esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 2 Fundamentals of LoRaWAN Technology
### LoRaWAN Limits  
LPWAN technologies are often requested for their large-scale and low-cost coverage benefits. As they are designed for this purpose, thousands of nodes with varied application requirements can be requested. From the above, we can summarise the main factors impacting the performance of LoRaWAN in three points:  
* The radio channel degradation: the CSS modulation uses a constant bandwidth for signal broadcasting [6], which makes the latter more robust against phenomena related to the propagation of electromagnetic waves. Despite this advantage, the various phenomena related to the propagation [7] of electromagnetic waves, the frequency collision, and the Doppler effect, significantly degrade the network QoS using this technology.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-Band** & **Freq. Range (MHz)** & **Condition (Pwr/DC)** \\ \hline g & 863–868 & 14 dBm@1\% or LBT + AFA \\ g1 & 868–868.6 & 14 dBm@1\% or LBT + AFA \\ g2 & 868.7–869.2 & 14 [EMAIL_ADDRESS]\% or LBT + AFA \\ g3 & 869.4–869.65 & 27 dBm@1\% or LBT + AFA \\ g4 & 869.7–870 & 14 dBm@1\% or LBT + AFA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frequency plan sub-band g EU868-SEMTECH with MTP and DC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Freq. (MHz)/BW** & **Condition (Pwr/DC)** & **Orthogonal Spreading Factors** \\ \hline
868.10 (g1)/125 kHz & 14 dBm@1\% & 7–12 \\
868.30 (g2)/125 kHz & 14 dBm@1\% & 7–12 \\
868.50 (g3)/125 kHz & 14 dBm@1\% & 7–12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: LoRaWAN default channels.  
* ISM band regulations: as detailed above, LoRaWAN is subject to strict regulations regarding the occupation time of the ISM band. These regulatory limits constitute a blocking factor for a traffic-oriented network, which is typical for a network in which the packet transmission sequence per node is very high and counts in thousands for a limited number of gateways. For example, for the g1 band, with a maximum spreading factor and a bandwidth of 125 kHz, the number of messages sent in all channels per minute is around four. Even if the LBT and AFA make it possible to bypass the limitation rules, these mechanisms are still not well documented by SEMTECH.
* The end devices (EDs) and gateway capacity: gateways play an essential role in the LoRaWAN architecture. For networks with constrained nodes that do not support the IP stack, such as class 0 objects governed by IETF RFC 7228 [8], the gateway is the essential element in the chain. For a network using objects of this class, the network's capacity can be reduced to the hardware limits of the latter. De facto, the channels' saturation at the gateway level will be characterized by congestion, which will generate a loss of packets on reception, proportional to the traffic. Such observation has been demonstrated in several works such as in [9; 10; 11]. This problem persists even for commercial gateways supporting eight channels of parallel communications.
## 2 Experimental Setup
### Synthesis and Modification of Au NPs with Capping ligands Preparation  
The citrate-stabilized Au NPs were synthesized by using the Grabar method [22]. Briefly, 10 mL of 38.8 mM sodium citrate dihydrate at 50-60 \({}^{\circ}\)C were added to 100 mL of 1 mM HAuCl4 under vigorous stirring at boiling point to form a ruby-red-colored solution after a series of color changes. The observed color change was caused by the Surface Plasmon Resonance (SPR) of the forming Au NPs. The solution was cooled to room temperature. The pH was adjusted to 7.4 using 0.5 M NaOH and the solution was filtered through a 0.22 um pore diameter Millipore syringe filter to remove unreacted salts. The filtrate was stored at 4 \({}^{\circ}\)C until required. All glassware and magnetic stirrer bars used in the syntheses were thoroughly cleaned in aqua regia (HCl/HNO3 3:1, \(v/v\)), rinsed in distilled water, and oven-dried prior to use.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Sequence** & **Number of Amino Acids** \\ \hline C/G dipeptide & Cys-Ala-Leu-Asn-Asn and Gly-Pro-Asp-Cys & 5 and 4 \\ GPG-EN-PEG4-LA & Gly-Pro-Gly-ethylenediamine-PEG4-lipoamide & 3 \\ GPDCALNNC & Gly-Pro-Asp-Cys-Ala-Leu-Asn-Asn-Cys & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peptide sequences trialed to enhance gold nanoparticle (Au NP) stability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Ligand Properties**} & \\ \hline  & C/G dipeptide & GPDCALNNC & GPG-PEG4-LA \\ \hline Binding Moiety & Sulfhydryl & Sulfhydryl & Disulfide \\ Molecular Weight & 390 (GPDC) 533 (CALNN) & 906 & 706 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline  & Au@C/G dipeptide & Au@GPDCALNNC & Au@GPG-PEG4-LA \\ \hline Diameter (nm) & 14.3 \(\pm\) 1.1 & 14.0 \(\pm\) 1.3 & 13.8 \(\pm\) 1.5 \\ \(\lambda_{\text{max}}\) (\(\Delta\lambda_{\text{max}}\) = \(\lambda_{\text{max}}\) - \(\lambda_{\text{original}}\)) & 524.7 (2.9) & 524.3 (2.5) & 523.5 (1.7) \\ Hydrated Diameter (nm) & 35.4 & 39.5 & 38.1 \\ Zeta Potential (mV) & \(-\)39 \(\pm\) 3.5 & 35.9 \(\pm\) 1.5 & \(-\)16.5 \(\pm\) 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of ligand and functionalized gold nanoparticle properties.
# Thermal Effect of the Back Radiation from Disk to Head after Laser Heating in HAMR
## 1 Introduction
For different theoretical models, the calculation methods of the near-field radiative heat transfer are different for different geometries, such as heat transfer between two close infinite bulks [15], a small spherical particle and a bulk [16; 17], two close infinite bulks with coats on the surface [18; 19], several arbitrary shape objects [20; 21], nanoparticles [22; 23], thin films [24; 25], and so on. The main difference between the calculation methods is focused on the method to solve the electric field and magnetic field on the target position, i.e., the solving methods of the stochastic the [PERSON]'s equations are different. The most common method to solve the [PERSON]'s equations is the discrete approximation method [26], such as the finite-different time-domain (FDTD) method, finite-difference frequency-domain (FDFD) method, boundary finite element (BEM) method, and the thermal dipole discrete approximation (T-DDA) method [20; 21]. The limitation of the existing discrete approximation method is that the calculation is of great complexity and time-consuming. Another mainstream method to solve the [PERSON]'s equations is to bring in the fluctuation-dissipation theory [27; 28], the main idea of which is to regard an object as a collection of many dipoles in constant motion and then to integrate over all sources in the volume of the object to obtain the electromagnetic field on the target position. For simple shape objects, the volume of the object is generally integrated directly, known as the fluctuate-volume-current (FVC) method [25; 28; 29]. And for complex shape objects, the volume integrals can be converted into surface integrals on the surface of objects to calculate the integral, known as the fluctuate-surface-current (FSC) method [28]. Back to the head/disk model discussed in this paper, the fluctuate-volume-current method can be chosen to calculate the near-field radiative heat transfer. The paper is constructed as follows: The theoretical analysis of the fluctuate-volume-current method to calculate the near-field radiative heat flux by the [PERSON]'s theory from disk to head is presented in Section 2. The properties of the imitating material are considered, and then the numerical results and discussion of the near-field radiative heat flux from disk to head in HAMR are given in Section 3. At last, the simulation results and discussion of the thermal deformation of the magnetic head are given in Section 4.
# Intelligent Detection of Parcels Based on Improved Faster R-CNN
## 3 Experiments
### Ablation Experiment  
To verify that edge detection and the Improving ROI Pooling scheme improve parcel detection accuracy, we compared the detection results of Faster R-CNN, Faster R-CNN + Edge Detection, and Faster R-CNN + self-attention ROI Align on the parcel dataset. Figure 15 depicts the corresponding results on some samples. For samples 1 and 3, Faster R-CNN presents some missed detections, while Faster R-CNN + Edge Detection and Faster R-CNN + self-attention ROI Align accurately detect all packages. Considering sample 2, although all methods present some false detections, the Faster R-CNN + Edge Detection and Faster R-CNN + self-attention ROI Align models have fewer false detections.  
Furthermore, to verify that edge loss accelerates network training convergence and reduces the network's loss values, we compared the RPN's loss values in Faster R-CNN (a) and Faster R-CNN + Edge Detection (b) during the training process. As shown in Figure 16, compared with the initial loss value of RPN in (a), which is about 2.47, the initial loss value of RPN in (b) is smaller, i.e., about 0.87. However, as the number of iterations increases, the loss value of RPN in (b) decreases rapidly, stabilizes at about the 8 th epoch, and finally converges, with a convergence value of about 0.027. The loss value of RPN in (a) decreases more slowly, stabilizes at about the 16 th epoch, and finally converges, with a convergence value of about 0.513. Therefore, adding the edge detection branch effectively accelerates the network training convergence and reduces the overall network loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **Re (\%)** & **Pr (\%)** & **AP (0.5) \%** & **FPS** \\ \hline VGG-16 & 84.64 & 88.71 & 88.59 & 32.74 \\ VGG-19 & 86.69 & 89.52 & 89.12 & 32.91 \\ ResNet-18 & 92.83 & 90.15 & 91.95 & 27.35 \\ ResNet-50 & 96.89 & 98.76 & 98.42 & 22.83 \\ ResNet-101 & 93.79 & 92.53 & 94.83 & 12.87 \\ MobileNet-V2 & 82.17 & 83.62 & 83.31 & 57.59 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of examination of test sets for different backbones.  
Figure 15: Ablation experiment results.  
Table 4 shows the results of the quantitative comparison between the three algorithms mentioned above and our method. After adding edge detection, the AP (0.5) improved by about 1.2%, the MD rate and FD rate decreased by about 1.7% and 1.3%, respectively. With the addition of the self-attention ROI Align module, the AP (0.5) improved by about 1.4%, and the MD rate and the FD rate were reduced by about 1.3% and 1.6%, respectively. In addition, our method improved AP (0.5) by 4.37% and reduced the MD rate and the FD rate by 2.14% and 1.8%, respectively, after fusing the Edge Detection and self-attention ROI Align module. This indicates that the two modules play a mutually reinforcing role in parcel detection.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Simulation of the Lattice Structure  
The transient thermal module of the commercial software Ansys Workbench 2023 R2 is employed for the simulation of the lattice structures and the equivalent solid blocks, which provides stable solutions to the 3D steady-state heat transfer problem. Four different cases are analyzed; all of them are printed in Inconel 718 and consist of a bcc lattice structure attached to a 2-mm-thick bottom plate. The bcc structure is selected, because it provides equal equivalent thermal conductivity in the X, Y, and Z directions when employing cubic cells [10], and it is possible to print it without support structures. The dimensions of the samples are indicated in Table 3. As an example, Figure 1a displays the geometry corresponding to case 1.  
A constant heat flux is introduced from the lower face of the bottom plate and convection losses are considered in all faces of the lattice structure; see Figure 1b. The model considers temperature-dependent properties, which are experimentally measured as indicated in Section 2.1.  
In the numerical simulations, unstructured tetrahedral meshes are generated for the lattice structures--see Figure 2a--whereas, for the solid block, hexahedral elements are  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Hatch distance [\(\upmu\)m] & 80 \\ Layer thickness [\(\upmu\)m] & 60 \\ Laser power [W] & 200 \\ Inert gas [-] & Argon \\ Scan speed (mm\(\cdot\)s\({}^{-1}\)) & 1250 \\ \hline \end{tabular}
\end{table}
Table 2: Printing parameters used in Inconel 718 sample manufacturing.  
Figure 1: (**a**) Design of the lattice cells, case 1, and (**b**) the applied boundary conditions for the simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Case** & \begin{tabular}{c} **Dimensions X \(\times\) Y** \\ \(\times\)**Z [mm\({}^{3}\)]** \\ \end{tabular} & **Fill \%** & \begin{tabular}{c} **Lattice Volume** \\ **[mm\({}^{3}\)]** \\ \end{tabular} & \
\begin{tabular}{c} **Baseline** \\ **Volume [mm\({}^{3}\)]** \\ \end{tabular} &
\begin{tabular}{c} **Solid Block** \\ **Volume [mm\({}^{3}\)]** \\ \end{tabular} \\ \hline
1 & 20 \(\times\) 20 \(\times\) 20 & 5.16 & 613.19 & 800 & 8800 \\
2 & 20 \(\times\) 25 \(\times\) 20 & 5.16 & 766.49 & 1000 & 11,000 \\
3 & 20 \(\times\) 25 \(\times\) 40 & 5.16 & 1282.15 & 1000 & 21,000 \\
4 & 20 \(\times\) 25 \(\times\) 100 & 5.16 & 2829.64 & 1000 & 50,000 \\ \hline \end{tabular}
\end{table}
Table 3: Dimensions of the lattice structures and solid blocks for the studied cases.  
selected--see Figure 2b. For both geometry types, a mesh independency study was carried out prior to the simulations to ensure the convergence and, thus, the validity of the results.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 4 Experiment and Discussion
### Experiment Process
#### 4.2.2 Geomagnetic Dynamic Measurement  
After the measurement of the geomagnetic calibration value of the experiment area, the geomagnetic dynamic measurement is carried out by repeated sliding 10 times on the truss. During the dynamic measurement, the data acquisition terminal collects the sample data in real time and sends it to the upper computer for compensation and display. The real-time curve measurement by the upper computer is shown in Figure 19.  
The black curve represents the measured data before compensation, and the indigo curve shows the dynamic compensated curve. The effect of dynamic compensation can be preliminarily seen from the comparison of the two curves in Figure 19. Further comparison and analysis are carried out in the next section.
## 4 Discussion
### Some Errors and Research Expectations  
Section 4.1 compared relevant studies on SSM estimation in vegetated areas, and the accuracy obtained by different studies is inconsistent. Part of the bias is caused by objective factors such as the inaccuracy of the input data and the limitations of the validation methods. Meanwhile, additional bias is contributed by the subjective factor of the innate defects of the algorithms. There are also errors in this study.  
On the one hand, S1 SAR and S2 MSI images have their own registration errors and correction errors, as well as spatial resolution matching errors of S1 and S2, which will affect the inversion accuracy of SSM. On the other hand, the spatial distribution of SSM has strong spatial heterogeneity, and the in situ measured sample value cannot completely objectively and truly reflect the actual situation of the 20 m spatial scale (spatial resolution of remote sensing image data resampling-sampling).  
In this research, the vegetation red-edge band with a central wavelength of 0.705 \(\upmu\)m was used to calculate the MCARI, which was used to estimate the VWC. The SSM estimation model was established, and high accuracy was obtained. However, there are four vegetation red-edge bands of the S2 satellite, and their central wavelengths are 0.705, 0.740, 0.783, and 0.865 \(\upmu\)m, respectively, corresponding to Band 5, Band 6, Band 7, and Band 8A. The indices calculated from each vegetation red-edge band are called MCARI1, MCARI2, MCARI3, and MCARI4, respectively. That is, our research used MCARI1 without a comparative analysis of MCARI2, MCARI3, and MCARI4. We still do not know the effects of the other three indexes on SSM estimation. In the future, the above four indices can be compared to further clarify the MCARI and which vegetation red-edge band calculated has the best performance in SSM estimation.  
The SSM estimation model developed in our research is simple and easy to implement, and the estimated SSM has very satisfactory robustness at a high spatial resolution. There is no denying that the developed model also has some disadvantages that need to be addressed in the future. First, there is still some error introduced due to ignoring the surface roughness and crop type. Secondly, the transit date of the S2 satellite is not completely synchronized with the transit date of the S1 satellite. In addition, although clouds, rain, and other extreme weather conditions have little to no impact on S1 images, they have a great impact on optical remote sensing images, and the data obtained by the cloud processing algorithm is not the best choice, which may be a challenge. At present, soil moisture data sets with a high spatiotemporal resolution are still scarce. In the future, to produce a high spatiotemporal resolution products (e.g., 20 m \(\times\) 20 m, daily), it is needed to make full use of MODIS and Landsat images and introduce spatiotemporal fusion methods, e.g., the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) [52], the Flexible Spatiotemporal Data Fusion (FSDAF) [53], and the Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) [54].
## 2 Problem Statement
### LPV Model of PMSG  
The mathematical model of a surface-mounted PMSG can be expressed in the stationary reference frame as [14; 18; 19]  
\[\frac{di_{\alpha}}{dt} =-\frac{R_{s}}{L_{s}}i_{\alpha}+\frac{n_{p}\psi}{L_{s}}\sin\left( \theta\right)\omega_{r}+\frac{1}{L_{s}}u_{\alpha} \tag{3}\] \[\frac{di_{\beta}}{dt} =-\frac{R_{s}}{L_{s}}i_{\beta}-\frac{n_{p}\psi}{L_{s}}\cos\left( \theta\right)\omega_{r}+\frac{1}{L_{s}}u_{\beta}\] \[\frac{d\omega_{r}}{dt} =-\frac{3n_{p}\psi}{2J}\sin\left(\theta\right)i_{\alpha}+\frac{3 n_{p}\psi}{2J}\cos\left(\theta\right)i_{\beta}-\frac{F}{J}\omega_{r}-\frac{1}{J}T_{L}\] \[\frac{d\theta}{dt} =n_{p}\omega_{r},\]  
where \(i_{\alpha}\), \(i_{\beta}\) and \(u_{\alpha}\), \(u_{\beta}\) are the currents [A] and voltages [V] of phases \(\alpha\) and \(\beta\) in the stationary frame, respectively. In addition, \(\theta\) is rotor electrical angle [rad]; \(\omega_{r}\) denotes rotor velocity [rad/s]; \(F\) is the viscous friction coefficient [N\(\cdot\)m\(\cdot\)s/rad]; \(T_{L}\) is the load torque [N\(\cdot\)m]; \(J\) is the inertia of the motor [Kg\(\cdot\)m\({}^{2}\)]; \(\psi\) is the magnetic flux of the motor [Wb]; \(R_{s}\) is the resistances of the phase winding [\(\Omega\)]; \(L\) is the inductance of the phase winding [H]; \(n_{p}\) is the number of pairs of rotor poles.  
By defining state variables \(x=\left[i_{\alpha},i_{\beta},\omega_{r},\theta\right]^{T}\) and measurements \(y=\left[i_{\alpha},i_{b},i_{c},\omega_{r},\theta\right]^{T}\), system (3) is expressed as a linear parameter varying model  
\[\dot{x} =A\left(\theta\right)x+B_{u}u+B_{d}d, \tag{4}\] \[y =Cx,\]  
in which \(i_{\alpha}\), \(i_{b}\) and \(i_{c}\) denote three-phase currents of PMSG which are acquired by current sensors. Provided that the currents and voltages remain nearly constant at each sample time interval \(T_{s}\). The Forward Euler Approximation method is introduced for discretization of a PMSG model  
\[x\left(kT_{s}+T_{s}\right)=x\left(kT_{s}\right)+T_{s}\left\{\frac{dx\left(t \right)}{dt}\right\}_{t=kT_{s}}, \tag{5}\]  
where \(T_{s}\) is the sampling time. Accordingly, system (4) leads to the following discrete system model:  
\[x\left(k+1\right) =A\left(\theta_{k}\right)x\left(k\right)+B_{u}u\left(k\right)+B_{ d}d\left(k\right), \tag{6}\] \[y\left(k\right) =Cx\left(k\right),\]  
where \(A\left(\theta_{k}\right)\), \(B_{u}\), \(B_{d}\) and \(C\) are listed as Equation (A1) in Appendix A.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 5 Results and Validation
### Experiment Setup  
Our experiments were executed on hardware consisting of an Intel (R) Core (TM) i5-9400 CPU running at 2.90 GHz and an NVIDIA GeForce GTX 1660 Ti for training the TBPPO agent. We implemented the AC model using PyTorch and optimized the training process using the Adam Optimizer as the loss function. In our experimental model, the state representation includes several components, such as the current KL trust values for each node, node types, node utilization, the current average delay, and the number of remaining steps. The action space in our experiments is constructed from a set of feasible routes calculated using an improved DFS algorithm. The Actor network is designed as a six-layer fully connected neural network, with each layer consisting of 256 neurons and layer normalization applied between these layers. The output size of the network matches the action space size, which is set to 5 in our experimental model. Similarly, the Critic network is constructed as a six-layer fully connected network with 256 neurons in each layer, featuring layer normalization between layers. The output of the Critic network corresponds to the Q-value, resulting in an output size of 1. For a comprehensive understanding of the remaining parameters of our experimental model, please refer to Table 2.
## 2 Related Works
### Communication Technologies  
In BSNs, a communication technology is required to meet the stringent requirements of BSNs (QoS, energy efficiency, scalability for heterogeneous data rate) considering the specific characteristics of BSNs. The existing technologies that are applicable in BSNs include Bluetooth, ZigBee, and IEEE 802.15.6. As Bluetooth does not support various traffic priorities in a device such as on-demand traffic, normal traffic, and emergency traffic in healthcare applications [14, 15, 16], we consider IEEE 802.15.4 and IEEE 802.15.6 in this paper [14, 15, 16].  
IEEE 802.15.4 is currently the most widely used radio standard in BSNs for its very low power consumption and cost [17, 18, 19, 20]. ZigBee is based on IEEE 802.15.4. Compared with other short range communication technologies (e.g., Bluetooth, Wi-Fi), ZigBee has the least protocol complexity and wake-from-sleep time. Moreover, it provides several power options depending on the specific applications (from \(-25\) dBm to 0 dBm), and it supports mesh network configurations. The only drawback is that ZigBee supports a data rate of up to 250 kbps, which may be insufficient for some BSN applications.  
The IEEE 802.15.6 working group has been formed to develop a standard for short-range, wireless communication for body area networks [21, 22]. It supports QoS, low power, and data rates up to 10 Mbps. This standard considers effects on antennas in the presence of a human body (varying with male, female, thin, fat, _etc._), radiation pattern shaping to minimize specific absorption of the human body, and changes in characteristics due to the BSN user motions. It defines a MAC layer that supports several physical layers, such as 2.4 GHz ISM Narrowband, ultra-wideband (UWB), and human body communications layers. Compared with IEEE 802.15.4, IEEE 802.15.6 requires a shorter communication range, and larger data rate, in order to safe more power cost, and enables support various applications. From the perspective of MAC, 802.15.4 and 802.15.6 are similar. In this paper, we will consider IEEE 802.15.4 and IEEE 802.15.6 for possible MAC in a BSN.