esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Development of Optimal Dust Suppressant Formula
#### 3.1.1 Performance Requirement and Formula Composition of Dust Suppressant
The curves of viscosity changes in the coagulant solutions with concentration are shown in Figure 3a, and the mass proportion of dust in large particles is shown in Figure 3b. In can be seen that the viscosity values of NB4 and NB1 solutions are relatively large, seldom show changes with concentration, and are easy to condense and process. The order of the bonding effect of coagulants is NBS > NB2 > NB3 > NB4 > NB1, and NB5 shows the best bonding effect of the three concentration gradients. NB5 was selected as a coagulant, and its concentration range was set from 0.06% to 0.12% in combination with the experimental conclusion of viscosity values. Sodium dodecyl benzene sulfonate (BZ1), sodium dodecyl sulfate (BZ2), phospholipid (BZ3), and sodium lignocellulosic (BZ4) were preliminarily selected as raw materials for the surfactant in this monomer experiment. The surface tension values of different concentrations of surfactant solutions were measured (Figure 3c) and the infiltration time of dust on the surfactants was observed through an infiltration experiment device (Figure 3d). According to the experiment, surfactants BZ2 and BZ4 demonstrated small surface tension values at the same concentration, and BZ2 demonstrated the shortest forward and reverse  
Figure 1: (**a–d**) Curves of water loss rate of humectants with concentrations of 2%, 4%, 6%, and 8% at 65 \({}^{\circ}\)C, respectively; (**e–h**) Curves of water loss rate of humectants with concentrations of 2%, 4%, 6%, and 8% under natural conditions, respectively. Figure 2: (**a–d**) Variation curves of moisture content rate of hygroscopic agent monomers with concentrations of 6%, 8%, 10%, and 12%, respectively. infiltration time. This shows that BZ2 has the best infiltration performance. Therefore, BZ2 was selected as the surfactant, and its concentration range was set from 0.06% to 0.12%.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 4 Countermeasures against Covert Channel Attacks
### Shielding  
One type of countermeasure for physical covert channels is adding shielding to attempt to block the transmission medium chosen. For example, Faraday cages are a common proposed countermeasure for electromagnetic covert channels in order to attenuate the signal. TEMPEST is a shielding standard developed by NATO and the National Security Agency (NSA) that requires systems to be protected with \"a 100 dB insertion loss from the frequencies of 1 KHz to 10 GHz\" [56]. However, note that there are techniques that are able to establish covert channels, despite various types of shielding, by manipulating the shape of the frequency spectrum [57] or focusing on the lower end of the frequency spectrum [36]. While shielding does provide a physical impediment to the communication medium, the ever-evolving nature of attacks has shown that relying on passive methods does not maintain a guarantee of security.
## 1 Introduction
The question which raises serious research opportunity is: does good performance and the superiority demonstrated by a continuous variant of an optimization algorithm translate into similar good performance when applied to solve binary optimization problems? To answer this question, this paper presents a binary Ebola optimization search algorithm (BEOSA) to solve the feature selection problem and to avoid some of these drawbacks. The baseline EOSA is a recently proposed metaheuristic algorithm [14], a bio-based algorithm inspired by the Ebola virus disease propagation model. The base algorithm was evaluated on 47 classical benchmark functions and compared with seven well-known techniques and 14 CEC benchmark functions, producing superior performance over other methods in the study. This consideration and the selection of the EOSA method as the base algorithm for the binary optimization method proposed in this study was motivated by the performance of the algorithm itself and even a recent outstanding report of its immunity-based variant, namely, IEOSA. The performance of this biology-based algorithm stood out among most of the state-of-the-art optimization methods with similar sources of inspiration. Since the algorithm has proven relevant in addressing some very difficult continuous optimization problems, we sought to determine whether its operators and optimization process could find optimal solutions for binary optimization problems. Hence, through an exhaustive and rigorous experimentation on several heterogeneous and high-dimensional datasets, this study investigates the influence, impact and benefit of designing and applying a binary variant of the EOSA /IEOSA methods. The performance of this method was the motivation for this binarization, and since this method was developed, it has not been utilized to solve the feature selection problem. Since the feature selection problem is binary, we present a binary version of the EOSA to solve this problem. We also utilize the k-near neighbor (kNN) as the classifier to test the goodness of the selected subset of features. The the major contributions of this work are as follows:  
* Proposal of the binary version of the EOSA algorithm (called BEOSA) for feature selection problems. * Evaluation of the performance of BEOSA using a convergence curve and other computational analysis metrics. * Evaluation and validation of the proposed method with 22 small and medium size and 3 high-dimensional datasets. * The proposed method was assessed using seven classifiers to evaluate its performance. * A comparison is made of the efficacy of the BEOSA with some other popular feature selection methods. The remainder of this manuscript is structured as follows: Section 2 presents a review of the relevant literature. Section 3 discusses the methodology used in this study. Section 4 details our proposed BEOSA approach and its application in feature selection. Section 5 centers on the results of the experiments and presents a discussion of this work. Section 6 provides the conclusion.
## 5 Simulation
### Experimental Results and Analysis
#### 5.2.3 Global Model Accuracy  
Figure 5 depicts the global model accuracy over 50 training rounds, evaluated on a held-out test dataset at the cloud level. This analysis reflects the aggregated performance of all participating IoT devices and highlights differences between the asynchronous aggregation approach of our framework and the synchronous strategy employed by HierFAVG.  
Figure 4: Local model training time cost across 50 rounds. (**a**) Training time with our framework. (**b**) Training time with HierFAVG.  
Our framework exhibits noticeable oscillations in accuracy during the initial 20 rounds, stabilizing in later stages to achieve a final accuracy of 94.2%, which is comparable to HierFAVG's 94.6%. The early-round oscillations in our framework are primarily due to the asynchronous nature of the aggregation process, where devices send updates at varying intervals based on their local training progress and network conditions. These asynchronous updates can lead to temporary inconsistencies in the global model, as the aggregated parameters may reflect uneven contributions from devices.  
From a theoretical perspective, asynchronous aggregation inherently accommodates the diverse capabilities and latencies of IoT devices but at the cost of potential staleness in some model updates. For instance, faster devices may contribute more frequently, while slower devices may lag in their updates, causing temporary imbalances in the global model. However, as training progresses, the system gradually incorporates updates from all devices, reducing these inconsistencies and achieving convergence. This behavior underscores the robustness of our hierarchical aggregation strategy, which balances device heterogeneity while maintaining long-term stability.  
By comparison, HierFAVG achieves consistent convergence due to its synchronous aggregation mechanism, which waits for updates from all devices before proceeding. While this ensures uniform contributions to the global model, it also introduces delays, particularly in heterogeneous environments where slower devices can act as bottlenecks. Overall, the comparable accuracy achieved by both approaches after 30 rounds indicates that asynchronous aggregation does not compromise the quality of the global model in the long term. Instead, it provides a practical trade-off by reducing synchronization constraints, making it well-suited for real-world IoT deployments with diverse device capabilities and network conditions.
# Electrically Conductive Nanocomposite Fibers for Flexible and Structural Electronics
###### Abstract  
The following paper presents a simple, low-cost, and repeatable manufacturing process for fabricating conductive, elastic carbon-elastomer nanocomposite fibers for applications in the textile industry and beyond. The presented method allows for the manufacturing of fibers with a diameter of 0.2 mm, containing up to 50 vol. % of graphite powder, 10 vol. % of CNT, and a mix of both fillers. As a result, resistivity below 0.2 \(\Omega\)m for the 0.2 mm-diameter fibers was achieved. Additionally, conductive fibers are highly elastic, which makes them suitable for use in the textile industry as an element of circuits. The effect of strain on the change in resistance was also tested. Researches have shown that highly conductive fibers can withstand strain of up to 40%, with resistivity increasing nearly five times compared to the unstretched fiber. This research shows that the developed composites can also be used as strain sensors in textronic systems. Finally, functional demonstrators were made by directly sewing the developed fibers into a cotton fabric. First, the non-quantitative tests indicate the feasibility of using the composites as conductive fibers to power components in textronic systems and for bending detection.  
polymer matrix composite; textronic; conductive composite; sensors; carbon nanotubes (CNTs); smart clothing; printed electronics; structural electronics +
Footnote †: journal:  
Article  
Electrically Conductive Nanocomposite Fibers for Flexible and Structural Electronics
## 3 Materials and Methods
### System Design and Architecture
#### 3.2.4 Article Retrieval  
DietNerd adopts a methodology akin to a Retrieval-Augmented Generation (RAG) system [17]. The five generated queries are utilized to retrieve relevant information from the PubMed database. This retrieval process is facilitated through Entrez Programming Utilities (E-utilities), an API provided by the National Center for Biotechnology Information (NCBI) that offers access to various NCBI databases, including PubMed [27].  
The retrieval mechanism operates in two stages:  
* Using the API's ESearch utility [28], the system runs each query through PubMed, identifies the ten most relevant articles per query, and stores a list of PubMed IDs (PMIDs). Therefore, for each question, the system can retrieve up to fifty articles.
* Each of the five PMID lists is fed to the EFetch utility [28], which then retrieves 2: article data in the form of Extensible Markup Language (XML) files.  
This approach parallels the retrieval component of RAG systems, which typically access a knowledge base to enhance the contextual understanding and accuracy of language models. In our case, PubMed serves as a specialized knowledge base, providing up-to-date and peer-reviewed information. By integrating this retrieved information with the capabilities of a large language model, DietNerd aims to generate responses that are both relevant to the user question and scientifically accurate based on the biomedical literature.
# Dynamic Interactions between Parallel Grid-Forming Inverters in a Microgrid
## 4 Stability Analysis
### Nodal Admittance Matrix Calculation  
The derivation of \(Y(s)\) is based on constructing the nodal admittance matrix, which is explained in great detail in [33]. First, \(Y(s)\) is constructed assuming the control has no virtual impedance nor the voltage feed-forward loops. Therefore, the nodal admittance matrix of Figure 5 becomes a \(3\times 3\) matrix given by  
\[Y(s)=\begin{bmatrix}Y_{ov,1}(s)+Y_{l,1}(s)&-Y_{l,1}(s)&0\\ -Y_{l,1}(s)&Y_{l,1}(s)+Y_{Load}(s)+Y_{l,2}(s)&-Y_{l,2}(s)\\ 0&-Y_{l,2}(s)&Y_{ov,2}(s)+Y_{l,2}(s)\end{bmatrix} \tag{29}\]  
where \(Y_{ov,i}(s)=\frac{1}{Z_{ov,i}(s)}\), \(Y_{l,i}(s)=\frac{1}{Z_{U,i}(s)}\), and \(Y_{Load}(s)=\frac{1}{Z_{Load}(s)}\)  
If we consider adding the virtual impedance and the voltage feed-forward loops, \(Y(s)\) will have five nodes and can be written as follows:\[Y(s)=\begin{bmatrix}Y_{\text{tr},1}(s)+Y_{\text{op},1}(s)&-Y_{\text{op},1}(s)&0&0&0 \\ -Y_{\text{op},1}(s)&Y_{\text{op},1}(s)+Y_{\text{1},1}(s)&-Y_{\text{1},1}(s)&0&0\\ 0&-Y_{\text{1},1}(s)&Y_{\text{1},1}(s)+Y_{\text{Load}}(s)+Y_{\text{1},2}(s)&-Y_ {\text{1},2}(s)&0\\ 0&0&-Y_{\text{1},2}(s)&Y_{\text{op},2}(s)+Y_{\text{1},2}(s)&-Y_{\text{op},2}(s) \\ 0&0&0&-Y_{\text{op},2}(s)&Y_{\text{op},2}(s)+Y_{\text{tr},2}(s)\end{bmatrix} \tag{30}\]  
where \(Y_{\text{tr},j}(s)=\frac{1}{G_{\text{dr},j}Z_{\text{cr},j}}\).  
Once the \(Y(s)\) matrix is constructed, the stability analysis can be applied.
## 3 Results
### Data Quantity and Out-of-Time Test Set Evaluation
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Exp \#** & **Total Months** & **Train Period** & **Test Period** & **MAE Train** & **MAE Test** & **MeAE Train** & **MeAE Test** \\  & & & & **(Counts/s)** & **(Counts/s)** & **(Counts/s)** & **(Counts/s)** \\ \hline
1 & 4 & Mar19–Jul19 & in-time & 4.383 & 4.421 & 3.361 & 3.370 \\
2 & 7 & Jan19–Jul19 & in-time & 4.280 & 4.306 & 3.296 & 3.310 \\
3 & 12 & Jan19–Jan20 & in-time & 4.446 & 4.442 & 3.353 & 3.361 \\
4 & 12 & Jan20–Jan21 & in-time & 4.202 & 4.222 & 3.314 & 3.327 \\
5 & 24 & Jan19–Jan21 & in-time & 4.445 & 4.449 & 3.421 & 3.423 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: For each training period, spanning from the initial month to the end month (excluding), a neural network was trained, and the resulting MAE and MeAE metrics are reported, averaged per detector and energy range. In some experiments, the total duration of the dataset was delineated as the sum of the training months, validation months and test months. The first five rows provide a comparison between training and test results, illustrating that the neural network avoids overfitting when the test set is randomly selected within the same period as the training set (in-time). However, experiments from # 6 to # 13 reveal that the neural network does not perfectly generalize background estimation when applied to a period not included in the training set (out-of-time). Nonetheless, the NN still yields satisfactory results in these out-of-time scenarios. Here, “#” stands for the specific experiment number. Figure 3: Solar flares counted by GOES-X satellite (source: spaceweatherlive.com, accessed on 10 January 2024) and by the Fermi GBM (source: Fermi GBM trigger catalog).
# A Denoising Method for Seismic Data Based on SVD and Deep Learning
## 3 Results  
### Synthetic Data  
The synthetic 2D seismic data containing 50 traces and 400 sampling points are presented in Figure 6(a). Gaussian noise with SNR = \(-\)4 was added to the synthetic seismic data, and intense amplitude noise was added to the seismic data of the 15 th and 30 th channels. The noise negatively impacted the seismic signal, where part of the seismic signal was completely drowned in the noise (see Figure 6(b)). To denoise the synthetic seismic signal, the seismic data were first reconstructed using the conventional SVD filtering (K = 1 and K = 2). When singular value decomposition filtering was performed to denoise seismic data, K = 1 and K = 2 were the most common because the seismic signal was mainly concentrated on the maximum singular value. Conventional singular value decomposition filtering uses window parameters of n = 15 channels and m = 100 sampling points.  
Only the first singular value (K = 1) was used to reconstruct the seismic data. The results are shown in Figure 7(a), where the random noise is removed to a large extent. However, the seismic signal is lost on a large scale due to the interference of intense amplitude noise, which is not removed. The conventional SVD filtering results are shown in Figure 7(b); the seismic signals are all preserved when K = 2, but it cannot effectively remove the intense amplitude noise. Since the SVD filter does not have low-pass filtering capabilities, the effective frequency band of the synthetic seismic data was between 0 and 100 Hz, and the synthetic seismic data were processed using low-pass filtering (0-100 Hz). The results in the later sections were also after adding low-pass filtering.  
When processing seismic data with the modified SVD filter, the window chosen was the same as the conventional SVD filter. The rank range was expanded to select K = 4 for processing. After the improved SVD processed the seismic data, the seismic signals were kept intact, and the intense amplitude noise was removed successfully. In the region without a seismic signal, Gaussian noise was suppressed completely. This result was also discernible in the region with a seismic signal. The continuity of the lineups' seismic signal was maintained (see Figure 8c).  
The comparison of the SNR of different methods is shown in Table 3. The seismic data's SNR was \(-10.2592\) dB before processing the synthetic seismic data with filling noise. After noise attenuation, the SNR for K = 1 and K = 2 in a conventional SVD filter was \(-6.8822\) dB and \(-6.5176\) dB, respectively. However, the SNR was \(8.1007\) dB after the seismic data was processed using improved SVD filtering. As a result, we discovered that the improved SVD filter's ability to suppress noise had improved.  
Figure 7: (**a**) Synthetic seismic data without noise; (**b**) seismic data with noise added.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Performance Evaluation Criteria  
In the context of the task allocation mechanism, we emphasize the following indicators:  
* _averageErr_: Represents the average of the average difference between the actual query count and the noisy query count results. A smaller difference indicates better algorithm performance.
* _n\({}_{\text{hops}}\)_: Denoting the number of broadcast hops in the platform's task dissemination region. A smaller number of hops suggests faster task completion.
* _ANW_: Indicating the average number of workers required for each task broadcast. A smaller workforce implies higher quality worker selection.
* _TASR_: Representing the acceptance rate by staff once the task is released on the platform. A higher ratio signifies more efficient worker selection.
* _WTD_: Signifying the average distance that staff members need to travel in order to complete their assigned tasks. A smaller distance suggests better suitability of the selected workers for the tasks.
# An In-Depth Review of the Weibull Model with a Focus on Various Parameterizations
## 5 Monte Carlo Simulation Studies
### MC Simulation Study I: Behavior of the ML Estimators  
For WEI4 (with \(q=0.5\)) and WEE5 models, the following regression structure is considered:  
\[\lambda_{i}=\exp\Bigl{(}\mathbf{x}_{i}^{\top}\boldsymbol{\beta}\Bigr{)},\quad \text{and}\quad v_{i}=\exp(\mathbf{z}_{i}^{\top}\xi),\quad i=1,\ldots,n.\]  
To assess the performance of the ML estimators for the WEI4 and WEI5 models, both the bias and the MSE are reported. The bias and MSE are calculated as follows:  
\[\widehat{\mathrm{Bias}}(\widehat{\varphi})=\frac{1}{B}\sum_{j=1}^{B}( \widehat{\varphi}_{j}-\varphi),\qquad\widehat{\mathrm{MSE}}(\widehat{ \varphi})=\frac{1}{B}\sum_{j=1}^{B}(\widehat{\varphi}_{j}-\varphi)^{2},\]  
where \(\widehat{\varphi}_{j}\) is the estimate in the \(j\)th replicate, \(\varphi\) is the true value of the parameter, and \(B\) is the number of MC replicates; 5000 MC replicates are conducted, where the explanatory variables are simulated from a Uniform (0, 1) distribution. Four scenarios for the true values of the parameters are considered: (i) true values of \(\beta_{1}\), \(\beta_{2}\), \(\zeta_{1}\), and \(\zeta_{2}\) are 0.2, 1.0, 0.2, and 1.0; (ii) true values of \(\beta_{1}\), \(\beta_{2}\), \(\zeta_{1}\), and \(\zeta_{2}\) are 0.2, 0.5, 0.2, and 1.0; (iii) true values of \(\beta_{1}\), \(\beta_{2}\), \(\zeta_{1}\), and \(\zeta_{2}\) are 0.5, 1.0, 0.2, and 1.0; and (iv) true values of \(\beta_{1}\), \(\beta_{2}\), \(\zeta_{1}\), and \(\zeta_{2}\) are 0.5, 1.5, 0.2, and 1.0. Tables 3 and 4 report the ML estimation results for the bias and the MSE of the WEI4 and WEI5 model estimators, respectively. A general trend observed in these tables is that--as the sample size increases--both bias and MSE decrease, as expected in a Monte Carlo study. Therefore, the results confirm the consistence and un-biasing of the ML estimators. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & \(\beta_{1}=0.2\); & \(\beta_{2}=1.0\); & \(\zeta_{1}=0.2\); & \(\zeta_{2}=1.0\) & \\ \cline{2-9}  & \multicolumn{5}{c}{Bias} & \multicolumn{5}{c}{MSE} \\ \hline \(n\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\zeta_{1}\) & \(\zeta_{2}\) \\ \hline
30 & \(-0.0028\) & 0.0013 & 0.0761 & 0.0232 & 0.0808 & 0.2592 & 0.2330 & 0.6466 \\
50 & \(-0.0039\) & 0.0035 & 0.0371 & 0.0296 & 0.0502 & 0.1470 & 0.0972 & 0.3091 \\
100 & \(-0.0005\) & 0.0007 & 0.0152 & 0.0179 & 0.0206 & 0.0661 & 0.0411 & 0.1357 \\
500 & \(-0.0001\) & 0.0000 & 0.0048 & \(-0.0005\) & 0.0043 & 0.0134 & 0.0080 & 0.0253 \\ \hline \end{tabular}
\end{table} 
Table 3: Bias and MSE for the WEI4 model parameters, with the indicated sample size and true values of the parameters.
## 5 Result and Discussion
### Data Distribution Rate  
We also investigated the performance difference according to the change in the distribution ratio of training, validation, and test data. The ratio of training data was set while keeping the ratio of validation data and test data the same. The distribution ratio used in the experiment is as follows: training, validation, and test data are 4:3:3, 6:2:2, and 8:1:1 respectively. Figure 6 shows the highest performance with a 6:2:2 distribution ratio. There is not much difference in the performance of each model according to the distribution ratio.