990 & 0.9932 & 0.9927 & 0.9975 & 0.9995 & 0.9903 & 0.9903 & 0.9947 & 0.9995 & 0.9991 & 0.9991 & 0.9991 \\ FlowPrint & 0.5333 & 0.2708 & **1.0000**\({}^{*}\) & 0.4262 & 0.8429 & 0.5556 & 0.7692 & 0.6452 & 0.8197 & 0.5500 & 0.8462 & 0.6667 \\ TFE-GNN & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ YaTC & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ YaTC & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ \hline \hline \end{tabular} \({}^{*}\) A number in bold means that the corresponding method performs better than its counterpart does, for the same metric and the same labeled sample size.  
\end{table} 
Table 5: Comparison results on USTC-TFC2016 dataset using different labeled sample sizes.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 2 Materials and methods
### Methods
#### 2.3.4 Hurst Exponent and R/S Analysis  
The Hurst exponent and R/S analysis have developed a fractal theory for studying time series and have been widely used in the research of urban environmental change, climate change, population, and economic development [67; 68]. R/S analysis can be used to predict future trends in vegetation cover, wherein the main principle is to construct a time series that defines an average series \(\left\{\xi(t),t=1,2\cdots\right\}\), for any positive integer \(\tau\geq 1\). First, the mean sequence must be defined:  
\[\left\langle\xi\right\rangle_{\tau}=\frac{1}{\tau}\sum_{t=1}^{\tau}\xi(t)\tau= 1,2\cdots \tag{8}\]  
Cumulative deviation:  
\[X(t,\tau)=\sum_{\mu=1}^{t}(\xi(\mu)-\left\langle\xi\right\rangle_{\tau})1\leq t \geq\tau \tag{9}\]  
The range:  
\[R(\tau)=\underset{1\leq t\geq\tau}{max}X(t,\tau)-\underset{1\leq t\geq\tau}{ min}X(t,\tau)\tau=1,2\cdots \tag{10}\]  
Standard deviation:  
\[S(\tau)=\left[\frac{1}{\tau}\sum_{t=1}^{\tau}(\xi(t)-\left\langle\xi\right\rangle _{\tau})^{2}\right]^{\frac{1}{2}} \tag{11}\]  
For \(R(\tau)/S(\tau)\overset{\Delta}{=}R/S\), if there is a relationship as follows:  
\[R/S\propto\tau^{H} \tag{12}\]  
where \(H\) is the Hurst index. According to the value of \((\tau,R/S)\), \(H\) can be obtained using least squares fitting (Equation (8)) in a double logarithmic coordinate system. The \(H\) value provides information on whether the sequence is completely random or if it follows a trend. Furthermore, trends can be expressed as persistent or anti-persistent. Overall, \(H\)= 0.5 indicates that vegetation change is random, \(0.5<H<1\) indicates that vegetation change is sustainable, and \(0<H<0.5\) indicates that future vegetation trend is expected to be the opposite of that in the past.
## 3 Metabolic Responses to Different Antibiotic Classes
### Polymyxins and Phospholipids  
Polymyxins (e.g., colistin) bind to lipopolysaccharidesaccharides on the outer membrane of Gram-negative bacteria, causing lipid disorganization [57; 58].  
In response to this selective pressure, several Gram-negative pathogens activate two-component regulatory systems (PmrA/PmrB and PhoP/PhoQ), which promote Lipid A modification through the addition of cationic groups such as 4-amino-4-deoxy-L-arabinose (L-Ara4N) and phosphoethanolamine (pEtN), catalyzed by the enzymes ArnT and EptA, respectively [59].  
These modifications reduce the net negative charge of the outer membrane, hindering electrostatic interactions with polymyxins.  
Concomitantly, alterations in membrane lipid composition have been observed, including a reduction in unsaturated fatty acids and a restructuring of biosynthetic pathways involving acetyl-CoA, the glycolate pathway, and acyl carrier proteins (ACPs), suggesting a metabolic rewiring aimed at supporting lipid plasticity and antimicrobial tolerance [60].  
In _Klebsiella pneumoniae_ infections, resistance to polymyxins--particularly colistin--is mediated by a wide array of genetic adaptations that converge on altering outer membrane lipid structure and modulating two-component response systems. In particular, modifications of Lipid A through the addition of L-Ara4N or pEtN reduce antibiotic binding affinity [61; 62].  
These changes are frequently associated with inactivating mutations or insertions in the _mgrB_ gene, as well as alterations in the regulatory systems PhoPQ, PmrAB, and, less commonly, CrrAB, leading to overexpression of the _pmrHFIJKLM_ operon and the _phoP_, _phoQ_, _pmrK_, and _pmrC_ genes [61; 62; 63].  
In numerous clinical cohorts, the emergence of resistance has been more frequently associated with selective pressure from prior polymyxin exposure than with human-to-human transmission of resistant clones and is often linked to high MIC values (\(\geq\)64 ug/mL) and high-risk epidemic clones such as ST11 and ST258 [62; 63].  
Notably, the insertion of mobile elements such as ISkpn14 into the _mgrB_ gene represents one of the predominant mechanisms of gene inactivation in resistant strains isolated in China [61; 62; 63].
# Automotive Seat Comfort and Vibration Performance Evaluation in Dynamic Settings
## 2 Materials and Methods
### Laboratory Evaluation
#### 2.2.3 Laboratory Vibration Exposures  
To expose the participants to the field-measured and sinusoidal vibration, a 6-degree-of-freedom motion platform (MB-E-6 DOF/24/1800 KG; Moog Inc.; East Aurora, NY, USA) was used.  
For the field-measure vibration exposures, tri-axial vibration was collected at 1280 Hz using a data recorder (DA-40; Rion Co. LTD; Tokyo, Japan) with a tri-axial accelerometer (352C33; PCB Piezotronics; Depew, NY) magnetically mounted to the floor below the driver's seat of a sedan (2015 Hyundai Sonata) and an SUV (2019 GMC Yukon XL). To obtain representative vibration exposure input, a sedan and a SUV were chosen as they account for the largest proportion of passenger car markers. Tri-axial vibration was measured while the vehicles were going over city streets, freeway, a cobblestone road, speed bumps, speed humps, and expansion joints on a freeway. The field-measured vibration was iteratively brick wall filtered and converted to displacement data by integration. Detailed information on the raw data processing and conversion is available in our previous studies [31; 32; 33]. Our validation studies have shown that the mean RMS errors between the field-measured vibration and the vibration output from the motion platform were less than 1% [31].  
For the sinusoidal vibration exposures, continuous sine sweeps (1-30 Hz) with peak amplitudes of \(\pm\) 0.5, 1.0 and 1.5 m/s\({}^{2}\) in each of the X, Y, and \(Z\) axes were created using a motion platform control software program (Moog Inc.; East Aurora; NY). The frequency range was determined based on previous findings that most of the WBV energy content is below 30 Hz; the amplitudes were chosen to encompass the daily vibration action (0.5 m/s\({}^{2}\)) and exposure limit values (1.15 m/s\({}^{2}\)) included in the the European Union (EU) Vibration Directive [34].
## 5 Case-Study: Resource Fitting
### Experiment 3: Performance of ML Backends under Constrained Resources
#### 5.3.1 Resource Configurations for Containerized Services  
Figure 9 presents the results of studying the round duration and average CPU utilization for different resource reservation policies. These policies represent various scenarios that a user may encounter in an FL deployment:  
* _Native:_ This configuration serves as the baseline, where no resource capping policies are applied. Because this represents what a non-expert user might consider first, we use it interchangeably with the term \"Native performance\".
* _Extreme over-provisioning:_ Users aim for optimal training time without much concern for resource spending. This involves reserving more CPUs than what the ML frameworks require. For our setup, this configuration entails reserving 16 CPUs.
* _Best Resource Fitting:_ This configuration reserves the number of CPUs that the ML framework can fully utilize without significantly extending the training time. In our setup, this is eight CPUs.
* _Resource Constrained Environments:_ This configuration examines how FL frameworks perform in execution environments with limited resources. This can be motivated by cost reduction or resource-constrained edge devices (like single-board computers). The available CPUs for the client are limited to four or two.  
We note that in all configurations, the accuracy remains consistent (as shown in Figure 5a) and thus, we omit the respective plots.  
Starting from the Native and Extreme Over-Provisioning configurations, we observe that the FL workflow only utilizes nine CPUs, demonstrating that the training time matches the native performance in both cases (left plot). However, for Extreme Over-Provisioning, six CPUs remain unused (right plot). This highlights that FL/ML systems may not fully utilize available resources, leading to unnecessary resource spending. Next, we examine the Best Resource Fitting scenario. Compared to Extreme Over-Provisioning, this configuration reduces the number of required resources by 50% (from 16 to 8) but with a modest 22% increase in training time. Therefore, _it becomes crucial to develop a Kubernetes scheduling mechanism that dynamically adjusts the capping to match the native value of ML frameworks._Now, for Resource Constrained environments, there is a trade-off between increased training time and reduced processing power. Reserving four CPUs increases the training time by a factor of three (from 100 s to 300 s) but reduces the CPU allocation by a factor of eight compared to the native performance (from 24 to 4). In the extreme case of two CPUs, the training duration is doubled compared to four CPUs and is six times higher than the native performance. This indicates that _the training time increases inversely exponential to the available processing power_.
## 5 Experiment Evaluation
### Playback Delay  
In a P2P-based VoD system, the data-prefetching strategy is proposed to minimize the latency that the user must wait after the VCR operation. Therefore, the playback delay is one of the most important indicators to evaluate the validity of a data-prefetching strategy. For identical network bandwidths, a smaller delay indicates a better data-prefetching strategy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ranking** & **Scheme Num** & **Anchor Interval** & **Ranking** & **Scheme Num** & **Anchor Interval** \\ \hline
1 & Scheme 5 & 2.5 min & 11 & Scheme 14 & 7 min \\
2 & Scheme 6 & 3 min & 12 & Scheme 15 & 7.5 min \\
3 & Scheme 7 & 3.5 min & 13 & Scheme 16 & 8 min \\
4 & Scheme 4 & 2 min & 14 & Scheme 17 & 8.5 min \\
5 & Scheme 8 & 4 min & 15 & Scheme 18 & 9 min \\
6 & Scheme 9 & 4.5 min & 16 & Scheme 19 & 9.5 min \\
7 & Scheme 10 & 5 min & 17 & Scheme 20 & 10 min \\
8 & Scheme 11 & 5.5 min & 18 & Scheme 3 & 1.5 min \\
9 & Scheme 12 & 6 min & 19 & Scheme 2 & 1 min \\
10 & Scheme 13 & 6.5 min & 20 & Scheme 1 & 0.5 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ranking results for 20 candidate schemes.  
As shown in Figure 6, when the network bandwidth is small, the DSA strategy requires the highest average delay, but it is only 0.5 s more than the other two strategies. With the increase in network bandwidth, the DSA strategy has the fastest performance improvement. When the network bandwidth increases to approximately 6 M, the average delay of the DSA strategy is lower than that of the API strategy, and almost equal to the anchor-prefetching strategy, which has the minimal value.
## 3 Methodology for Dynamic Risk Evaluation and Early Warning of Crest Cracking
### Computation of Crest Cracking Probability and Estimation of Cracking Time
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & \(\mathbf{\phi}\) (\({}^{\circ}\)) & \(\mathbf{\Delta\phi}\) (\({}^{\circ}\)) & \(\mathbf{c}\) (MPa) & \(\mathbf{R_{f}}\) & \(\mathbf{K_{e}}\) & \(\mathbf{K_{b}}\) & \(\mathbf{n}\) & \(\mathbf{m}\) \\ \hline Main rockfills & 44.4 & 4.9 & 0 & 0.7 & 1350 & 668.35 & 0.2 & 0.22 \\ Secondary rockfills & 42.1 & 3.7 & 0 & 0.7 & 796 & 421.34 & 0.2 & 0.22 \\ Gravelly soil core Alluvial deposits & 35.0 & 0 & 6 & 0.8 & 384 & 207.70 & 0.4 & 0.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calibrated parameters of Duncan and Chang’s E-B model for the materials in dam and foundation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & \(\mathbf{\phi}\)/\(10^{-2}\) & \(\mathbf{b_{f}}\)/\(10^{-4}\) & \(\mathbf{c_{f}}\)/\(10^{-4}\) & \(\mathbf{d_{f}}\)/\(10^{-3}\) & \(\mathbf{m_{1}}\) & \(\mathbf{m_{2}}\) & \(\mathbf{m_{3}}\) \\ \hline Main rockfills & 0.986 & 0.518 & 0.518 & 1.121 & 0.514 & 0.416 & 0.427 \\ Secondary rockfills & 0.637 & 0.572 & 0.572 & 2.465 & 0.848 & 0.455 & 0.542 \\ Gravelly soil core Alluvial deposits & 0.632 & 1.004 & 1.004 & 3.014 & 0.996 & 0.679 & 0.518 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calibrated parameters of the creep deformation model for the materials in dam and foundation. Figure 7: Procedure of the back analysis for the Pubugou Dam. Figure 8: _Cont._  
The time series of crest settlement inclination around dam axis is derived from the computed crest settlements and is presented in Figure 11. It is shown that the crest settlement inclination is closely related to the reservoir level, i.e., when the reservoir level rises, the crest settlement inclination increases correspondingly, and vice versa. The maximum settlement inclination is 0.98% and occurs in the first period when the reservoir level keeps around normal water level. It is also shown in Figure 10 that crest cracking happens with higher values of settlement inclination. However, crest cracking is not associated with a specific value of settlement inclination. In addition, the time intervals from  
Figure 8: The measured and computed time series of displacements of the monitoring points. Figure 10: The contour of horizontal displacement of the dam when the reservoir level first reaches the normal water level. Figure 9: The contour of settlement displacement of the dam when the reservoir level first reaches the normal water level. the emergence of abnormal state (i.e., \(\gamma_{S}>\gamma_{C}\)) until the occurrence of crest cracking is of obvious random nature.
###### Abstract  
This article describes the design and validation of deployable low-power probes and sensors to investigate the influence of the ionosphere and the Earth's magnetic field on radio wave propagation below the plasma frequency of the ionosphere, known as Near Vertical Incidence Skywave (NVIS) propagation. The propagation of waves that are bent downward by the ionosphere is dominated by a bi-refractive mechanism called'magneto-ionic propagation'. The polarization of both downward waves depends on the spatial angle between the Earth's magnetic field and the direction of propagation of the radio wave. The probes and sensors described in this article are needed to simultaneously investigate signal fading and polarization dynamics on six radio wave propagation paths. The 1-Watt probes realize a 57 dB signal-to-noise ratio. The probe polarization is controlled using direct digital synthesis and the cross-polarization is 25-35 dB. The intermodulation-free dynamic range of the sensor exceeds 100 dB. Measurement speed is 3000 samples/second. This publication covers design, practical realization and deployment issues. Research performed with these devices will be shared in subsequent publications.  
deployable; magneto-ionic; magnetic field; polarization; fading; ionosphere; radio wave propagation; Near Vertical Incidence Skywave (NVIS); circular polarization +
Footnote †: journal: Sensors
# Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol
## 4 Architecture and Implementation
### Parameter Generation  
We assume that the elliptic curve parameters are created and deployed before any communication takes place. These parameters are the base point of the curve and the order of the curve. This is a prerequisite for the implementation of our key distribution algorithm. The parameters are stored locally in each device and before initiating communication, each device uses them to generate its own keys.  
For the rest of the operations, a time sequence diagram is provided in Figure 4.
## 1 Introduction
Recently, inspired by wavelet transform and empirical mode decomposition (EMD) [27], the empirical wavelet transform (EWT) has been developed by [PERSON] [28; 29]. [PERSON] et al. [30] employed EWT to estimate both natural frequencies and damping ratios of large civil structures. [PERSON] and [PERSON] [31] used EWT to separate the closely-spaced natural frequencies of structures. The main idea of EWT is to determine the Fourier segments and then design a series of wavelet filters to decompose the signal into several sub-signals. EWT became adaptive when the Fourier segments were automatically determined. The empirical rule was to find all the local maxima of the spectrum and then use the center of the two adjacent local maxima as the boundaries of Fourier segments [32]. Some researchers have proposed solutions to modify the Fourier segments. [PERSON] et al. [33] proposed a method of adaptive parameterless EWT to fulfill an adaptive separation of the Fourier spectrum. [PERSON] et al. [34] developed the sparsity-guided EWT to automatically establish Fourier segments. [PERSON] et al. [35] employed the scale-space histogram segmentation to determine the boundaries adaptively. However, for damage AE signals, when strong noise or frequency dispersion exist in the Fourier spectrum of the signals, such Fourier segmentation solutions are not always reliable and useful. In this paper, local window maxima (LWM) algorithm is proposed to automatically determine the boundaries of Fourier segments, and it is appropriate for EWT to analyze the damage AE signals of composite structures. The key ideas of the algorithm are to search all the local maxima of the Fourier spectrum in an optimal window and then to obtain the segmentation boundaries. Since different damage mechanisms for composite structures have different frequency ranges, separating the different damage mechanisms is equivalent to decomposing the AE signals in the Fourier spectrum. The analysis results on a simulation signal show that this method can effectively obtain the peak frequency bands. In addition, the pencil-lead breaks (PLB) and three-point bending experiments on the composite structure is carried out to prove the applicability of the proposed solution.
# Optical Response of an Interacting Polaron Gas in Strongly Polar Crystals
## Appendix A Analytic Model for the Conductivity Band in SrTiO\({}_{3}\)  
For the calculation of the many-polaron response, it is useful to simulate numerical data for the band structure by an analytic expression. Here, we treat the tight-binding expression similarly to Refs. [2; 13]. In these works, an analytic parametrization of the ab initio band structure is introduced using the matrix Hamiltonian:  
\[H=4\left(\begin{array}{ccc}\varepsilon_{1}\left(\mathbf{k}\right)&0&0\\ 0&\varepsilon_{2}\left(\mathbf{k}\right)&0\\ 0&0&\varepsilon_{3}\left(\mathbf{k}\right)\end{array}\right)+\frac{1}{2}W, \tag{10}\]  
with the energies which enter the diagonal part of this matrix Hamiltonian,  
\[\varepsilon_{1} =t_{\delta}\sin^{2}\left(\frac{a_{0}k_{x}}{2}\right)+t_{\pi}\sin ^{2}\left(\frac{a_{0}k_{y}}{2}\right)+t_{\pi}\sin^{2}\left(\frac{a_{0}k_{z}}{ 2}\right), \tag{11}\] \[\varepsilon_{2} =t_{\pi}\sin^{2}\left(\frac{a_{0}k_{x}}{2}\right)+t_{\delta}\sin ^{2}\left(\frac{a_{0}k_{y}}{2}\right)+t_{\pi}\sin^{2}\left(\frac{a_{0}k_{z}}{ 2}\right),\] \[\varepsilon_{3} =t_{\pi}\sin^{2}\left(\frac{a_{0}k_{x}}{2}\right)+t_{\pi}\sin^{2} \left(\frac{a_{0}k_{y}}{2}\right)+t_{\delta}\sin^{2}\left(\frac{a_{0}k_{z}}{ 2}\right),\]where \(a_{0}\) is the lattice constant. The matrix \(W\) describes the mixing of subbands within the conductivity band, which comes from the spin-orbit coupling in first-principle calculations. For the cubic phase of SrTiO\({}_{3}\), counting the band energy from the \(G\) point (i.e., dropping a uniform shift of the whole band), \(W\) is given by:  
\[W=\left(\begin{array}{ccc}0&\xi&\xi\\ \xi&0&\xi\\ \xi&\xi&0\end{array}\right). \tag{10}\]  
For the tetragonal phase as reported in Ref. [36], the matrix \(W\) is:  
\[W=\left(\begin{array}{ccc}2D&\xi&\xi\\ \xi&2D&\xi\\ \xi&\xi&-4D\end{array}\right). \tag{11}\]  
Here, the parameter \(D\) at the main diagonal of \(W\) describes a uniform shift of each subband due to the spin-orbit coupling, while the parameter \(\xi\) is responsible for mixing between different subbands. The values of parameters entering the analytic parametrization are determined from the numeric fit to the ab initio band structure. They are described in Section 4.