esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Electrical Neural Stimulation Effects Using Extraneural and Intraneural Electrodes
## 1 Introduction
### Hybrid FEM-Neuron Simulation to Study Nerve-Electrode Interaction  
[PERSON] et al. [10] investigated the impact of the cuff electrode design in a simulation environment. Flat interface nerve electrode (FINE) cuffs and single-ring round cuff electrodes are considered to evaluate selectivity on the pudendal nerve. When the nerve is deformed due to cuff electrode positioning, nerve segment reshaping is also considered. To evaluate fiber activation, a hybrid FEM-Neuron computational model is used. The main achievement of this study is related to the comparison of nerve fiber selectivity using FINEs and single-ring cuff electrodes to asses an appropriate electrode design for pudendal nerve stimulation in the restoration of bladder function. The hybrid FEM-Neuron approach is a valuable tool that could be used to study the electric stimulation of the median nerve using different types of electrode models in different active site configurations.  
Another study in the literature [4], in which the same hybrid FEM-Neuron approach was used, studied fiber activation and selectivity related to human sciatic and median nerves subjected to electrical current stimulation sent by FINEs and Transverse Intrafascicular Multicontact Electrodes (TIME). Some general effects are studied related to fibrotic reaction, the location of active sites in the nerve section (close to a nerve fascicle or into it), and monopolar and bipolar stimulation. From simulation results about the optimal number of active sites to send current stimulus to in a single TIME model, the optimal number of TIME implants and monopolar and bipolar stimulations using FINEs and TIMEs are studied. Some limitations of this work are related to the consideration of a single nerve topography of inner fascicles and the usage of only the specific models of TIMEs and FINEs. Important aspects remain unaddressed regarding other electrode models (with different active site configurations, for example, bipolar stimulation with active sites located longitudinally along the nerve) and the nerve fascicle's behavior considering the fascicle's topography variability when it is subjected to the electric stimulus.  
Looking at the literature about experimental studies on human median nerve stimulation to restore sensory feedback, it is evident that there is a lack of simulation analysis to evaluate the nerve fascicle activation according to the location of electrode active sites and the nerve fascicle's topography.
## Materials and Methods
### Methods
#### 2.2.5 Image Processing Technology for SEM Testing  
It is widely recognized that F-T cycles can lead to changes of pores in soils. In order to investigate the influence of F-T cycles on the microstructure and pore distribution of soils, test data of samples (\(\sigma_{3}\) = 50 kPa, \(\sigma_{4}\) = 25 kPa) were adopted to conduct the SEM tests after F-T cycles. After the resilient modulus test, the soil samples were naturally dried. Small rectangular samples with a size of 10 mm \(\times\) 5 mm \(\times\) 5 mm (height \(\times\) length \(\times\) width) were cut from the specimens. They were glued on trays and gilded for scanning. To observe the microstructure of cohesive soils, the magnification of 500\(\times\) to 1000\(\times\) is suitable. In this manuscript, the test samples were captured at the magnification  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & **Confining** & **Cyclic Deviator** & **Axial Stress \(\sigma_{1}\)** & **Loading** \\  & **Pressure \(\sigma_{c}\) (kPa)** & **Stress \(\sigma_{d}\) (kPa)** & **(kPa)** & **Cycles** \\ \hline \multirow{2}{*}{0-Preloading} & 30 & 36 & 66 & 1000 \\ \cline{2-5}  & 40 & 25 & 65 & 100 \\ \(1\) & 40 & 15 & 55 & 100 \\  & 40 & 5 & 45 & 100 \\ \hline \multirow{4}{*}{2} & 50 & 5 & 55 & 100 \\  & 50 & 15 & 65 & 100 \\  & 50 & 25 & 75 & 100 \\ \hline \multirow{4}{*}{3} & 40 & 25 & 65 & 100 \\  & 40 & 15 & 55 & 100 \\ \cline{1-1}  & 40 & 5 & 45 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loading sequence of resilient modulus test.  
of 500\(\times\) and 600\(\times\). Three evenly distributed points in each test sample were selected and scanned by electron microscopy. Images with no delta points (points containing specific information, such as cracks) and clearly reflecting the typical structure of soils were obtained as the representative images for further imaging analysis. The scanned images are processed as follows:  
Firstly, the original image was obtained and processed to binarization (shown in Figure 3a).  
Secondly, the appropriate threshold values for test soils were manually adjusted to make the dark particles in the binary image (shown in Figure 3b) the same as those in the original image. The threshold value could be first set near the gray average of the image and then adjusted to obtain the optimum value. In this study, the threshold values of unmodified soil range from 68 to 72, while the average threshold values of the modified soil vary from 85 to 94.  
Thirdly, small and irregular miscellaneous points of the image caused by impurities and image noise were removed to avoid influencing the analysis and statistical results. In this paper, pores with area less than 30 pixels were removed (shown in Figure 3c).  
Finally, the porosity and mean diameter were calculated by the IPP (Image Pro Plus) software to analyze the change of pores.
## 3 Results
### Antioxidant Capacity  
Some researchers found that D-limonene has the effects of antioxidant, inhibition of lipid peroxidation, and resistance to cell damage induced by free radicals [42]. [PERSON] et al. added \(\beta\)-myrcene to corn oil (200 mg/kg/d) and administered it to Sprague-Dawley (SD) rats via gastric irrigation. After 60 days, the TBARS content in liver tissue decreased by 21 \(\pm\) 0.5 nmol/g, which proved that \(\beta\)-myrcene has a positive antioxidant ability [44]. It was observed that sabinene possesses inhibitory activity against acetylcholinesteraseinesterase [36]. The above studies indicate that these flavors not only confer favorable flavor to PPO but also exhibit antioxidant and other biological activities.  
The antioxidant analysis results of PPO and microcapsules with different storage times consistently demonstrated that the incorporation of embedded microcapsules effectively reserved the antioxidant capacity of PPO (Figure 5). All four encapsulation methods increased the three antioxidant capacity measures all throughout the air-exposure period (\(p<0.05\)). Encapsulation enhanced the antioxidant capacity of PPO, probably because of the high retention effect of microcapsules on flavor substances. The shell structure of the microcapsules efficiently encapsulated the active ingredient in oil, decreasing its exposure time to oxygen and thereby augmenting its antioxidant properties.  
Figure 4: Changes in the OAV of flavor substances during the storage period: (**a**) Heat map of substances with OAV \(>\) 1 for 15 days. (**b**) Heat map of core flavor substances’ OAV changes over 15 days. (**c**–**f**) Changes in OAV of linalool, sabinene, \(\beta\)-myrcene, and D-limonene in PPO, SS, and SG during the storage period.  
The DPPH clearance, hydroxyl radical clearance, and lipid hydroperoxide clearance of PPO, SG, and SS showed similar magnitudes due to the antioxidant activity of PPO itself on day 0 (Figure 5), then decreased with time. The DPPH clearance decreased linearly due to the rapid oxidation of PPO over 15 days (Figure 5a). The scavenging rate of the hydroxyl radical and the lipid hydroperoxide clearance rates decreased exponentially with time (Figure 5b,c). There were significant differences in antioxidant capacity between PPO and microcapsules (\(p<0.05\)).  
In general, the relatively porous structure of the freeze-dried surface permitted faster evaporation of volatile PPO components, thus reducing the EE, which resulted in a higher antioxidant capacity of spray-dried microcapsules than freeze-dried microcapsules. SG had a higher antioxidant capacity than SS, which agrees with a previous report that large particle-size spray-dried powders had poor oxidation stability compared to small particle-size powders [45].
## 3 Interference Mitigation Schemes
### Power Control Approach
#### 3.1.7 Bayesian Game Power Control Scheme  
Another PCG based on the Bayesian game is used to mitigate inter-WBASN interference [23]. This system is modeled with multiple WBASNs in which each WBASN consists of one coordinator and multiple sensors. The Bayesian game is defined as follows: A player is a WBASN; the type set of players is identical to the node set that is scheduled to transmit; the set of players' actions chooses its power; the payoff function for players includes the type and the corresponding power. In this scheme, the WBASN tries to maximize its payoff that is evaluated by the Bayesian equilibrium. Each WBASN maps to the set of actions based on its type.
## 4 Genetic Regulatory Network-Based Rescheduling Method
### Regulatory Parameter Optimization  
Based on values of regulatory parameters \(\epsilon_{1}\) and \(\epsilon_{2}\), the gene expression procedure governed by Equation (4) determines the gene states \(\{y_{m}|r=1,2,\cdots,R;\ m=1,2,\cdots,M\}\) within the \(l\)th window, each of which represents whether the \(r\)th job entering the HFS belongs to job type \(m\). Moreover, the regulatory parameters \(h_{1}\) and \(h_{2}\) specify the gene regulation in Equation (5) and further determine the gene states \(\{x_{\textit{rki}}|r=1,2,\cdots,R;\ k=1,2,\cdots,K_{i};\ i=1,2,\cdots,I\}\) within the \(l\)th window. Each gene state \(x_{\textit{rki}}\) represents whether the \(r\)th job is manufactured on the \(k\)th machine at the \(i\)th production stage. In this way, a solution to the rescheduling problem is obtained based on regulatory parameter values (i.e., \(\epsilon_{1}\), \(\epsilon_{2}\), \(h_{1}\) and \(h_{2}\)). Figure 6 illustrates this procedure in the rescheduling of a specific HFS.  
A parameter optimization procedure is further implemented to minimize the makespan. According to the regulation equations, a gene receives inhibition if its expression breaks scheduling rules, and the inhibition strength is weighted by related regulatory parameters. These parameters have different values because each rule plays its distinctive role in determining an optimal solution. For example, decreasing setup times is important if machines require comparatively longer durations for job type changeovers, and the parameter \(h_{2}\) should have a large value. The sensitivity analysis on makespan can evaluate the importance weights and thus realize regulatory parameter optimization [42]. Alternatively, machining learning methods can also optimize these parameters if there is enough  
Figure 6: An example of GRN-based rescheduling solution (\(M=2\), \(I=2\), \(k_{1}=2\), \(k_{2}=2\), \(R=16\), \(d_{1}=8\), \(d_{2}=8\)).  
historical data. For instance, a neural network with workshop conditions can be trained to recommend appropriate parameters.  
Apart from these analytical and machine learning methods, random searching algorithms (e.g., genetic algorithms and immune learning algorithms) are also alternative to optimize parameters [43]. For instance, each individual in the genetic algorithm can represent a specific value set of regulatory parameters, and the fitness value of this individual can be evaluated based on related makespan. Through a series of genetic operations (i.e., evaluation, crossover and mutation), the best individual in this algorithm determines the minimum makespan. Moreover, a real-coded algorithm should be used in the parameter optimization procedure because the regulatory parameters are real variables.
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 5 Detail of Correlation-Based Federated Learning
### Aggregation Optimization
#### 5.2.2 Correlation Degree Analysis  
Over \(T\) unit time intervals, the center server \(C\) obtains a feature sequence named \(B_{C}\), and \(B_{C}=\left\{b_{C}^{1},b_{C}^{2},\cdots,b_{C}^{T}\right\}\). The edge servers also obtain their feature sequences in the same way, which could be expressed as \(B_{e_{j}}\), and \(B_{e_{j}}=\left\{B_{e_{j}}^{1},B_{e_{j}}^{2},\cdots,B_{e_{j}}^{T}\right\}\). Then, the relative entropy \(L(B_{e_{i}}||B_{C})\) based on the edge server feature sequence \(B_{e_{j}}\) and center server feature sequence \(B_{C}\) could be calculated according to Formula (18).  
\[L(B_{e_{i}}||B_{C})=\sum_{i=1}Tb_{e_{j}^{i}}\ln\frac{b_{e_{j}}^{i}}{h_{C}^{i}} \tag{18}\]  
In the next step, the relative entropy \(L(B_{e_{i}}||B_{C})\) is used to analyze and quantify the correlation degree according to Formulas (19) and (20).  
\[w_{e_{j}}=\tanh(L(B_{e_{i}}||B_{C})) \tag{19}\]  
\[w_{e_{j}}^{C}=1-\tanh(L(B_{e_{i}}||B_{C})) \tag{20}\]  
where the larger the value of \(w_{e_{j}}^{C}\) indicates that in the cooperative update, the larger the proportion of the global parameter in parameter aggregation. Conversely, the larger the proportion of local parameters in parameter aggregation.  
The detailed feature grouping algorithm is shown in Algorithm 3.  
```
0: eigenvalue sequence length \(T\), number of detecting nodes \(m\)
1: // detecting nodes execute
2: Initialize: current number of eigenvalues \(i\)
3:for\(i=0\);\(i<T\)do
4: calculate eigenvalues for the current feature
5:endfor
6: end eigenvalue sequence to aggregation node
7: // aggregation node executes
8:for\(j=0\);\(j<m\)do
9: receive eigenvalue sequence \(H_{d_{j}}\) from \(d_{j}\)
10:endfor
11: calculate eigenvalue sequence \(H_{C}\) based on \(H_{d_{j}}\)
12: calculate correlation degree of \(H_{C}\) and \(H_{d_{j}}\)
13: calculate parameter weight \(w_{d_{j}}^{C}\), \(w_{d_{j}}\) for \(d_{j}\)
```  
**Algorithm 3** Parameter optimization algorithm
## 3 The Proposed Model and Methods
### PAM for a Nonlinear System Model
The mathematical model of the system is  
Figure 3: Force and contraction characteristic of Festo DMSP-20 [30]. given in (6), where \(k_{1}\) and \(k_{2}\) correspond to the constants of the pre-tensioning springs on both sides of the PAM, and \(k_{\text{Pam}}\) refers to the spring behavior due to the internal structure of the PAM. Among the other variables defined in Equation (6), \(x_{0}\) and \(x\) are the initial and final position, respectively, and \(F(P)\) is the static force resulting from the applied inlet pressure. For each spring used as a system component, the spring constant is modeled by calculating with (7), where \(R\) is the radius of the spring coil, \(n\) is the number of turnings, \(G\) is the shear modulus of the spring material, and \(d\) is the diameter of the wire. Thus, the stiffness value of the system with constant coefficient springs can be obtained. \[-m\ddot{x}+c\dot{x}+k_{1}x+k_{2}x+k_{Pam}(x-x_{0})=F(p) \tag{6}\]  
\[k=\frac{G*d^{4}}{64*n*R^{3}}\text{ and }\sum_{i=1}^{n}k_{i} \tag{7}\]  
Even when all components of the system are modeled with damping and spring characteristic elements, precise modeling requires approximations through the determination of damping and stiffness coefficients for each element [11; 12; 27], and still, experimental validation is decisive. For the control algorithm of a system that is already in active use with its design phase completed, the most effortless approach to developing the control algorithm is through a data-driven method. Artificial neural networks offer superior advantages over other methods for obtaining experimental coefficients in the given system, providing practical benefits. With the obtained discrete measurement results, an embedded data-driven controller can be designed without the need to model the entire system, making it suitable for real-time control applications.
## 3 Results and Discussion  
### Exploration of Optimal Hyperparameters  
Tables S2-S6 exhibit the exploration results for the optimal hyperparameters for each of the 16 models listed in Table 2. The optimal hyperparameters depended on the model configuration. For the SVM-N model, the RBF parameter \(\gamma\) had a larger impact on model performance than the regularization parameter \(C\).
# Energy Harvesting over Rician Fading Channel:
## 3 System Performance
### Optimal Time-Switching Factor  
It is not difficult to learn that there is always a trade-off between the amount of energy used for transmission and the duration of the transmission in the considered protocol. Specifically, if more time is allocated for energy harvesting, a higher available transmission power can be obtained, which may lead to a higher throughput. However, at the same time, less time resources are left for signal transmission, which may lead to the decrease of the transmission rate. Hence, there exists an optimal time-switching factor \(\alpha^{*}\) that provides the best throughput performance.  
Given the throughput expression obtained in Equation (16), the optimal time switching can be obtained by solving Equation \(\frac{d\tau_{t}(a)}{da}=0\). However, due to the complicated infinite series and Bessel function in each throughput expression, this optimization problem can hardly admit a closed-form solution.  
Here, we apply an iterative algorithm to solve this problem numerically. In particular, the Golden section search algorithm, which has been used in many global optimization problems in communications (for example, in [35]), is chosen for this work. For a detailed algorithm as well as its related theory, please refer to [36].
## 3 Prototype Description
### Clustering Methods
#### 3.7.1 K-Means  
The K-means algorithm is a clustering technique used in data analysis and machine learning. Its goal is to partition a given dataset into \(K\) distinct, non-overlapping clusters [64]. Each data point is assigned to the cluster whose centroid (mean) is closest to it. This algorithm is particularly useful for grouping similar data points together [65]. The K-means algorithm follows a simple yet effective process:  
* Initialization: Choosing \(K\) initial cluster centroids. These can be randomly selected data points or determined using other methods.
* Assignment: Assigning each data point to the nearest centroid. This creates \(K\) clusters.
* Update: Recalculating the centroids of the clusters based on the current assignment of data points.
* Iteration: Repeating the assignment and update steps iteratively until convergence. Convergence occurs when the centroids stabilize or a predetermined number of iterations is reached.  
Given a dataset with \(N\) data points \(\{x_{1},x_{2},\ldots,x_{N}\}\) and cluster centroids \(\{c_{1},c_{2},\ldots,c_{K}\}\), the assignment step can be mathematically expressed as follows:  
\[\text{cluster}(x_{i})=\arg\min_{j}||x_{i}-c_{j}||^{2}\]  
where \(||x_{i}-c_{j}||^{2}\) represents the squared Euclidean distance between data point \(x_{i}\) and centroid \(c_{j}\). The update step involves calculating new centroids \(c_{j}\):  
\[c_{j}=\frac{1}{N_{j}}\sum_{x_{j}\in\text{cluster}(j)}x_{i}\]  
where \(N_{j}\) is the number of data points in cluster \(j\).
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 3 Methodology: Mapping Study
### Planning
#### 3.1.3 Inclusion and Exclusion Procedure
In this stage of manual filtering, two document selection phases are carried out: the phase based on titles and abstracts and the phase based on full-text review. Figure 8: Decision tree for manual selection of papers, based on [24]. \begin{table}
\begin{tabular}{c c l} \hline \hline
**Criteria** & **Description** \\ \hline \hline Inclusion criteria & 1. & The paper is a primary contribution. \\  & 2. & The contribution reported in the paper is related to UAV path planning in 3D. \\  & 3. & The paper includes at least one contribution that proposes a 3D UAV path planning technique. \\ \hline Exclusion criteria & 1. & The work reports a secondary or tertiary contribution. \\  & 2. & The contribution does not focus on UAV path planning in 3D. \\  & 3. & The reported contribution does not use a 3D UAV path planning technique. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Manual inclusion and exclusion criteria. 1. **Phase of titles and abstracts:** For the selection of the documents, the participation of one or more researchers (evaluators) is required. The researchers collaborate with the analysis and choice through the manual inclusion-exclusion criteria, with 20 percent of the information uploaded in CADIMA. The researchers discuss and resolve the divergences found in the classification of articles in scheduled meetings. At the end of this stage, there are 104 selected documents. 2. **Full-text phase:** In this second phase, the evaluators carefully read the text of the documents selected in the first phase. The title is analyzed first, then the abstract, then the techniques used (algorithms), and finally, the conclusions. In this second selection phase, like the first phase, the researchers review 21 contributions (20 percent) of the 104. The researchers discuss and resolve the divergences found in the classification of articles in scheduled meetings. Once the manual inclusion-exclusion process is completed, 37 documents are finally obtained for the coding process.
# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 4 Deep Learning Techniques for Load/Generation Forecasting  
In this section, the criteria that can be utilized to effectively forecast demand and generation in an MG (residential MG) is presented. As mentioned before, unlike predictable scenarios [6], the randomness and intermittency of both the generation and consumption in MG systems cause difficulties and uncertainties when forecasting their trends. However, noticeable efforts have been conducted in this regard.  
In [8], a method based on utilizing artificial neural networks (ANN) for short-term load forecasting (STLF) with a 24-hours time-horizon in an MG scenario was proposed by [PERSON] et al. The prediction model of this study consists of two stages where the output of the first stage (maximum and minimum forecasted values for the following day) were  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **PV** & **Load** & **PCC No Control** & **PCC EMS** & **\%Change** \\ \hline Energy (kWh) & 2.145 & \(-\)2.599 & \(-\)0.455 & 0.019 & \(-\)104.2 \\ Power RMSE (kW) & - & - & 0.191 & 0.029 & \(-\)96.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between base scenario and MQTT-based EMS.  
Figure 4: PCC test results. Positive and negative signs indicate the direction of PCC power exchange, import to the MG, and export from the MG, respectively.  
used by the second stage that provides the 24-hours prediction data. [PERSON] et al. proposed a recurrent neural network (RNN) algorithm to forecast both the PV-generation and household-consumption in residential MGs for the following day(s). In this study, the uncertainty factor of both the trends were explained and the importance of implementing long short-term memory (LSTM) methodology in the RNN structure was explained [9]. Wind power and PV generation patterns have been forecasted (for the next 24-hours) by using a multistage prediction strategy that was composed of empirical mode decomposition (EMD) and cascade forward neural network (CFNN) algorithms. These predictions were utilized to manage and optimize the consumption from the demand side by providing forecasted generation curves [10]. [PERSON] et al. [31] explained the fundamental differences between forecasting aggregated residential load of multiple users with forecasting a single load. They described the challenges of load forecasting for single residential applications and proposed an LSTM-RNN framework and validate its performance with real-world data.