esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Wireless Sensor Networks (WSNs) may be incorporated with thousands of small nodes. This gives them the capability to effectively sense, communicate, and compute parameters. However, the security and life span of a WSN node is a primary concern. This paper is focused on introducing a mathematical model of a modified Multitier Deterministic Energy-Efficient Clustering (DEC) based on novel election multi-tier random probability protocol for agricultural WSNs to enhance the life span of a WSN node along with a comparison of it with existing DEC protocol. In the proposed model, the selection of cluster heads, (CH), is done based on the energy drain pattern and location of the sensor nodes, which increased the lifespan of sensor nodes. In addition, several WSN probabilistic routing protocols to save energy throughout data transmissions like Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), DEC, and Stable Election Protocol (SEP) are explained. Moreover, it has been found that, after some mathematical modification in existing DEC routing, it will be capable to give a more positive result and reduce the energy drain in WSN nodes using a selective cluster head technique based on residual energy of sensor nodes. The DEC protocol is also compared with our proposed modified protocol for showing the energy-efficiency. The energy efficiency of clustering is associated with the field of energy sustainability of wireless sensor networks which is in the scope of Symmetry journal.  
wireless sensor networks (WSN); routing; energy-efficient; clustering +
Footnote †: journal: Computer Science and Engineering  
1
## 3 Methodology
### Complex Network Analysis and Girvan-Newman Method
#### 3.2.5 Betweenness Centrality  
The betweenness centrality of a node indicates how much the node is playing a role as an intermediate bridge in the network.  
\[c_{k}^{\text{between}}=\ \frac{2}{(N-1)(N-2)}\sum_{i=1}^{N}\sum_{j=1}^{i-1}\frac{ n_{i\to k\to j}}{n_{i\to j}} \tag{12}\]  
\(n_{i\to j}\) is the number of shortest paths from node \(i\) to \(j\) and \(n_{i\to k\to j}\) is the number of shortest paths from node \(i\) to \(j\) through node \(k\). Thus, the betweenness centrality \(c_{k}^{\text{between}}\) of a node \(k\) means the average of ratio \(n_{i\to k\to j}/n_{i\to j}\) for all node pairs \((i,j)\).
## 4 Results and Discussion
### Building Image Acquisition and Processing  
The data collection objects in this chapter are the dangerous engineering facilities and abnormal engineering facilities in the 300 M of Metro Line 9 as the buffer zone. The data mainly include the crack photos of the dangerous engineering facilities and abnormal engineering facilities and the distance between the camera and the engineering facilities when the crack photos are taken. When collecting data, it is necessary to collect the vertical distance between the camera and the target. Therefore, the lens and the body need to be adjusted to face the target without tilting, because tilt shooting will cause higher errors. While shooting the crack, the laser rangefinder measures the distance in real-time. Figure 9 shows the flight path of the UAV when collecting data. Among them, red engineering facilities are hazard monitoring points, green engineering facilities are abnormal monitoring points, and blue engineering facilities are safety monitoring points. The solid orange line indicates the flight route, starting from the Civil Engineering College and ending at Baishi Road. A total of 256 images were collected, and the pixels of this batch of images were unified to 4864 \(\times\) 3684 pixels. Then, the image is cropped, and the small images cropped from the same image will be placed in the same folder and processed for grayscale and image enhancement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multirow{2}{*}{**Metro Line**} & \multirow{2}{*}{\begin{tabular}{c} **Facility** \\ **Name** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Settlement** \\ **(mm/y)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Monitoring** \\ **or No** \\ **Monitoring** \\ \end{tabular} } & \multirow{2}{*}{**NO. of PS Points**} \\ \hline
1 & Line 9 & \
\begin{tabular}{c} Zhigong \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Zhiteng \\ Building \\ Southern \\ \\ \end{tabular} & \multirow{2}{*}{
\begin{tabular}{c} **-25.554718** & Yes & 44 \\ **-27.472183** & Yes & 57 \\ \(-28.976548\) & Yes & 31 \\ \(-35.676012\) & Yes & 526 \\ \(-6.054376\) & No & 89 \\ \(-29.384743\) & Yes & 25 \\ \(-26.788934\) &
## 5 Benchmark Testing and Performance Analysis of MRSO
### Experimental Configuration and Evaluation Methods
#### 5.1.2 Assessing Stability with Standard Deviation  
Standard deviation (std.) measures the extent of variation or dispersion within a set of values and is crucial for assessing the stability of an optimization algorithm. A lower standard deviation across multiple runs suggests that the algorithm consistently produces similar results, demonstrating both effectiveness in finding good solutions and reliability in maintaining performance. Thus, standard deviation is a vital metric for evaluating the stability and consistency of an algorithm's performance [34; 35].
## 1 Introduction
The SFX approach relies on collecting many stationary diffraction images of micrometre-sized crystals in a distribution of random orientations. Typically, many thousands of individual crystals are measured. Scaling and merging techniques then find the magnitudes of the structure-factor amplitudes that are used for structure solution. Importantly, this analysis is also generally applied to time-resolved, pump-probe SFX data. The different crystal orientations, however, result in differences of linear and non-linear population dynamics, such that the usual methodology, which averages over all orientations, loses this information that is intrinsically present in the dataset. Furthermore, averaging suppresses the light-induced differences relative to orientations that maximise the interactions. Such phenomenon can be particularly relevant for low quantum yield (<10%) reactions where the signal to noise ratio is a significant issue. Another observation is that, since SFX datasets already contain orientational information for each indexed frame, the methodology we propose can be retroactively applied to previously collected datasets. A general method has previously been proposed for the calculation of populations from photoexcitation in oriented uniaxial crystals (which include trigonal, tetragonal, and hexagonal point-group symmetries) [12]. Here, we extend the analysis to include biaxial crystals and provide practical methods and a set of tools for decomposing the electric field of the pump laser and calculating the associated modified dipole projections. Furthermore, we provide practical guidance and methods to deal with crystal orientation, indicatrix determination, indexing, and molecular structure transforms, as well as solving ambiguities where these arise. We go on to describe and present a computational toolbox that combines birefringence decomposition and calculation with non-linear excitation theory. The toolbox is presented for the purpose that it can be applied to specific examples and systems in two-dimensional optical crystallography and in TR-SFX. The toolbox is provided as open source and is user friendly, including a graphical user interface and worked examples.
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 4 Discussion
### Elastic Modulus Evolution with Time of PHA  
The PHA fraction of P(3 HB-co-88% 3 HV) shows an elastic modulus of 708 \(\pm\) 78 MPa after seven days. This value, as expected, is lower than the elastic modulus usually exhibited by pure PHB: indeed, the increase of HV content allows a reduction of the elastic modulus. For example, in this work, P(3 HB-co-2% 3 HV) shows an elastic modulus of 1623 \(\pm\) 172 MPa. The introduction of 88 mol% leads to an average reduction of the elastic modulus of 915 MPa.  
In addition, the measure of the elastic modulus with time for the PHA fraction, P(3 HB-co-88% 3 HV), allows having indirect information about the crystallization kinetics of this sample. The gradual increase of the elastic modulus with time, from 217 \(\pm\) 31 MPa after one day, to 708 \(\pm\) 78 MPa after seven days, suggests that the crystallization kinetics are sluggish. This phenomenon is not uncommon for the PHA [42] and agrees with the thermal analyses. DSC studies, indeed, highlight the absence of crystallization in the second heating scan, indicating a long time required for the copolymer to form crystals [37].
## 5 Results
### Nominal System Response  
First, the developed control system is tested by applying it to the nonlinear model of the triple inverted pendulum given in Equation (3), with the nominal values of its parameters given in Table 1. The initial condition is 0.5 deg, 1 deg, and 2 deg for the first, second, and third angle, respectively. The three angles respond as shown in Figure 8, the phase-plane trajectories of the angles and their derivatives are shown in Figure 9, and the applied torques are shown in Figure 10. Please note that data tips in regular font correspond to hybrid H\({}_{\infty}\)/SMC system, while data tips in italic font correspond to the H\({}_{2}\) control system in all responses.  
Figure 6: Control system used in optimization.  
Figure 7: Cost-function convergence of PSO.  
Figure 8: Nominal system response: (**a**) first link’s angle; (**b**) second link’s angle; (**c**) third link’s angle.  
Figure 9: Nominal system phase-plane trajectory: (**a**) (\(\theta_{1}\), \(\dot{\theta}_{1}\)); (**b**) (\(\theta_{2}\), \(\dot{\theta}_{2}\)); (**c**) (\(\theta_{3}\), \(\dot{\theta}_{3}\)).  
The \(\theta_{1}\) and \(\theta_{2}\) absolute overshoot difference between the two control systems is 0.1 deg and 1.59 deg, respectively, while \(\theta_{3}\) overshoots 17.323 deg higher in the H\({}_{2}\) control system. The difference between the errors of the two systems is small: 0.062, 0.151, and 0.025 deg for \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\), respectively.  
The above phase-plane trajectories show the direct sliding nature of the states towards their desired equilibrium point, driven by the hybrid H\({}_{\infty}\)/SMC control law. The H\({}_{2}\) control system lacks this preference.  
The start-up torques \(t_{m1}\) and \(t_{m2}\) in the H\({}_{2}\) control system are 9.633 and 11.872 N.m times higher, respectively, than in the hybrid H\({}_{\infty}\)/SMC system. On the other hand, the differences in steady torques are 0.153 and 0.404 N.m.
## 3 Results
### Numerical Simulations and Data Fitting
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Symbol** & **Description** & **Value (Unit)** & **Comments/Source** \\ \hline \(N^{*}\) & Cell density at equilibrium & \(4.5929\times 10^{11}\) (cells/lit) & [18] \\ \(\alpha\) & Basic AHL production rate & \(1.0564\times 10^{-7}\) (mol/(lit\({}^{2}\cdot\) h)) & \(=\alpha_{A}*N_{qui}\), [18] \\ \(\gamma\) & AHL decay rate (includes washout) & \(0.105\) (1/h) & \(=\gamma_{A}+D\), [18] \\ \(\delta\) & Lactonase-dependent degradation rate & \(1.500\times 10^{-4}\) (lit/(mol \(\cdot\) h)) & [18] \\ \(\beta\) & Feedback-regulated AHL production rate & \(1.0564\times 10^{-6}\) (mol/(lit\({}^{2}\cdot\) h)) & \(=\beta_{A}*N_{qui}\), [18] \\ \(n\) & Hill coefficient for \(x\) & \(2.3\) (dimensionless) & [18] \\ \(x_{th}\) & Critical threshold for positive-feedback in \(x\) & \(3.597\times 10^{-13}\) (mol/lit) & estimated \\ \(\omega\) & Lactonase decay rate (includes washout) & \(0.105\) (1/h) & \(=\gamma_{e}+D\), [18] \\ \(\rho\) & Lactonase production rate & \(5.0521\times 10^{3}\) (mol/(lit\({}^{2}\cdot\) h)) & \(=\alpha_{e}*N_{qui}\), [18] \\ \(\tau\) & Delay in the release of \(y\) & \(2\) (h) & [18] \\ \(m\) & Hill coefficient for \(x\) & \(2.5\) (dimensionless) & [18] \\ \(y_{th}\) & Critical threshold for positive-feedback in \(y\) & \(3.597\times 10^{-13}\) (mol/lit) & estimated \\ \(x_{0}(t)\) & AHL concentration (initial data) \(t\in[18,20]\) & \(5.4044\times 10^{-7}\) (mol/lit) & mean of exp. data \\ \(y_{0}(t)\) & Lactonase (initial data) \(t\in[18,20]\) & \(5.2\times 10^{3}\) (mol/lit) & estimated \\ \hline \end{tabular}
\end{table} 
Table 1: Variables and parameters in model (8), with values used for data fit in Figure 3.  
Figure 2: Experimental data and numerical solution of the mathematical model (4). Picture adapted from [18]. Copyright 2014, Springer-Verlag Berlin Heidelberg. The cell population reaches its equilibrium after approximatively 20 h from the beginning of the experiment.
## 3 The VTP-GCN Model
### Vehicle Trajectory Prediction  
Previous vehicle trajectory prediction networks, such as CS-LSTM, GRIP, and GSTCN, use LSTM or GRU-based encoder-decoder structures to encode the interaction information, and then to predict the future position coordinates of the target vehicles by step-by-step inference. However, these RNN-based prediction methods parameterize the output with certain vehicle trajectory prediction errors in the previous prediction time step into the current prediction time step, leading to a prediction error accumulation problem during the inference phase. To minimize this problem, we constructed a multi-vehicle trajectory prediction module based on a fully convolutional operation whose input \(H_{s-t}\in\mathbb{R}^{C^{\prime}\times N\times t_{h}}\) was generated from \(h_{s-t}\) by the transposition of dimensions. Through this module, future trajectories of vehicles can be generated by one forward step, simultaneously avoiding cumulative error spreading. The operations of the prediction module in the temporal dimensions are shown in Figure 4.  
Figure 4: An illustration of the vehicle trajectory prediction module.  
Similarly to previous papers [15; 21], we assumed that the predicted trajectory coordinates at the time step \(t_{h}+t^{\prime}\) of the vehicle \(i\) followed a bivariate Gaussian distribution:  
\[(x^{i}_{t_{h}+t^{\prime}},y^{i}_{t_{h}+t^{\prime}})\sim\mathcal{N}(\hat{\mu}^{i }_{t_{h}+t^{\prime}},\delta^{i}_{t_{h}+t^{\prime}},\hat{\rho}^{i}_{t_{h}+t^{ \prime}}) \tag{17}\]  
where \(\hat{\mu}^{i}_{t_{h}+t^{\prime}}=\left(\hat{\mu}_{x},\hat{\mu}_{y}\right)^{i}_{ t_{h}+t^{\prime}}\) is the mean, \(\hat{\sigma}^{i}_{t_{h}+t^{\prime}}=\left(\sigma_{x},\hat{\sigma}_{y}\right)^{i }_{t_{h}+t^{\prime}}\) is the standard deviation, and \(\hat{\rho}^{i}_{t_{h}+t^{\prime}}\) is the correlation. Hence, our prediction module was trained by minimizing the negative log-likelihood loss:  
\[Loss(\text{W})=-\sum_{i=1}^{N}\sum_{t=t_{h}+1}^{t_{h}+t_{f}}\log(\mathbb{P}(x ^{i}_{t},y^{i}_{t}\Big{|}\hat{\mu}^{i}_{t},\phi^{i}_{t},\hat{\rho}^{i}_{t})) \tag{18}\]  
where W denotes all trainable parameters in the model.
## 5 Case Study
### Simulation Results
When the wind forecast error is large, there should be DR participation because of the AGC units' output restrict. If the generator ramp constraint is not considered, the AGC units can provide an adequate up and down reserve, which is not correct in reality. The results are indicated in Figure 11 compared with taking the ramp constraint into account. After the wind accommodation is maximized with the combination of generators and smart appliances, the line power varies and may be beyond the limit. Table 2 shows the line active power without and with power flow transmission constraint at a given time. As shown in Figure 4, the branches 15-21, 18-21, 19-20, and 20-23 are double circuit lines. Therefore, their power limit and line active power are depicted as the corresponding parameter of the single line multiplied by two in Table 2. It can be seen that the active power of branch 14-16 shown in red frame exceeds the power limit in the condition of not considering the power flow transmission constraint. Figure 10: Comparative results: (**a**) load response results with time-varying DRP; and (**b**) load response results with constant DRP. considering the power flow transmission constraint, in which the generator regulating capability is abundant to balance the fluctuation. Therefore, it is indispensable to consider the power flow transmission constraint in the process of optimizing the utilization of DR.
# A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
## 5 Performance Evaluation  
### Experimental Setup  
The proposed HBMMO was implemented by conducting simulation experiments using an extension of CloudSim [41] called the WorkflowSim-1.0 toolkit [42], which is a modern framework aimed for modeling and simulating scientific workflow scheduling in cloud computing environments. It provides a higher layer of workflow management and also adds functionalities required to support the analysis of various scheduling overheads. Table 3 gives the parameters used in the simulation setup.  
Experimentation was carried out with different real workflow applications published by Pegasus project, including Montage, CyberShake, Epigenomics, LIGO Inspiral Analysis, and SIPHT [43; 44]. Montage is an input/output (I/O)-intensive astronomical application for constructing custom mosaics of the sky. CyberShake is a data-intensive application for generating probabilistic seismic hazard curves for a region. Epigenomics is a CPU-intensive workflow for automating various operations in genome sequence processing. LIGO Inspiral Analysis is a CPU-intensive workflow used for gravitational physics. SIPHT is a computation-intensive workflow used in bioinformatics for automating the search for untranslated RNA (sRNA) encoding-genes for bacterial replicons. Datasets of all the mentioned workflows are provided in the form of DAX files ([[https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator](https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator)]([https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator](https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator))) in XML format. They are later converted to DAG-based workflows by using workflow management system framework tools, such as Pegasus [44]. Figure 3 shows the simplified representations of small instances of workflows used in our experiments and the characteristics of these workflows are presented in Table 4.
## 2 Overview of Study Area, Data, and Methods
### Data Sources and Processing
Land use data: The land use data for Heilongjiang Province in 2020 (Figure 1) (1 km \(\times\) 1 km) came from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)), accessed on 28 July 2021). In this paper, referring to the land type classification standard of the Chinese Academy of Sciences, the study area's land use/cover was reclassified into two categories: forest land and other land. The summary of each variable and the descriptive statistical analysis are shown in Table 1.
## 3 Results and Analysis  
### Low-Temperature Decomposition Process of AP  
Figure 4 shows the typical state of the pore distribution at different mass losses. When the mass loss reached 9.5% (a), the pores were mainly concentrated near the surface of the particles, with no pores in the core of the particles. The pores connect to each other across a small range but are still in a separate state as a whole, which can be seen from the colour distribution. As the decomposition continued and the mass loss reached 20.2% (b), the pores had spread to the centre of the particles, with most pores being connected, although a few pores were still locally connected. When the mass loss reached 29.9% (c), the pores greatly increased, with almost all the pores being connected. As seen from the evolution of  
Figure 3: Nano-CT images of the AP pore structure.  
the pores, the low-temperature decomposition of AP particles began near the surface of the particles and from multiple points. During the heating process, they gradually expanded to the surrounding material and towards the centre. Then, local thermal decomposition occurred inside the particles.  
Combined with the mass loss process, the low-temperature decomposition of AP can be divided into three stages, as shown in Figure 5. The first is the nucleation and phase interface reactions (I), where the AP has no mass loss. Second, a rapid reaction phase (II) starts from surface decomposition and produces gas, forms pores, and extends to the centre of the particles. This stage corresponds to the linear descending segment of the TG curve, with the mass loss increasing linearly over time. Finally, local decomposition occurs within the particles (III) when the decomposition slows, and the mass loss decreases until decomposition stops, which corresponds to the last slow decline stage of the TG curve.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Challenges in C-RAN  
There are two major challenges in C-RAN architecture, namely the fronthaul capacity issue and complications of virtualization techniques [34]. The practical fronthaul is often capacity, and time-delay constrained that significantly reduces spectral efficiency and energy efficiency gains [35]. The use of virtualization techniques in cellular networks like C-RAN is far more complicated due to the unique features of wireless communication. Two RAN architectures (Heterogeneous C-RAN and Fog RAN), for compensating the challenges in C-RAN, are proposed and evaluated in the literature. This section provides a brief discussion of these two architectures.
## 3 Experiments and Results
### Empirical Properties of the PINN  
For the problem defined by Equations (24) and (25), which has an analytical solution, we performed a study to quantify the training time and the predictive error for two different variables: one is the network's size and the other is the number of samples. Some empirical conclusions will be drawn. Specifically, the neural network's size includes the network's width and depth; the width refers to the number of each layer's neurons; and the depth refers to total number of hidden layers. In the following, we will study each of these two variables while the other variable value is set at a reasonable value. Hyperparameters that are not studied in this work are set to be the same as in the previous section. For each combination, we set the maximum epoch to \(20,000\) and the cut-off threshold for the loss function to \(1\times 10^{-3}\). The root mean squared error (RMSE) is used to evaluate the prediction error. Considering \(u_{i}\) as the analytical solution, \(\hat{u}_{i}\) as the neural network's predictive value, and \(n\) as the number of test points, the RMSE is given by  
\[RMSE(u,\hat{u})=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(u_{i}-\hat{u}_{i})^{2}}. \tag{27}\]  
Figure 8: (**a**) The mesh diagram of the model; (**b**) the gradient modulus comparison between the first-order FEM and the analytical result.  
Figure 9: The gradient modulus comparison between the PINN result and the analytical result.