esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 3 System Model  
### Sensor Network Model and Assumptions  
In designing the routing protocol, the network model presents the sensor nodes' operating environment that consists of \(n\) homogeneous sensor nodes, i.e., \(N_{i}(i=1,\ 2,\ 3,\ldots\ldots,n)\). The sensor nodes are distributed randomly in a target monitoring area of \(F\times F\) dimensions, and they are stationary after deployment. Each node has the ability to sense the events (activity), collect, process, and communicate data within the network. The nodes are equipped with battery energy that is limited and mostly consumed during the data transmission and reception process at its radio transceiver. In traditional WSNs, the nodes are left unattended after deployment; therefore, battery replacement or recharge is not feasible. Data sensing occurs when an event occurs, e.g., rise in the temperature of the environment wherein the nodes are deployed. In the proposed scheme, an event is defined as the variation in the sensed value beyond a certain predefined threshold level. In Figure 2, the car accident represents the occurrence of an event as it will result in an increased environmental value. The cluster member (CM) node located near the event-occurring area will sense the data and report it to the CH. The sensed information is transmitted to the BS through CHs. The BS is located far from the network area, and it does not have limitations regarding computational power, energy, and memory resources. The proposed scheme is a centralized routing algorithm in which the centralized entity is the BS. Additionally, it considers that all the transmissions to the BS are solely through the CH using multi-hop communication, except for the first communication round. Further, in our scheme the CH selection process takes residual energy and intra-cluster communication cost into account for selecting a node as the CH. This results in selection of a head node which is well-located and has a sufficient amount of energy to transmit data from member nodes to the BS.  
In this study, the sensor nodes were organized in the form of clusters. Each cluster had its own CH, and it was responsible for data aggregation and transmission to the BS in a multi-hop manner. Nodes belonging to the clusters were referred to as CMs, and the nodes that could be reached by a single-hop transmission using a maximum transmission radius \(r_{t}\) were referred to as neighbor nodes. Additionally, only the member node near which the event occurred delivered its sensed data to the CH over multi-hop paths. These multi-hop paths were formed as a result of the next hop choices made on the basis of a weight function, which is discussed in detail in Section 4.4: Data Transmission Phase.  
Figure 2: Network topology of proposed scheme.  
This resulted in energy-efficient data packet transmission from the source node (CM) to the destination node (CH). Once data reached the CH, the CH aggregated it and transmitted it to the BS. Figure 2 illustrates the proposed sensor network topology.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 5 Results and Analysis
### Reflection Phase  
Figure 8 shows the reflection phase for the proposed 16 elements metasurface antenna with and without PIN diodes. As observed from the figure, there was a phase difference of 21.8\({}^{\circ}\) and 4.3\({}^{\circ}\) at 28 GHz and 40 GHz (designed operating frequencies), respectively, as diode capacitance was increased. Additionally, before adding the diodes, there was a phase shift of 360\({}^{\circ}\) at 26.08 GHz and at 26.68 GHz when the diodes were added for all capacitance values. The same phase shift was also observed at 22.7 GHz for when capacitance was 0.05 pF.  
Figure 8 further shows that for a frequency reconfigurable metasurface, the capacitance increases when the operating frequency decreases [62, 63].  
Figure 7: Return loss simulation results of 16 element metasurface array with and without PIN diodes.
## 3 Software Design of Performance Testing System for Space Harmonic Reducer

Table 1: _Cont._the transmission efficiency of harmonic reducer can be determined by calculation, but there are too many factors affecting the transmission efficiency, and some assumptions must be made when conducting quantitative analysis, so the theoretical calculation results are often approximate values even if the calculation accuracy can reach a high level. As a definite value of reducer efficiency, it has lost its significance. Therefore, it is necessary to determine the transmission efficiency of the reducer by test. Formula (3) shows that the efficiency is ultimately related to the input-output torque and the deceleration ratio. As long as the deceleration ratio is known, the transmission efficiency can be calculated by collecting the input and output torque values in real time. \[\eta=\frac{P_{0}}{P_{i}}\times 100\%=\frac{n_{0}\times T_{0}}{n_{i}\times T_{i}}= \frac{n_{0}\times T_{0}}{n_{0}\times i\times T_{i}}=\frac{T_{0}}{i\times T_{i} }\times 100\% \tag{3}\]  
In the efficiency test, the operation mode, operation time, load torque size and other related parameters can be set, and parameters such as input, output torque, operation speed and efficiency can be displayed in real time. Efficiency testing is divided into three modes: constant speed and constant torque efficiency testing; constant speed and variable torque efficiency testing; variable speed and constant torque testing mode. Angle, input, and output torque are sampled at the same time in order to ensure accuracy. The test curve and the calculated precision curve can be stored in JPG image format. HBM torque sensor is also a CANOpen interface. Through the CAN card with two channels in the PCI slot in the computer, HBM sensor and motor servo controller can be connected respectively. In software, different channel numbers need to be set to distinguish. The performance test software of space harmonic reducer has many test tasks, among which the swing life test takes the longest time in all tests, and it has certain representativeness. As an example, the software control process architecture is shown in Figure 4.
## 1 Introduction
* Use two different cities having different geographical locations and different weather conditions that are Alexandria city in Egypt and Srinagar city in India and study the performance of the suggested model under their weather conditions. The remainder of the paper is structured as follows. Section 2 illustrates the code property and construction of FRS code followed by the proposed model description in Section 3. Section 4 shows the mathematical analysis in terms of BER of our suggested model. Finally, obtained simulation results are discussed in Section 5, followed by main conclusions and suggested applications for our FSO model are drawn in Section 6.
## 5 Numerical Results
### Testing 2D Interpolation  
In this section, the proposed RBF-TPU approach is assessed utilizing a 2D model function, where the following function and its derivatives are used:  
\[f(x,y)=\left\{\sqrt{1+2\exp\biggl{(}-\frac{3}{2}\biggl{(}9\sqrt{x^{2}+y^{2}}-6 \biggr{)}\biggr{)}}\right\}^{-1} \tag{24}\]  
\[\frac{\partial f(x,y)}{\partial x}=\frac{27x}{2\sqrt{x^{2}+y^{2}}}\exp\biggl{(} -\frac{3}{2}\biggl{(}9\sqrt{x^{2}+y^{2}}-6\biggr{)}\biggr{)}f^{3}(x,y) \tag{25}\]  
\[\frac{\partial f(x,y)}{\partial y}=\frac{27y}{2\sqrt{x^{2}+y^{2}}}\exp\biggl{(} -\frac{3}{2}\biggl{(}9\sqrt{x^{2}+y^{2}}-6\biggr{)}\biggr{)}f^{3}(x,y) \tag{26}\]  
The model function and its derivatives (i.e., Equations (24)-(26)) are approximated with \(21\times 21\) mesh points within the range of \([-1,1]\) for \(x\) and \(y\). This mesh size is selected as the mesh points have a negligible effect on accuracy, as seen in Table 2. The results of the conventional RBF approach are plotted in Figure 9. It can be observed that the results of the 2D model have a minimum error at a certain shape parameter value or in a narrow range of shape parameter values, similar to the case of 1D simulation (Figure 4). For regular mesh distribution, the minimum errors of the interpolation and derivatives are \(-0.707\), \(0.66\), and \(0.66\) for \(f\), \(\frac{\partial f}{\partial x}\), and \(\frac{\partial f}{\partial y}\), respectively, which are located approximately at \(\varepsilon\approx 0.95\). For the stretched mesh distribution, the minimum errors for \(f\), \(\frac{\partial f}{\partial x}\), and \(\frac{\partial f}{\partial y}\) are \(-1.840\), \(-0.260\), and \(-0.260\), respectively, and are located at \(\varepsilon\approx 3.6\). These results will act as reference results with which to compare. For RBF-TPU, the effects of \(m\) and \(\varepsilon\) on the results of the 2D case are plotted in Figure 10. Similar to the case of 1D interpolation (see Figure 8), the error of RBF-TPU does not change significantly when changing \(m\) and \(\varepsilon\). The results of the comparison between Gaussian RBF and RBF-TPU for the 2D case are tabulated in Table 3. The average errors of interpolation and derivatives are \(-0.713\), \(0.65\), and \(0.65\) for the regular mesh distribution and \(-1.117\), \(0.132\), and \(0.145\) for the stretched mesh distribution. These results are consistent with the results obtained for the 1D case. Hence, it can be concluded that the present basis (i.e., RBF-TPU) yields accurate results for the 1D and 2D cases that are comparable to those of RBF interpolation and independent of the shape parameter, \(N_{e}\), and \(m\).  
Figure 9: The error of approximating the given function \(f(x,y)\) and its derivatives using the conventional RBF approach as it varies with \(\varepsilon\). The Gaussian radial basis function is used and the number of mesh points is fixed as 21 \(\times\) 21 regularly distributed points.
## 2 Model Development and Numerical Computation
### Contact Model and Material Parameters  
The results of the discrete element method (DEM) simulations were significantly influenced by the choice of model boundary conditions [53; 54]. By transitioning from conventional rigid boundaries to a flexible boundary construction method [55; 56], we developed a three-dimensional discrete element model that integrates a particle membrane flexible boundary for triaxial testing. In DEM simulations, the contact model dictates the mechanical interactions between particles, thereby playing a pivotal role in accurately predicting the macroscopic mechanical behavior of S-RMs.  
By analyzing the contact characteristics of gravel clumps and sand particles, along with a review of previous scholars' research [2; 57; 58], this study developed a linear contact bond model for the contact force between sand particles that accurately captures the cohesion between them. For the contact constitutive models between gravel clumps and sand particles, a linear contact model was adopted to optimize computational efficiency. The contact model between flexible boundary particles was set as a linear parallel bond model [59]. Given the scarcity of large-scale laboratory triaxial test data for S-RMs, this study utilized a comprehensive set of parameters derived from discrete element numerical simulations of S-RMs to inform the selection of stiffness parameters, friction coefficients, and damping coefficients for the contact model (see Table 2). Simultaneously, to prevent fracture failure at particle boundaries due to concentrated stresses, the tensile strength and shear strength were set to extremely high values. Detailed parameter values are provided in Table 3.  
Figure 4: Discrete element models of gravel clumps, sand balls, and S-RMs.  
Figure 3: The optimization of the combination of two parameters: clump creation with different parameters.
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 4 Results and Discussion
Our TinyML model demonstrates superior performance, achieving higher accuracy, 96%, while also excelling in resource efficiency, making it more suitable for deployment on low-power devices. One of the key reasons our model outperforms that of [33], which used the same dataset, lies in the application of pruning and quantization techniques. These methods allowed us to significantly reduce both model size and inference time, optimizing the model for resource-constrained environments. Pruning effectively removes less critical weights from the network, thus speeding up computation and reducing memory usage, while quantization lowers the precision of the model's parameters without substantially affecting its accuracy, leading to more efficient deployment on embedded devices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Inferencing Time** & **Peak RAM USAGE** & **Flash Usage** \\ \hline
**Our CNN** & 127 ms & 12.0 K & 249.6 K \\ \hline
**CNN Edge Impulse** & 6 ms & 4.5 K & 31.7 K \\ \hline
**Our LSTM** & 324 ms & 23.2 K & 190 K \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of different models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & [33] & [34] & **Our Work** \\ \hline
**Dataset** & KAUH & ICBHI 2017 & **KAUH** \\ \hline
**Feature** & Mel-spectrogram & MFCC & **MFCC** \\ \hline \multirow{4}{*}{**Preprocessing**} & Discrete Fourier & Zero padding, & **Zero padding,** \\  & transform (DFT)-based & segmentation into 3 s & **Butterworth bandpass** \\  & filtering and & windows, Butterworth & **filter, and** \\  & segmentation into 5 s & bandpass filter, and & **segmentation into** \\  & windows & \(Z\)-score normalization & **5 s windows** \\ \hline \multirow{2}{*}{**Lung diseases**} & Asthma and healthy & Normal and & **Ashma and healthy** \\  & & subnormal & **Ashma and healthy** \\ \hline
**Target device** & - & Alinx AX7A200 FPGA & **Arduino Nano 33 BLE** \\ \hline
**Model** & RDsLINet & LSTM & **CNN** \\ \hline
**Accuracy** & 91\% & 81.4\% & **96\%** \\ \hline
**Total execution** & \multirow{2}{*}{5.439 s} & \multirow{2}{*}{-} & **890 ms** \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline
**Fash usage** & 498 KB & - & **249.6 KB** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Qualitative performance comparison of the proposed CNN with existing works. In contrast, in [33], the authors applied depthwise separable convolutions and global average pooling (GAP) layers instead of fully connected layers to reduce the model size and execution time. This likely contributes to the differences in execution time and accuracy observed in our model compared with [33].
# Sensitivity Analysis for Microscopic Crowd Simulation
## 2 Materials and Methods
### Choice of Sensitivity Analysis Methods  
As discussed in the Introduction (Section 1.1), we are interested in a ranking of model parameters by their impact on the simulation outcomes to learn more about our models and, possibly, to reduce  
Figure 3: Snapshot of a simulation of the bottleneck scenario used for the sensitivity analysis after 30 s.  
Figure 2: Voronoi diagram: For each agent, a Voronoi cell is shown which contains all points that are closer to this agent than to any other agent. The size of a Voronoi cell gives an indication of the agents free space and it is used to determine the density.  
complexity. Variance-based approaches and, in particular, Sobol' indices, have proved their worth in many applications ([18], p. 237 ff). There are several open-source libraries for the approximation of Sobol' indices due to their popularity. Furthermore, variance-based approaches need only model evaluations; gradients or further outputs are not necessary. Moreover, Sobol' indices can be calculated as part of a forward propagation when using an approximation based on polynomial chaos [50] with reduced computational costs. All of these advantages make them an attractive choice for our application.  
Variance-based methods serve to identify important parameters in the input parameter space. Based on the results, single parameters that were classified non-influential can be fixed to an arbitrary value within the range. This procedure is typically referred to as factor fixing. However, the analysis of parameter importance by itself may not point out enough potential for model reduction. Therefore, we also aim to identify important directions, which is, linear combinations of the input parameters that are particularly relevant. For this, we harness the theory of active subspaces [19; 51]. [PERSON] and [PERSON] [26] also define activity scores as a sensitivity metric on single parameters that, in many situations, are similar to other sensitivity metrics, such as Sobol' indices. Note that they are less versatile than Sobol' indices, because they require (approximations of) model gradients. Generally, gradients are not directly available for most quantities of interest in locomotion models, even for differential equation-based models. In the case of the Optimal Steps Model, we approximate gradients through finite differences.  
In this contribution, we combine all three aspects: we compute Sobol' indices as a robust and dependable single-parameter sensitivity metric. They serve as reference for the activity scores, where we need to assure that the approximation of the gradients is sufficiently accurate. Finally, we identify an active subspace of important parameter directions.  
From now on, the following nomenclature is used: we study a model \(f(x)\) that depends on a set of \(n\) input parameters \(x\in\mathbb{R}^{n}\). The output of the model \(y=f(x),y\in\mathbb{R}\) is our quantity of interest. Both of the methods require a scalar simulation output.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 3 Energy Management of an HRES Based on Deep Q-Network
### Markov Decision Process Model for the EMS
#### 3.2.3 Transition Probability  
Transition probability defines the probability that the agent moves from one state to another state. Given an action \(a_{t}\), where \(t\) denotes the current time step, the transition probability from a current state \(s_{t}\) to the next state \(s_{t+1}=s^{\prime}\) is denoted by \(P_{sg^{\prime}}^{a}\), that is [42]:  
\[P_{sg^{\prime}}^{a}=P\left[S_{t+1}=s^{\prime}|S_{t}=s,A_{t}=a\right] \tag{17}\]  
In model-based energy management approaches, the transition probabilities \(P_{sg^{\prime}}^{a}\) are estimated by Monte Carlo simulation based on the prior probability distribution, or they are predicted by a short-term prediction model. However, in a model-free approach such as the DQN algorithm, they are estimated through learning from data.
# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
###### Abstract  
The actor nodes are the spine of wireless sensor and actor networks (WSANs) that collaborate to perform a specific task in an unverified and uneven environment. Thus, there is a possibility of high failure rate in such unfriendly scenarios due to several factors such as power consumption of devices, electronic circuit failure, software errors in nodes or physical impairment of the actor nodes and inter-actor connectivity problem. Therefore, it is extremely important to discover the failure of a cut-vertex actor and network-disjoint in order to improve the Quality-of-Service (QoS). In this paper, we propose an Efficient Actor Recovery (EAR) paradigm to guarantee the contention-free traffic-forwarding capacity. The EAR paradigm consists of a Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the critical node. In addition, it replaces the critical node with backup node prior to complete node-failure which helps balancing the network performance. The packets are handled using Network Integration and Message Forwarding (NIMF) algorithm that determines the source of forwarding the packets; either from actor or sensor. This decision-making capability of the algorithm controls the packet forwarding rate to maintain the network for a longer time. Furthermore, for handling the proper routing strategy, Priority-Based Routing for Node Failure Avoidance (PRNFA) algorithm is deployed to decide the priority of the packets to be forwarded based on the significance of information available in the packet. To validate the effectiveness of the proposed EAR paradigm, the proposed algorithms were tested using OMNET++ simulation.  
Keywords:wireless sensor network; wireless sensor actor network; WSN; WSAN; RSSI; latency; actor; sensor; node failure; data recovery +
Footnote †: journal: Sensors
# Research on Mechanical Properties and Damage Constitutive Model of Water-Bearing Coal
## 1 Introduction
[28], [PERSON] et al. [29], and [PERSON] et al. [30] established the continuous damage constitutive models of red sandstone, shale, and sandstone considering the influence of water content. [PERSON] et al. [31] established a logistic equation-based continuous damage model, which can fit the uniaxial compression stress-strain curves of various types of rocks. [PERSON] et al. [32] established the two-stage discontinuous damage constitutive model of ordinary water-bearing coal recently. The two-stage model uses a piecewise function to describe the constitutive relationship of ordinary water-bearing coal, which has a better fitting effect than the traditional continuous model. Coal is a special soft rock with low strength [33], and the stress-strain curve of water-bearing coal with large porosity and high permeability used in this work is more obvious in the initial compaction stage due to the water-rock interaction, so the existing continuous model and two-stage discontinuous model are not fully applicable, and the theoretical value of the existing damage constitutive model will have a large deviation from the experimental value in the compaction stage. Therefore, this paper establishes a more accurate three-segment damage constitutive model of water-bearing coal with large porosity and high permeability to improve the fitting effect of the theoretical curve on the experimental curve. In this article, first, uniaxial compression experiments of raw coal specimens with five water contents are performed at four loading rates. Secondly, the stress-strain properties of water-bearing coal specimens under loading are investigated, and the link between water content and mechanical parameters of coal specimens is obtained. Finally, the piecewise water-bearing coal damage constitutive model is established, and the model is validatedby experimental data. The research results are predicted to supplement the rock damage mechanics theory and improve the safety of mining in coal mines.