esa-sceva/satcom-chunk-collection huggingface dataset:
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 5 Simulations and Results Analysis
### Performance Analysis
Note that RO-DDPGRA achieves higher satisfaction for tasks offloaded than the DQNO-ARA; yet, due to the randomness in the offloading decisions, the overall rate of satisfied tasks of the RO-DDPGA is constrained and falls below that of the DQNO-ARA method.
# NMR and LC-MS-Based Metabolomics to Study the Effect of Surfactin on the Metabolome of Flax
## 4 Conclusions  
In this research, we used advanced metabolomic methods, including \({}^{1}\)H NMR and LC-MS, to assess the effects of surfactin on the metabolic composition of flax (_Linum usitatssimum_) roots, stems, and leaves. Our results indicate significant changes to both primary and secondary metabolic pathways.  
The capacity of flax roots to take up surfactin was demonstrated. It was found that surfactin treatment induced a local effect in this organ with an increase in amino acids and cyanogenic glycosides and a decrease in carbohydrate content.  
Surfactin treatment also induced a systemic effect demonstrated by the metabolic changes in the aerial parts of the plant. The leaves and stems presented an increase in several amino acids and carbohydrates. Additionally, we observed a decrease in the metabolites associated with the phenylpropanol pathway, including flavonoids.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app142411999/s1](https://www.mdpi.com/article/10.3390/app142411999/s1)]([https://www.mdpi.com/article/10.3390/app142411999/s1](https://www.mdpi.com/article/10.3390/app142411999/s1)), Table S1: NMR chemical shift and/or retention time and m/z value of the signal used for semi-quantitation of the different discriminant metabolites present in the different organs of flax grown in control conditions or with surfactin-treatment.  
Conceptualization, O.F., M.O. and F.M.; methodology, O.A.B., D.H., R.M. and J.-X.F.; software, O.A.B., D.H. and J.-X.F.; validation, O.F. and F.M.; formal analysis, O.A.B. and R.M.; investigation, O.A.B., M.C., D.H. and A.S.; resources, M.O.; writing--original draft preparation, O.A.B. and O.F.; writing--review and editing, O.F. and F.M.; supervision, O.F., M.O. and F.M.; project administration, O.F., M.O. and F.M.; funding acquisition, O.F. and F.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Region Hauts de France and the University of Picardie Jules Verne, grant number 20003876.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
### Geometric Characteristics
#### 3.3.1 Fourier Descriptor  
Fourier descriptors are used as distinctive characteristics to create the outline of an image, thereby functioning as an image feature [28]. If we consider the goal shape profile as a closed curve, and a point \(p(i)\) moves along this boundary curve with its complex coordinates \(x(i)+iy(i)\), the period is determined by the perimeter of the closed curve. The given function can be represented mathematically as a Fourier series. Fourier descriptors refer to the coefficients \(z(k)\) in the Fourier series that are defined as many coefficients. By choosing a coefficient term of \(z(k)\) significant magnitude, the Fourier descriptor is capable of completely extracting contour details and reconstructing the contour of the object.  
Let us assume that the boundary curve of the cucumber target is represented by a sequence of coordinates \(s(k)=[x(k),y(k)]\). Each pair of coordinates is represented as a complex number, as shown in Equation (1).  
\[s(k)=x(k)+iy(k),k=0,1,2\cdots K-1 \tag{1}\]  
In the context of the complex sequence \(s(k)\), the horizontal x-axis corresponds to the real axis, while the vertical y-axis corresponds to the imaginary axis. Since the boundary curve of the cucumber target repeats itself every T units, the sequence of boundary coordinates can be regarded as a one-dimensional signal. The one-dimensional discrete Fourier transform (DFT) is defined according to Equation (2).  
\[a(u)=\sum_{k=0}^{K-1}s(k)e^{-i2\pi uk/K},\ u=0,1,2,\cdots K-1 \tag{2}\]  
The coefficient \(a(u)\) with complex values in Equation (2) is known as the Fourier descriptor of the border.  
By applying an inverse Fourier transform to the complex coefficients \(a(u)\), it is feasible to retrieve the original signal \(s(k)\), specifically the coordinates of the boundary points of the target's set. By utilizing the initial P coefficients, Equation (3) demonstrates the approximation of the original boundary.  
\[s(k)=\frac{1}{K}\sum_{u=0}^{P-1}a(u)e^{i2\pi uk/K},\ k=0,1,2\cdots K-1 \tag{3}\]
## 3 Methodology
### DGCNN, PointCNN, and PAConv  
Dynamic Graph CNN (DGCNN) [55] architecture constructs the point cloud data into a graph structure, where points serve as nodes, and the edges are dynamically built based on spatial relations between points. This method effectively tackles challenges such as the disordered nature and non-Euclidean structure of point cloud data. Its core operation, EdgeConv, can generate effective local neighborhood feature representation for each point.  
Figure 3: Visualization of selected point clouds from our dataset. (**a**) Cotton point cloud scanned by TLS in the field. (**b**) Maize point cloud scanned by TLS in the field. (**c**) Maize point cloud derived from UAV images. (**d**) Maize plot point cloud collected by UAV-LS. (**e**) Individual maize plant segmented from (**d**). (**f**) Rapeseed point cloud obtained by TLS outdoors.  
Specifically, the EdgeConv operation encompasses two key steps: computation of edge features and aggregation of these edge features. Edge feature computation defines edge features as a function of the relationships between a point and its k-Nearest Neighbors. The arguments of these functions are learnable, permitting the precise capture of local structure information in the point cloud. Edge feature aggregation utilizes a symmetrical function across a channel (max pooling) to aggregate the edge features and re-represents the centroid's features based on these aggregated characteristics. This aggregation mechanism enables the model to incorporate the information in local neighborhoods, thus enriching and improving feature representation accuracy. A noteworthy trait of DGCNN is the dynamic recalculation of the graph structure in each layer's feature space, meaning the model builds the graph based not only on initial spatial relationships but also dynamically updates the graph structure in each layer according to nearest-neighbor relationships in the feature space.  
Next, the PointCNN [57] architecture emulates the characteristics of 2D convolution, constructing a core X-Conv operation, and mimicking the convolution layer in two-dimensional models. Consequently, 3D point cloud data can conduct feature extraction similarly to 2D images under the operation of X-Conv. Like PointCNN, PAConv [91] also follows the core point convolution concept, a plug-and-play convolution module. Its key idea is to construct its convolution kernel by dynamically combining the fundamental weight matrix in the weight library, where the assembly coefficients are learned adaptively from the relative position. Particularly, a ScoreNet assigns scores to the primary weight matrix, thereby assembling the new weight matrix; hence, it can adaptively assemble the corresponding weight matrix for different points, overcoming the limitation of poor flexibility in traditional core point convolution kernels. We amalgamate PAConv and DGCNN to validate the performance enhancement effect of PAConv on DGCNN.
# Use of Social Media Data in Disaster Management: A Survey
## 8 Applications
### Disaster Management Phases  
Disaster management phases describe the normal life cycle of a disaster and provide a useful framework for response [65]. As we have already established, there has been an increased use of social media in different phases of disaster management. Several techniques for the application of social media for disaster management have been proposed in the literature. As presented in Sections 6.1 and 6.2, social media data are usually generated with spatial and temporal information. Such information can be used to facilitate disaster management in different phases.  
* Mitigation--the actions for minimising the cause and impact of hazards and preventing them from developing into a disaster.
* Preparedness--the action plans and educational activities for communities to confront unpreventable hazard events.
* Response--the actions for protecting people's lives and property during hazards or disaster events.  
Figure 7: Dimensions of social media applications in disaster management.  
* Recovery--the actions for restoring damaged property and community infrastructures and for curing people of their illnesses.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### EEG Preprocessing and Generation  
In this section, we validate the proposed epilepsy detection framework using the publicly available Bonn EEG dataset. The following section details the EEG datasets, preprocessing steps, and data matrix generation methodology.
## 4 Computer Results and Simulations
The four EHO algorithms were tested against three other algorithms at different irradiance levels and temperatures for more testing. Table 7 shows the extracted parameters for the seven algorithms at different irradiance levels and temperatures. Finally, the three-diode model is tested against three algorithms from [43] (Moth-Flame Optimizer (MFO), FPA, and Hybrid Evolutionary algorithm (DEIM)). the RMSEs for each algorithm at varying irradiance levels are listed in Table 8. Again, at low radiation with 109.2 W/m2, CEHO outperforms EHO with a slightly small difference but a big difference compared to other algorithms. CEHO outperformed other algorithms at other radiations, and BIEHO's results were slightly different from CEHO's. The superiority of the CEHO algorithm is proven as the best compared with the other three variants and the other three algorithms for all irradiance levels. Figure 10 shows that calculated data fit the I-V curve of measured data for CEHO. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**EHO**} & \multicolumn{4}{c}{**EHO Variants**} & \multirow{2}{*}{**ABSO**} & \multirow{2}{*}{**HS**} \\ \cline{3-3} \cline{5-6}  & & \(\alpha\)**EHO** & & & & & \\ \hline \(I_{ph}(\text{A})\) & 0.76079 & 0.7607 & 0.76077 & 0.76079 & 0.76078 & 0.76176 \\ \hline \(I_{d1}(\mu\text{A})\) & 0.19895 & 0.23015 & 0.470885 & 0.294513 & 0.26713 & 0.12545 \\ \hline \(I_{d2}(\mu\text{A})\) & 0.25005 & 0.22753 & 0.258635 & 0.478734 & 0.38191 & 0.25470 \\ \hline \(R_{s}(\Omega)\) & 0.0367292 & 0.036594 & 0.036595 & 0.036591 & 0.03657 & 0.03545 \\ \hline \(R_{sh}(\Omega)\) & 53.47509 & 54.0848 & 54.85623 & 53.415705 & 54.6219 & 46.8269 \\ \hline \(n1\) & 1.44596 & 1.45601 & 1.994023 & 1.47250 & 1.46512 & 1.49439 \\ \hline \(n2\) & 1.69709 & 1.73558 & 1.462378 & 1.98067 & 1.98152 & 1.49989 \\ \hline \((RMSE)F\) & \(9.876\times 10^{-4}\) & \(9.853\times 10^{-4}\) & \(9.830\times 10^{-4}\) & \(9.852\times 10^{-4}\) & \(9.834\times 10^{-4}\) & 0.00126 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison between EHO algorithms, ABS, and HS for double-diode solar cells. Figure 7: Convergence rates of EHO and its variants. (**a**) single diode. (**b**) double diode. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \(\mathbf{V_{t}}\) **(v)** & \(\mathbf{I_{t}}\) **(A) Measured** & \(\mathbf{I_{ph}}\) **(A) Calculated** & **Relative Error** \\ \hline  
(-\)0.007552 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The relative error for 26 measurements (single diode) with CEHO. Figure 8: Measured power vs. calculated by CEHO. **(a)** single diode. **(b)** double diode. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \(\mathbf{V_{t}}\) **(v)** & \(\mathbf{I_{t}}\) **(A) Measured** & \(\mathbf{I_{ph}}\) **(A) Calculated** & **Relative Error** \\ \hline  
0.001582\) \\ \hline \end{tabular}
\end{table} 
Table 6: The relative error for 26 measurements (double diode) with CEHO. Figure 9: Measured current vs. calculated by CEHO. (**a**) single diode. (**b**) double diode.
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 4 Test Procedure
### Simulation  
As the corrections in the scheme have a time dependency associated with them (the correction values age based on changes in common mode noise like changes in atmospheric conditions). We performed simulations to evaluate this growing uncertainty of the corrections over time. The simulation involves 2000 data points for each of the FAGPS and non-FAGPS schemes. These data points were simulated to contain both elements of Gaussian noise (\(Z_{ix}\) and \(Z_{iy}\) simulating the general noise in GPS signals) and Gaussian noise with a time dependency (\(Z_{itx}\) and \(Z_{ity}\) simulating the aging of correction data).  
Hence, the FAGPS (3) and non-FAGPS (4) data sets are computed as follows:  
\[FAGPSx_{i} =Z_{ix}+Z_{itx}\] \[FAGPSy_{i} =Z_{iy}+Z_{ity} \tag{3}\]  
\[GPSx_{i} =Z_{ix}+Z_{itx}+x_{0}\] \[GPSy_{i} =Z_{iy}+Z_{ity}+y_{0} \tag{4}\]  
where \(Z_{ix}\) and \(Z_{iy}\) are Gaussian noise with \(\mu=0\) and \(\sigma^{2}=0.5\). \(Z_{igx}\) and \(Z_{igy}\) are time increasing Gaussian noise variables with \(\mu=0\) and \(\sigma^{2}=i/1000\). Finally \(x_{0}=0.75\) m and \(y_{0}=0.5\) m and are added onto the non-FAGPS data set to show an initial error offset corrected by the FAGPS scheme.
# A System for Controlling and Monitoring IoT Applications
## 3 Block 1  
### Control Unit  
The control unit (CU) must be able to work as sensor master and control any other device which the installation requires. It must govern the installation and supply the data for users and it has to execute the requested tasks. To achieve this, it is important to have a small size, powerful, low consumption and a low cost device, with a single board. Several devices have been studied [7]. Taking into account the performance/cost ratio, the Raspberry Pi was chosen. In particular, the Raspberry Pi (RPi) used in this work includes a system-on-a-chip (SoC) Broadcom BCM2835, a CPU ARM11 76 JZF-S running at 700 MHz, a graphical processor unit (GPU) VideoCore IV, and 512 MB of RAM memory. The device does not include a hard disk or solid state disk by default, instead it uses an SD card for the operating system and for non volatile storage.  
The free open source Raspbian Operating System (OS) from the Raspberry Pi official web [8] was chosen for this work. Based on Debian (Linux) [9] this OS was optimised for the RPi hardware. It offers more than 35,000 packages and programs, many of which are necessary to develop this work.  
It is possible to have access to the Rpi without needing a screen, a mouse or a keyboard connected through secure shell (SSH) from other devices such as laptops, PCs or mobile phones. The peripherals must be compatible with the device. There is a large list available on the Internet [10] with a large variety of brands and models. The incompatibility problems mainly come from the power supply and the SD card. In this work, a 5 V and 1 A Samsung power supply, and a LEXAR SDHC memory card were selected.  
The RPi is connected to the Internet through a high-gain wireless USB adapter. It is a TP-LINK TL-WN722N device which allows a connection of up to 150 Mbps, compatible with the IEEE 802.11n standard, with a 4 dBi outdoor-gain antenna. In addition, this device provides WPA/WPA2 encryption.  
To configure a static IP, we have to take into account the Raspbian OS which uses dynamic host configuration protocol (DHCP) to assign a static IP to the RPi. Therefore, we have to specify a static IP, the netmask and the gateway for the eth0 interface. After that, the last step is configuring the domain name system (DNS) server. With the objective of increasing the security of the connection, it is better to avoid using default values for the ports and other parameters.
# On Hybrid Nanogrids Energy Management Systems--An Insight into Embedded Systems
## 3 Nanogrid EMS
### Overview of EMSs
In another study, an optimization algorithm for a microgrid system is proposed, containing a wind turbine, a micro-turbine, a diesel generator, a photovoltaic generator, a fuel cell, and a storage battery [71]. Some studies have considered a sailing boat based on hybrid sources, designed to carry out trips for 6 months [72]. The aim of this project was to study the relevance of coupling renewable energy sources with hydrogen storage. In other studies, an electric boat based on hybrid sources and storage systems such as batteries and fuel cells was developed to replace the diesel generator [73; 74]. In the field of electric vehicles, EMS is mainly used to reduce energy and fuel consumption. In a study [46], a non-linear optimization algorithm was applied to minimize the fuel consumption of an electric vehicle powered by dihydrogen. The constraints used in this study are limited to the equilibrium of the electric microgrid, which allows the use of a non-linear solving method. It is more accurate but slower than other methods. The optimization parameters are also optimized via neural networks to improve the solution obtained over predefined driving cycles.
## 2 Background
### Long Short-Term Memory  
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to model sequential data [24]. It addresses the vanishing gradient problem of traditional RNNs by introducing gating mechanisms to control the flow of information. The LSTM unit consists of several gates, including an input gate (\(i\)), forget gate (\(f\)), output gate (\(o\)), and cell state (\(C\)). Activation functions control these gates and learn to regulate the flow of information through the cell. The Equations (3)-(8) governing LSTM operations are as follows:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{3}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{4}\]  
\[\tilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{5}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t} \tag{6}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{7}\]\[h_{t}=o_{t}*\tanh(C_{t}) \tag{8}\]  
The hidden state at time \(t\) is denoted as \(h_{t}\), the input at time \(t\) is denoted as \(x_{t}\), the sigmoid activation function is denoted as \(\sigma\), element-wise multiplication is denoted as \(*\), and the weight matrices and bias vectors are denoted as \(W\) and \(b\), correspondingly. These parameters are learned during training. This method utilizes Long Short-Term Memory (LSTM) networks to advance the field of fault prognosis for rolling element bearings, which are crucial components in industrial setups [24]. The model achieves improved performance by directly importing raw time series sensor data, minimizing the need for feature engineering. LSTM, or Long Short-Term Memory, allows the model to surpass other models that rely on conventional characteristics, demonstrating its ability to accurately forecast faults [24]. Moreover, using unprocessed vibration data improves the model's capacity to make accurate predictions across different types of energy sources, including hydro and wind power turbines, demonstrating the robustness of LSTM in proactive, predictive maintenance applications [24].
## 5 Swarm-Based Solution
### Optimal Sink Selection Problem
#### 5.1.2 Calculation of the Heuristic Information  
Since it is essential to make the data transmission invulnerable to environmental impact, the data messages should be transmitted on a safe path away from the dangerous area. So, the environmental impact metric is considered as heuristic information that helps select the sink node under suitable environments and bypass the dangerous areas to a certain limit. The selection of the sink node with maximum environmental impact is required to lower the impact of the external environmental factors on the data transmission. Thus, the final environmental impact metric of sink node \(s_{i}\) in zone \(i\) is used as heuristic information as follows:  
\[\eta_{s_{i}}^{1}(t)=\frac{EIM_{s_{i}}(t)}{\sum\limits_{l\in FSN^{t}}EIM_{l}(t)} \tag{64}\]  
The candidate sink node with a higher \(\eta_{s_{i}}^{1}(t)\) value has less negative impacts on packet delivery and will be selected as the optimum sink.  
Any sink node with heavy weight should not be selected to balance sink node traffic and prolong the network lifetime. As a result, while choosing the sink node, the load metric of sink nodes is used as heuristic information, as demonstrated by:  
\[\lambda_{s_{i}}^{1}(t)=\frac{\sqrt[]{\textit{SLM}_{s_{i}}(t)}}{\sum\limits_{l \in FSN^{l}}\sqrt[]{\textit{SLM}_{s_{i}}(t)}} \tag{65}\]  
The candidate sink has a higher \(\lambda_{s_{i}}^{1}(t)\) value, implying it has less load and will be picked more often.
###### Abstract  
The radiometric properties, including the extinction efficiency, absorption efficiency, scattering efficiency, and asymmetric parameter values of particle aggregates consisting of multiple chemical components are critical in industry and nature. This article aims to analyze the influence of chemical component distribution on these radiometric properties. The particle aggregates are generated by a diffusion-limited aggregate method by which spherical particles are stuck together randomly. The particle aggregates have two components with a major component of a fixed refractive index and a minor component of a changed refractive index. The radiometric properties are calculated using a multi-sphere T-matrix (MSTM) method for particle aggregates with different particle radii and with refractive indices, distributions of components, and volume fractions of the minor component. The results show that the chemical component distribution influences the radiometric properties of the particle aggregate. Evenly spreading the strong absorptive minor component into each particle, compared to concentrating it in a few particles, can raise the absorption efficiency, reduce the scattering efficiency, and ultimately reduce the extinction efficiency of the aggregate. For aggregates with major and minor components in different particles, a similar effect is shown when spreading the minor component particles evenly in the aggregate relative to gathering them in one part of the aggregate.  
radiometric properties; particle aggregate; MSTM; chemical component +
Footnote †: journal: Applied Physics Letters