esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of PSO-LSSVM  
### Least Squares Support Vector Machine (LSSVM)  
The computational complexity of the support vector machine (SVM) is closely related to the number of samples. The larger the number of samples, the longer the training time is, and it is increasingly challenging to match the system modeling with the high real-time requirements. Therefore, [PERSON] et al. [24; 25; 26; 27] improved the support vector machine and proposed the least squares support vector machine (LSSVM). The equality constraint is used instead of the inequality constraint as the loss function. The training process is transformed from a quadratic programming problem into a linear equation. The group solves and, at the same time, minimizes the squared error term, reduces the parameters to be determined, simplifies the calculation, improves the convergence speed, and dramatically reduces the complexity of the model [28].  
The least squares support vector machine (LSSVM) is a new type of support vector machine method. The nonlinear function \(\Psi(x)\) (the nuclear function) is used to map samples to high-dimensional feature space, and the nonlinear function estimation problem in the original sample space is transformed. The problem is estimated for linear functions in high-dimensional eigenfunctions.  
Let D = {(xi,yi)} i=1,2,...,n} be the training sample set, where xi\(\in\)Rn is the input data, and yi\(\in\)R is the output data. In the feature space, the LSS  
where \(\omega\) and b are the weight vector and the deviation vector, respectively, \(\delta_{i}\) is the error, and \(\gamma\) is the regularization parameter. The Lagrangian function is constructed according to the above optimization problem.  
\[\begin{cases}\text{M}=\sum_{i=1}^{N}\alpha_{i}\left(\omega^{T}\varphi(x_{i})+b+ \delta_{i}-y_{i}\right)\\ \text{L}(\alpha,\omega,b,\delta)=J(\omega,\delta)+\text{M}\end{cases} \tag{3}\]  
where \(\alpha_{i}\) is a Lagrangian multiplier; the linear equations are obtained by optimizing the above equation.  
\[\begin{bmatrix}0&1^{T}\\ 1&\vartheta+\frac{1}{Y}\end{bmatrix}\begin{bmatrix}b\\ \alpha\end{bmatrix}=\begin{bmatrix}0\\ Y\end{bmatrix} \tag{4}\]  
where \(1=[1,\ldots,1]T\), \(\alpha=[\alpha_{1},\ldots,\alpha_{N}]^{T}\), \(Y=[y_{1},\ldots,y_{N}]^{T}\), and \(\theta_{ij}=k\left(x_{i},x_{j}\right)=\varphi(x_{i})^{T}\varphi(x_{j})\). \(k(x,x_{i})\) is a symmetric positive definite kernel function that satisfies the Mercer condition. After obtaining a and b from Equation (4), the regression function of the LSSVM model is:  
\[y(x)=\sum_{i=1}^{N}\alpha_{i}K(x,x_{i})+b \tag{5}\]
## 3 A Feasible Solution and the Mathematical Formulations
### Mathematical Formulations  
We consider a network composed of switches and controllers (nodes), where these nodes are represented using a graph denoted by \(G=(V,A)\), with \(V\) representing the set of nodes of the network and \(A\) the set of arcs in the network. The connection rules for the networks under study are as follows: switches can only connect to a single controller, and communications between controllers form a single network component that spans the entire graph. The networks are evaluated under different scenarios considering that the number of controllers varies according to the following percentages \(\{20\%,30\%,and\ 40\%\}\) of the total network nodes. In each scenario, the goal is to minimize the latency of connections between switches and controllers relative to the total active connections in the network.  
In order to mathematically represent the aforementioned elements, the following parameters and variables are introduced.
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 2 Materials and Methods  
### Sample Preparation  
For this study, late long-grain nonglutinous rice grown in Zunyi city (Guizhou province, China) was chosen as the experimental sample. The original rice grain moisture was relatively low, since the samples had been sun-dried before experiment. The sample preparation process was as follows. First,the rice grain samples were soaked in water at 40-50 \({}^{\circ}\)C for 10 hours. Second, the samples were placed into a 25 \({}^{\circ}\)C thermostat for 5 hours; thus, the samples were able to absorb the moisture adequately, and the moisture content of samples was the highest at this point. Third, the samples were dried in an oven of 40 \({}^{\circ}\)C. To obtain rice grain of a gradient of moisture level, the drying time was set for 9 gradients, including 0 min, 5 min, 15 min, 30 min, 50 min, 75 min, 105 min, 145 min, and 195 min. For each drying time, ten samples (50 g each) were randomly selected and divided into duplicates (half for true moisture determination and half for NIR spectra acquisition). A total of 90 samples were prepared. Additionally, the true rice grain moisture was determined by the 105 \({}^{\circ}\)C constant weight method from the national standard GB5497-1985 (Determination of moisture content of grain and oil). Rice grain moisture is this paper is expressed as % of dry weight.
# LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis
### Contributions  
To address these limitations, we propose LogRESP-Agent, a modular AI framework that combines LLM-based semantic analysis, context retrieval, and autonomous reasoning. The main contributions of this work are the following:  
**(a) Template-free Semantic Log Interpretation (C1)**  
We design an LLM-based agent capable of directly analyzing unstructured and heterogeneous logs without relying on parsing templates. This enables broad adaptability across formats and mitigates schema drift, addressing Q1.  
**(b) Context-enriched Semantic Reasoning for Explanation (C2)**  
Our framework includes an RAG module that retrieves relevant context--such as historical logs, process hierarchies, threat intelligence, and TTP knowledge--during inference. This allows the agent to enrich its analysis beyond isolated inputs and generate meaningful, explanation-driven outputs that align with real-world investigative needs, addressing Q2.  
**(c) Autonomous and Recursive Threat Investigation (C3)**  
LogRESP-Agent is equipped with a planning-capable LLM that autonomously identifies missing context, sets investigative subgoals, and selects appropriate tools--such as sequence scoring, TTP mapping, or process reconstruction--based on intermediate results. This process is guided by a recursive Thought-Action-Observation (TAO) loop, enabling the agent to iteratively refine its hypotheses and adapt its reasoning to evolving log evidence, directly addressing Q3.
# Characterization of RNA in Extracellular Vesicles
## 2 Isolation and Characterization of EVs
Atomic force microscopy is used to further study EV morphology, whereby a mechanical cantilever is passed over a surface, and the presence of surface structures, such as surface-bound particles, can be detected [46]. Furthermore, nanoparticle tracking analysis is useful to determine the concentration and size of vesicles by using a laser beam, which is scattered by particles in the sample [47]. Further characterization of EVs relies on different protein components of different types of EVs and includes biochemical methods such as immunoblotting, Western blot analysis, proteomic analysis, or imaging techniques. For example, exosomes contain endosome-associated proteins, including tetraspannins such as CD9, CD63, CD81, or CD82, which have been used to identify these vesicles [48,49]. However, new data indicate that some proteins regarded as exosome markers (e.g., heat-shock proteins, flotillins, and major histocompatibility complex) are present in all different EV types, and additionally, a number of subpopulations, especially of exosomes, with diverse morphology and distinct biological functions, have been described [50, 51, 52, 53, 54]. In summary, most of the strategies used for the isolation of EVs are still under development. Fractions obtained after differential centrifugation steps always contain proteins characteristic of other fractions [43]. Furthermore, EVs are rapidly cleared from peripheral blood, and thus strategies need to be developed to stabilize them [55]. The number of EVs isolated from plasma is also dependent on the type of anticoagulant used for the generation of plasma samples [4,42]. Lipoproteins, microbes, microsomes, or protein aggregates may be coppurified in vesicle preparations.
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 6 Discussion of the Obtained Results
### Speed Comparison of EB-REP and Existing FRR Mechanisms
#### 6.1.2 Simulation Comparison  
We also performed simulation testing for comparison of the proposed EB-REP against LFA, R-LFA, and EM-REP mechanisms. We focused on the comparison of rerouting time.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **EB-REP** & **EM-REP** & **D-LFA** & **LFA** & **MRC** & **MRT** &
\begin{tabular}{c} **Not-Via** \\ **Addresses** \\ \end{tabular} & **R-LFA** & **TI-LFA** \\ \hline
100\% Repair Coverage & Yes & Yes & Yes & No & Yes & Yes & No & Yes \\ Custom Alternative Path & Yes & No & Yes & No & Yes & No & No & No \\ Precomputing & Yes & No & Yes & Yes & Yes & Yes & Yes & Yes \\ Packet Modification & Yes & Yes & Yes & No & Yes & Yes & Yes & Yes \\ Link-State dependency & Yes & No & Yes & No & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of EB-REP with other FRR solutions.  
In these simulations, we used our OMNeT++ implementation of the BFD protocol [75]. For comparison purposes, we used ten various topologies and results were averaged over eight simulation runs.  
The BFD echo interval was set on R1 to 0.010 sims. The BFD protocol will declare the link as down in the case when three BFD responses have not arrived. When a specific node fails at time 50 sims, the BFD protocol running on R1 will then report this at time 50.031 sims. By receiving this system message, the FRR mechanism will be activated and will perform the rerouting process.  
We measured the time from the occurrence of a node failure to the time when the source router sends rerouted data to an alternative next-hop; this means that we measured the detection time plus installation of FRR path plus forwarding time. The measured results of reroute process are shown in Table 14.  
The results show that the rerouting process of a specific FRR mechanism depends mainly on the failure detection time (0.030 sims), and that the rerouting process time of a specific FRR mechanism is negligible. FRR alternative routes are calculated in advance, and therefore they are ready to install immediately. Installation of an FRR alternative path takes only a minimal part of the whole rerouting process.  
According to obtained results from the OMNeT++ simulation, we could confirm the theoretical comparison that the EB-REP mechanism in terms of speed is comparable with these two FRR solutions.
## 3 Results
### Crp
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Cross-Correlation**} & \multicolumn{2}{c}{**RMS Differences**} \\ \hline \multirow{3}{*}{**Joint Pairs**} & \multirow{3}{*}{**Phase**} & **Amputated** & **Non-Amputated** & **Amputated** & **Non-Amputated** \\  & & **Limb versus** & **Limb versus** & **Limb versus** & **Limb versus** \\  & & **Control** & **Control** & **Control** & **Control** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Hip–knee \\ Swing \\ \end{tabular} } & Stance & 0.79 (\(<\)0.001) & 0.89 (\(<\)0.001) & \(-\)3.5\% (0.41) & 3.2\% (0.93) \\  & Swing & 0.79 (\(<\)0.001) & 0.93 (\(<\)0.001) & \(-\)5.4\% (0.20) M & \(-\)1.7\% (0.90) M \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Knee–ankle \\ Swing \\ \end{tabular} } & Stance & \(-\)0.05 (0.61) & 0.78 (\(<\)0.001) & 21.2\% (0.03) & 9.3\% (0.54) \\  & Swing & 0.23 (0.02) & 0.89 (\(<\)0.001) & 5.0\% (0.70) & 18.6\% (0.01) \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Hip–ankle \\ Swing \\ \end{tabular} } & Stance & 0.296 (0.003) & 0.81 (\(<\)0.001) & 20.7\% (0.04) & 13.5\% (0.15) \\  & Swing & 0.66 (\(<\)0.001) & 0.88 (\(<\)0.001) & 21.1\% (0.20) M & 13.8\% (0.20) M \\ \hline \multicolumn{6}{l}{Brackets refer to significance level; M} \\ \multicolumn{6}{l}{data non-normality.} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cross-correlation coefficients and root-mean-square (RMS) differences between the amputees and able-bodied controls.
# Audio Steganalysis Estimation with the Goertzel Algorithm
###### Abstract  
Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. In this work, the use of the Goertzel algorithm as a steganalyzer in the frequency domain is combined with the proposed sliding window adaptation to allow the analyzed audio vectors to be compared, enabling the differences between the vectors to be identified. We then apply linear prediction to the vectors to detect any modifications in the acoustic signatures. The implemented Goertzel algorithm is computationally less complex than other proposed stegoanalyzers based on convolutional neural networks or other types of classifiers of lower complexity, such as support vector machines (SVD). These methods previously required an extensive audio database to train the network, and thus detect possible stegoaudio through the matches they find. Unlike the proposed Goertzel algorithm, which works individually with the audio vector in question, it locates the difference in tone and generates an alert for the possible stegoaudio. In this work, we apply the classic Goertzel algorithm to detect frequencies that have possibly been modified by insertions or alterations of the audio vectors. The final vectors are plotted to visualize the alteration zones. The obtained results are evaluated qualitatively and quantitatively. To perform a double check of the fingerprint of the audio vectors, we obtain a linear prediction error to establish the percentage of statistical dependence between the processed audio signals. To validate the proposed method, we evaluate the audio quality metrics (AQMs) of the obtained result. Finally, we implement the stegoanalyzer oriented to AQMs to corroborate the obtained results. From the results obtained for the performance of the proposed stegoanalyzer, we demonstrate that we have a success rate of 100%.  
Goertzel's algorithm; audio quality metrics; audio signals; stegoanalyzer; linear prediction 22 May 202422 June 20242 June 20242 June 20242 June 20242
## 1 Introduction
In view of the current research, when the frequency component of the narrowband interference signal is relatively single and its frequency band is far from the PD signal, the existing method works well. However, the frequency of the narrowband interference in the field is numerous and random, it is challenging to filter out all narrowband interference without energy loss and waveform distortion, especially mixed with white noise in practice. A novel method to exact real PD signal from complex noisy signal interfered by periodic narrowband noise and white noise is proposed in this paper. The contributions of this paper are summarized as follows. First, to deal with complex noises with different characteristics, a novel two-step de-noising framework is proposed in which each noise is rejected respectively by appropriate algorithm according to their specific advantages. Second, we present an improved adaptive VMD to remove out periodic narrowband interference. VMD can decompose an input signal into an ensemble of band-limited intrinsic mode functions (BLIMFs) with the minimal sum of estimated bandwidths for each mode. We employ its instinctive advantage to separate narrowband noise from the input signal. To avoid over-decomposition or mode loss, the correlation coefficient method is used to determine the optimal intrinsic mode number adaptively, and components containing partial discharge signals are synthesized according to the energy spectrum characteristics of noise and partial discharge signals. Third, in order to effectively filter out white noise, a scale adaptive wavelet packet decomposition method based on correlation coefficient is proposed, which decomposes the signal into high, middle,and low-frequency components. The components with low frequency, small amplitude are removed out as the white noise interference according to the threshold method, and the residual is the de-noising PD signal. The paper is organized as follows. Section 2 presents the whole methodology to deal with complex noises, in which Section 2.1 formulates the problem of multi-sensor travelling wave based localization, Section 2.2 describes the principle of VMD and proposes an improved fast adaptive implementation, Section 2.3 presents a scale adaptive wavelet packet decomposition algorithm, Section 2.4 gives the complete de-noising method flow for complex noisy PD signal. Section 3 presents the simulations results and its discussion, while Section 4 concludes the paper.
## 5 Conclusions  
The results of this study reveal that treatment with an atmospheric-pressure plasma jet (APPJ), particularly LP15, has a significant impact on the colorimetric parameters, textile characteristics, and medulla properties of dyed wool samples. The LP15 treatment reduces lightness (L) to 52.10 \(\pm\) 0.82, darkening the fiber, and presents the highest chromaticity values a* (22.91 \(\pm\) 1.18) and b* (15.81 \(\pm\) 0.45), enhancing the reddish and yellowish tones. Compared to mordant-dyed fibers (LM0) and white fibers (LB), LP15 produced more intense colors.  
Similarly, plasma treatments (LP5, LP10, and LP15) significantly improved the mean fiber diameter (MFD) and spinning fineness (SF), with LP15 being the most efficient treatment. This treatment also increased the curvature index (CI) and comfort factor (CF), suggesting an overall improvement in the properties of the treated fibers. The LM0 and LB samples showed the highest percentages of medulllated fibers (TMED) with 99.13 \(\pm\) 0.23% and 98.59 \(\pm\) 0.18%, respectively, while the LP10 sample had a lower percentage of 97.34 \(\pm\) 1.09%. Additionally, LP10 showed the highest percentage of fragmented medulla (FMED) with 2.64 \(\pm\) 1.04%, and LP15 stood out with the highest percentage of continuous medulation (CMED) with 1.48 \(\pm\) 0.02%, indicating the influence of plasma on the colorimetric parameters and medulla properties of the fibers.  
These findings suggest that the APPJ is an effective technology for improving the textile quality of wool and reducing reliance on traditional methods that employ toxic chemicals. This has important implications for sustainability and economic impact in the textile industry, as it allows for higher-quality products with lower environmental impact and potential cost savings in production.  
Conceptualization, A.Q.-Q. and F.L.; methodology, V.M.-M., J.Q.-M. and A.Q.-Q.; software, A.Q.-Q. and F.L.; validation, J.Q.-M., V.M.-M. and A.Q.-Q.; formal analysis, J.Q.-M., F.L. and A.Q.-Q.; investigation, J.Q.-M., V.M.-M., A.Q.-Q. and F.L.; data curation, A.Q.-Q. and F.L; writing--original draft preparation, A.Q.-Q. and F.L; writing--review and editing, A.Q.-Q., A.Q.-Q. and F.L; supervision, A.Q.-Q.; project administration, A.Q.-Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Vicerrectorado de Investigacion de la Universidad Nacional Micaela Bastidas de Apurimac, Peru.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest that could inappropriately influence their work. They have no financial or personal relationships with other individuals or organizations that may present conflicts of interest. This work is not related to any employment, consultancies, stock ownership, honorary factors, paid expert testimony, patent applications/registrations, funding, or grants.
###### Abstract  
In view of the low-energy explosion foil detonation system's requirements for the integration technology of high-voltage switches and technical overload resistance technology, a magnetron sputtering coater is used to sputter copper film on the surface of the substrate. The thickness is 4.0 \(\upmu\)m, the radius of the main electrode is 4 mm, the trigger electrode is 0.6 mm and 0.8 mm, and the main gaps are 0.8 mm, 1.0 mm, 1.2 mm mm, 1.8 mm, 2.0 mm, 2.2 mm, and 2.6 mm. Copper foil three-electrode planar spark gap high voltage switches are designed and manufactured; and the static self-breakdown characteristics, dynamic operating characteristics, and discharge life characteristics of the three-electrode planar spark gap high voltage switch based on copper foil are studied in this paper. The test results show that with the increase of the main electrode gap from 0.8 mm to 2.6 mm, the self-breakdown voltage of the planar spark gap switch increases, and the working voltage also increases. When the main electrode gap is a maximum of 2.6 mm, the self-breakdown voltage of the switch can reach 3480 V, which indicates that the maximum operating voltage of the switch is 3480 V. When the charging voltage is 2.0 kV, with the increase of the main electrode gap from 0.8 mm to 2.6 mm, the minimum trigger voltage value of the planar spark gap switch increases from 677 V to 1783 V (\(a\) = 0.6 mm), and from 685 V to 1766 V (\(a\) = 0.8 mm), the switch on time is 16 ns, 22 ns, 28 ns, 48 ns, 64 ns, 77 ns, 93 ns (\(a\) = 0.6 mm), and 26 ns, 34 ns, 51 ns, 67 ns, 81 ns, 102 ns (\(a\) = 0.6 mm). With the increase of the gap between the two main electrodes of the switch, the maximum static working voltage of the three-electrode plane spark gap high-voltage switch increases, the minimum trigger voltage value also increases, and the on-time of the switch gradually becomes longer. The peak current of the discharge circuit decreases and the dynamic impedance and inductive reactance of the switch also increase; as the width of the trigger electrode increases, the minimum trigger voltage decreases, the dynamic impedance and inductance decrease, and the switch operating voltage with the same parameters is higher. The easier the switch is to turn on, the lower the minimum trigger voltage. The electrode thickness of the three-electrode plane spark gap switch has a certain influence on the field strength and the service life of the switch. The results of this study provide useful references for promoting the research and development of LEEFIs.  
three-electrode planar spark gap high voltage switch; static characteristics; dynamic characteristics; impedance characteristics; electric field strength +
Footnote †: journal: Applied Sciences