esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Questions and Communication Efficient Methods
### RQ2-How Can Communication Be More Efficient in an FL Environment?
#### 3.2.1 Local Updating
In the proposed framework, the periodic averaging feature is introduced, where it helps to reduce the number of communication rounds. In contrast to other training methods, where each participating device sends their ML models to synchronize through the parameter servers over each iteration, resulting in increasing the number of communication rounds between the devices and central sever, the parameters and local updates of each device can be synchronized with the central server, where a periodic average of models takes place. This is achieved by adjusting the parameter that corresponds to the number of iterations that occur locally on the device itself. Other features, such as partial node participation and a quantized message passing of the FedPAQ, also reduce communication overhead. The quantized message passing is further discussed in the compression schemes subsection. In FL, the FedAVG algorithm is used for its simplicity and, in turn, is about to reduce communication costs in an FL environment. The algorithm tackles the challenges presented in communication by performing numerous updates on available devices before communicating with the central server. However, in some cases, as the authors in [62] state, over heterogeneous data, the FedAVG algorithm could introduce 'drift' towards the updates of each client or device that is participating. This 'client drift' will result in a slow and unstable convergence and could persist if all clients participate in training rounds with full batch gradients. The authors ran their analysis and determined the FedAVG algorithm full batch gradients and matching lower bounds over no client sampling, concluding that it is slower than the stochastic gradient descent over some parameters [62]. To overcome this challenge, the authors created a framework called a stochastic controlled averaging (SCAFFOLD) algorithm that can fix the drift [62]. The SCAFFOLD algorithm performs well for their analysis and provides reliable convergence rates as SGD, even for non-iid data. It also takes advantage of the similarity that exists within the clients, and reduces communication overload [63; 64]  
An approach introduced by authors in [65] called FedDANE tackles some of the practical constraints that are present with FL. The approach is a culmination of methods introduced in [63; 64]. FedDANE collects gradient updates from a subset of devices during each communication round. FedDANE also works well with low client participation settings. In summary, local updating is an essential part of federated learning. Each device needs to compute local updates so a better overall global model can be generated and all participating devices can benefit from it. Making the process of local updating more efficient could indeed make communication more efficient.
## 6 Conclusions  
The fire detection ability of HJ-1B sensors was evaluated based on a simulation. A contextual daytime fire detection algorithm for HJ-1B data was proposed. The work presented in this paper provides both qualitative and quantitative evaluation of simulated HJ-1B fire detection with its characteristics. The analysis has shown the limitations and advantages of HJ-1B data for this purpose. It can be seen from the simulated results that the algorithm performance varies under different fire, atmospheric conditions, solar zenith angle, etc. The performance has been characterized in terms of probabilities of fire detection and false alarm as functions of fire temperature and fire area. There are several general implications from this work: (i) HJ-1B data is capable of detecting the fires as small as about 10 m\({}^{2}\). This advantage is due to its fine spatial resolution compared with MODIS or AVHRR data. However, the fire temperature must be enough high to detect fires with this algorithm when the fire area is about 10 m\({}^{2}\), otherwise, omission errors may occur; (ii) solar zenith angle and different atmospheric conditions can slightly affect the performance of the algorithm; (iii) the factors affecting the performance mainly include the threshold, land surface situation, fire temperature and fire area, etc. High thresholds can reduce commission errors, but can cause large omission errors, and _vice versa_. A  
Figure 8: Cont.  
nighttime fire detection algorithm was not proposed in this work because only daytime AHS data are available. The algorithm proposed in this paper is more similar to a regional fire detection algorithm, because the study area is not large enough. The simulation also provides an important method for fire detection algorithm of HJ-1B evaluation, but which can not represent completely the real landscape. So this work is expected to be tested using the provided a candidate fire detection algorithm for the HJ-1B satellite in the next study.
## 3 Calibration of ATMD with Viscous Damping-Stiffness Control Force
### Equations of Motion and Dynamic Amplification
Considering the control objective and the desired maximal dynamic amplification factor given by (13), the expression for the gain (24) can be simplified to a concise form, dependent on the parameter \(K\):  
\[g_{Nis}=\frac{K\mu-\mu}{K\mu+1}. \tag{25}\]  
The spring stiffness \(k_{2 Nis-opt}\) in (23) is:  
\[\frac{X_{d}(\omega)}{F_{exc}}=\Bigg{|}\frac{\omega^{2}m_{2}}{\left(k_{1}- \omega^{2}(m_{1}+m_{2})\right)\left(k_{a}+k_{2 dh}-\omega^{2}m_{2}+i\omega c_{ a}\right)-\omega^{4}m_{2}^{2}}\Bigg{|}. \tag{17}\]0  
and can be expressed as a function of \(K\) as:  
\[\frac{X_{d}(\omega)}{F_{exc}}=\Bigg{|}\frac{\omega^{2}m_{2}}{\left(k_{1}- \omega^{2}(m_{1}+m_{2})\right)\left(k_{a}+k_{2 dh}-\omega^{2}m_{2}+i\omega c_{ a}\right)-\omega^{4}m_{2}^{2}}\Bigg{|}. \tag{17}\]1The viscous damping coefficient in (22) equals:  
\[\frac{X_{d}(\omega)}{F_{exc}}=\Bigg{|}\frac{\omega^{2}m_{2}}{\left(k_{1}- \omega^{2}(m_{1}+m_{2})\right)\left(k_{a}+k_{2 dh}-\omega^{2}m_{2}+i\omega c_{ a}\right)-\omega^{4}m_{2}^{2}}\Bigg{|}. \tag{17}\]2  
where:  
\[\frac{X_{d}(\omega)}{F_{exc}}=\Bigg{|}\frac{\omega^{2}m_{2}}{\left(k_{1}- \omega^{2}(m_{1}+m_{2})\right)\left(k_{a}+k_{2 dh}-\omega^{2}m_{2}+i\omega c_{ a}\right)-\omega^{4}m_{2}^{2}}\Bigg{|}. \tag{17}\]3  
and  
\[\frac{X_{d}(\omega)}{F_{exc}}=\Bigg{|}\frac{\omega^{2}m_{2}}{\left(k_{1}- \omega^{2}(m_{1}+m_{2})\right)\left(k_{a}+k_{2 dh}-\omega^{2}m_{2}+i\omega c_{ a}\right)-\omega^{4}m_{2}^{2}}\Bigg{|}. \tag{17}\]4  
After substituting (25), (27) and (28) into (22) and (23), we obtain the sought feedback gains conveniently expressed as functions of the primary structure parameters \(m_{1}\), \(\omega_{1}\), the mass ratio \(\mu\), the control parameter \(K\), and, in the case of \(k_{a}\), the actual frequency of vibration \(\omega\):  
\[\frac{X_{d}(\omega)}{F_{exc}}=\Bigg{|}\frac{\omega^{2}m_{2}}{\left(k_{1}- \omega^{2}(m_{1}+m_{2})\right)\left(k_{a}+k_{2 dh}-\omega^{2}m_{2}+i\omega c_{ a}\right)-\omega^{4}m_{2}^{2}}\Bigg{|}. \tag{17}\]5  
\[\frac{X_{d}(\omega)}{F_{exc}}=\Bigg{|}\frac{\omega^{2}m_{2}}{\left(k_{1}- \omega^{2}(m_{1}+m_{2})\right)\left(k_{a}+k_{2 dh}-\omega^{2}m_{2}+i\omega c_{ a}\right)-\omega^{4}m_{2}^{2}}\Bigg{|}. \tag{17}\]6  
The ATMD with the viscous damping-stiffness control force defined by (12) and the parameters (31) and (32), represents a new ATMD that is calibrated to replicate the performance of the ATMD by [PERSON] et al. under harmonic excitation. Despite their different feedback signals, both ATMD designs, the one presented in [17] and the one introduced here, are fully equivalent. Depending on the actual frequency, the active control force (12) consists of a viscous damping force combined with either a negative, for \(\omega^{2}<\omega_{1}^{2}/(\mu+1)\), or positive, for \(\omega^{2}>\omega_{1}^{2}/(\mu+1)\), stiffness force. The case of \(\omega^{2}=\omega_{1}^{2}/(\mu+1)\) results in a pure viscous damping force.
The multiple regression equations, artificial neural networks (ANN), and gene expression programming (GEP) were used for modeling energy dissipation over stepped gabion weirs and the ANN results were recommended for reliable estimation [42]. It is evident from the previous studies that evaluation of energy dissipation using new stochastic methods such as MSP, stochastic MSP, and random forest has not been done yet. Evaluation of new models can introduce the optimum model for prediction of energy dissipation. The primary focus of this research is to investigate the accuracy of three new artificial intelligence-based methods (M5P, stochastic MSP, and random forest) for estimation of skimming flow regime energy dissipation along the cascade spillways. In addition, several regression equation based models are also tested for energy dissipation estimation. For evaluation of used models, experimental data are used. The experimental variables are spillway steps (\(N\)), spillway slope (\(\alpha\)), spillway height (\(H_{dam}\)), and discharge over spillway (\(Q\)).
## 1 Introduction
### Outline of the Paper  
This remaining paper is structured as follows. The background and related work is discussed in Section 2. The system model is presented in Section 3. In Section 4, we propose MPFRS-FC and APCRS-FC techniques for time-triggered flows to improve scheduling rate and reduce execution time. We present the evaluations of our algorithms with a relevant discussion in Section 5 and the conclusion in Section 6, respectively.
# Influence of Soil Characteristics on Wood Biodeterioration by Brown Rot Fungi
## 4 Discussion
Humic substances represent the largest part of soil organic matter (~60%) and are considered to be responsible for many complex chemical reactions in soils [45]. Humic substances have the ability to complex metal ions, oxides, hydroxides, pesticides and toxic pollutants. The formation of these complexes, can dissolve, immobilize and transport metals and organic compounds [46]. Thus, we assume that the organic matter content and other soil compounds such as HA may influence Cu migration as well as other wood preservative compounds in Cu-impregnated wood. For instance, in this study, low concentrations of HA (0.005%) produced higher amounts of Cu released in the water while higher concentrations (0.020%) allowed lower Cu-release. This could be related to the saturation ofreactive sites in the HA by the released Cu. Therefore, the possibility to modify and saturate the reactive sites available in soil substances can help to avoid rapid migration of Cu into organic substrates. In general, there are many different factors affecting wood performance and the durability of utility poles, such as wood species (soft- or hardwood), heartwood/sapwood ratio, type and quality of preservative treatment and the environmental conditions. Vacuum-pressure impregnation aims to protect the sapwood in utility poles as it highly susceptible to wood decay fungi. Our study shows the importance of the proper selection of wood preservatives. Brown rot fungi rapidly degrade glucosyl residues of the wood cellulose molecule [40]. The depolymerisation of cellulose causes a significant reduction in wood strength within relatively short periods (\(<\)10% weight loss) [47]. In the United States a fully loaded wood pole can lose 50% of its initial strength before replacement is required [48]. More recently, [PERSON] et al. [49] studied the influence of brown rot and white rot fungi on the mechanical properties of pine wood samples after different incubation periods. The results showed that after 12 weeks, the changes in stiffness by brown rot fungi was almost five times greater than for white rot fungi. Moreover, they concluded that even before significant weight losses were recorded in wood by brown rot fungi, stiffness was already significantly reduced. This supports the theory that cellulose plays a major structural role on the wood mechanical properties [50] and brown rot fungi are of major concern for wood in service [13]. However, differences in the impact of brown rot fungi, as illustrated here, should be considered as many other factors can influence the metabolism of fungi.
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 1 Introduction
It consists of an OCC-based modulation which can work adequately for short, medium, and long distances without the necessity of previous calibration or specialized equipment in both indoor and outdoor scenarios, which is a critical aspect of future OCC systems for IoT within urban surroundings. The modulation technique compares the similarity between consecutive frames in terms of 2D statistical values. The paper is organized as follows. A brief analysis of state-of-the-art modulations for OCC is presented in Section 2, while the theoretical fundamentals of the modulation are presented in Section 3. Then the modulation and demodulation schemes are detailed in Section 4, the experimental evaluation of the proposed scheme is detailed in Section 5, and the obtained results are presented and discussed in Section 6. Finally, some conclusions are provided in Section 7.
## 3 Results and Discussion
### Sem  
Figure 4 represents optical SEM image of the prepared membrane. As seen in Figure 4, the long titanate nanofibers (TNF) are intertwined with the carbon nanofibers (CNF). The length and the diameter of the lab-synthesized TNFs are 60 \(\upmu\)m and 200 nm, while the length and the diameter of the CNFs are 50 \(\upmu\)m and 150 nm, respectively. The ultralong length of both TNF and CNF ensures the sound mechanical strength and flexibility of the membrane. The interconnected pore structure of the membrane is favorable for high permeation flux, which can be proved in the following performance tests.  
The porosity of the membrane was investigated by analyzing the SEM image of the membrane using Image J software [19]. As presented in Table 1, the average size of pores is 0.071 \(\upmu\)m, and the area of the pores counts for 21.4% of the membrane area. The average pore size of the membrane is lower than the size of the emulsified oil droplet (d90 = 5.27 \(\upmu\)m); thus, the oil cannot penetrate through the membrane, which can be proved by the following performance tests with high oil rejection rates.  
\begin{table}
\begin{tabular}{c c c} \hline
**Total Area of Sample \(\upmu\)m\({}^{2}\)** & **Total Area of Pores \(\upmu\)m\({}^{2}\)** & **\% of Pores Area** \\ \hline
46.455 & 9.956 & 21.4 \\ \hline \end{tabular}
\end{table}
Table 1: Porosity results of the membrane analyzed by Image J.  
Figure 4: (**a**) Optical photo of the prepared membrane. (**b**) SEM image of the membrane materials made of titanate nanofibers and carbon nanofibers.  
Figure 3: The new super superhydrophobic and superoleophobic ultralong titanate nanofiber and carbon nanofiber membrane.
## 3 Results
### Evaluation on Banking Telemarketing Dataset  
This is a publicly available dataset to predict the success of telemarketing.  
From Table 2, it is observed that, for the bank dataset, SMOTE-ENC achieved a significantly greater improvement in precision and F1-score at threshold 0.5 than SMOTE-NC on minority class instances. We further compared these two methods using ROC and precision-recall curve. Figure 1 demonstrates that SMOTE-ENC was a better sampling method than SMOTE-NC for the banking dataset when identifying minority class instances was the focus of interest. We further looked at the statistical significance of our improved results and identified that the \(p\)-value from the two tailed \(t\)-test was less than 0.002 for precision, recall and f1-score. This shows that our improved performance was statistically significant. Therefore, it can be said SMOTE-ENC has been able to interpret nominal features more accurately while generating synthetic data points. We further looked at whether SMOTE-ENC had any effect on the change of importance of features (Figure 2).  
In the banking dataset, euphor3m, cons.price.idx, cons.conf.idx, age and campaign were continuous features. Figure 2 shows that the top three important features were continuous variables, which remained unchanged, contributing >0.55 importance towards classification. A slight shift in the importance of low-ranked features was observed.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 3 Simulation Environment and Settings
### Modeling the Network and Mobility Issues  
We developed a MANET model with heterogeneous nodes (fixed node, mobile nodes with varying speed/mobility, and transmission ranges) using the OMNeT++ simulation software. Figure 2 shows the network model. Heterogeneous nodes comprise PDAs (fixed nodes), laptops serve as low-mobility nodes, and mobile phones serve as high-mobility nodes. In addition to the random waypoint mobility model, we also evaluated the performance of the routing protocols using the Gauss-Markov mobility model, which provides more realistic node movement patterns. The results obtained with the Gauss-Markov model were consistent with those from the random waypoint model, confirming the robustness of our findings.  
As MANET routing protocols must cope with a wide range of network topologies, evaluations are only often performed when there is random waypoint mobility. The issue with this is that worst-case scenarios are not covered for these networks. The random topology that we applied involves the nodes being placed at random in a simulation grid and indicates how protocols may perform in an average scenario run. The density of the network also has an impact on the routing performance, as shown in the results we produced. As the distance between communicating nodes grew, it was evident that there were such latencies in the network, especially with message forwarding.  
Mobility significantly impacts simulation outcomes, and one key insight from our study is that the random waypoint mobility model, while widely used, can lead to inaccuracies in results due to its simplistic assumptions. Despite these limitations, we simulated various scenarios using different routing protocols to evaluate the performance of the developed network. The results from these simulations were then analyzed to compare the network performance and assess the impact of varying configurations and parameters.
## 3 Application in Interventional Surgical Robot Training
### Master Interface  
The robotic system included a suite of master and slave devices which served as the interface between surgeons and the tools. The master interface was a portable 2-DoF robotic device that can be used to issue translation (push/pull) and rotation action during interventions. It is located outside the operation room; hence, the interventionist can sit outside the radiation area to issue control commands. The device has a knob with a magnetostrictive electromagnetic bar that provides hand motion control data. Meanwhile, the usability of the tactile sensor during intravascular interventions was validated by fixing the sensors on the thumb and index fingers to obtain the tactile information as surgeons manipulate the knob. The master device also had a display unit from a control station which is used to guide the bedside (i.e., slave) robotic device during intravascular cardiac interventions. As found in our previous study [36], data from the potentiometer sensor bar are transmitted to a slave robotic device which is positioned close to patients in the operation rooms.  
The apparent resistance signals prove the fast response to touch with stability along the testing time of the pushing and rotating processes. Moreover, we noticed that the outputs of both sensors matched one another almost perfectly. This result proves the potential of the piezoresistive tactile sensor for force sensing in surgical robots, smart gloves, and e-skin.
# Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
## 2 Deep Small-World Neural Network (DSWNN)
### Small-World Transformation of DSWNN
The diagonal weights in matrix \(W\) are not changed because the connection does not disconnect. The global \(W_{a}\) for the entire hidden layers can be represented as the Equation (11). \[W=\left[ \begin{array}{cccccccc}W_{1}&W_{1}^{3}&\cdots&W_{1}^{i+1}&W_{1}^{i+2}& \cdots&W_{1}^{H}\\ 0&W_{2}&\cdots&W_{2}^{i+1}&W_{2}^{i+2}&\cdots&W_{2}^{H}\\ \vdots&\vdots&\ddots&0&0&\cdots&0\\ 0&0&0&W_{i}&W_{i+2}^{i+2}&\cdots&W_{i}^{H}\\ 0&0&0&0&W_{i+1}&\cdots&W_{i+1}^{H}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&0&0&\cdots&W_{H-1}\end{array} \right] \tag{11}\]  
where, \(W_{a}^{b}\) is the weight matrix of the added edges between layer \(a\) and layer \(b\), and its structure is shown in Equation (12). \[W_{a}^{b}=\left[ \begin{array}{cccc}w_{a11}^{b}&w_{a12}^{b}&\cdots&w_{a1M}^{b }\\ w_{a21}^{b}&w_{a22}^{b}&\cdots&w_{a2M}^{b}\\ \vdots&\vdots&w_{axy}^{b}&\vdots\\ w_{aN1}^{b}&w_{aN2}^{b}&\cdots&w_{aNM}^{b}\end{array} \right] \tag{12}\]  
where, \(w_{axy}^{b}\) is the weight of the added edge between the \(x\)th neuron in layer \(a\) and the \(y\)th neuron in layer \(b\). \(N\) and \(M\) represent the number of neurons contained in layers \(a\) and \(b\), respectively. On the basis of the probability \(p\), the \(W_{a}^{b}\) is a sparse matrix in which only the non-zero weights represent the randomly added edges. Therefore, transforming the DSWNN model into a small-world one is equivalent to randomly selecting the position in the global weight matrix and giving non-zero value. The pre-trained weights will not be changed in this procedure.
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 4 Performance Evaluation  
This section presents the performance evaluation results, comparing the serial implementation with the distributed methodology proposed in this article for addressing the EDC placement problem. These performance tests were conducted using the dataset that encompasses the cellular network deployment and usage in the city of Shanghai, as represented in the Shanghai Telecom dataset [13; 14; 15].