esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Safe Pathways to Deploy a LoRaWAN Network  
After experimentation with different variables based on the influence of crucial factors, we are proposing some optimal deployments aiming to include as many EDs as possible (scalability), cover a wide range (coverage) and use as little power as possible (power efficiency). In simple words, the parameters for the protocol adoption is the trade-off between scalability, coverage and power consumption and a parallel goal is to offer LoRaWAN-wide solutions depending on the application requirements, in order to avoid the withdrawal to other LPWAN alternatives (NB-IoT, Sigfox) without reason [30].  
For small-scale networks (<100 EDs), which is the easiest situation, it is safe to deploy EDs to only DR5 when the required coverage within a rural area is under 3-4 km. In this case the data acquisition frequency could be even less than a minute. When we want to reach medium coverage on such a deployment we can additionally deploy EDs on DR4, DR3, DR2, DR1, but the transmission interval needs to be increased in most cases. For reaching the maximum coverage on small networks we can deploy DR0 EDs but  
Figure 11: Range of communication with TX power of 2 dBm.  
only for transmitting in intervals more than 1 h. Once the capacity increases (<500 EDS) and the required range is medium (<6 Km) the network can work flawlessly when data acquisition has been set to 10 min or more and the data rates used are distributed to DR3 or greater. Once the network gets bigger the safe option is to also decrease the data acquisition frequency. It is proved that you can have the highest data acquisition assurance once on a medium-to-large network (<1000 EDS) the data acquisition frequency drops to 1 h, while only DR4 and DR5 EDs are deployed for short-to-medium (<4 km) coverage deployments, that involve DR2 and DR3 along with ACKs for medium-to-large (<6 km) coverage deployments. Data acquisition frequency of 1 h can certainly support even large-capacity deployments (<5000 EDs). For such dimensioned networks only DR5 should be used for short-to-medium (<4 km) communication range requirements, or DR2 to DR5 with ACKs for medium coverage ones (<6 km). When large capacity and wide range are both requirements for a network then the way to have the highest possible acquisition assurance is to set the transmission interval to 12 h, deploy EDs in all DRs (DR0 for less than 20% of EDs) and activate ACKs with one retransmission.  
It is worth mentioning here that The Things Industries proposed and facilitated some great concepts to overcome a lot of the issues we have also identified. A fair access policy and the ability to configure RX2 to SF9 are by far the most advanced methods to maintain great capacity common infrastructure LoRaWAN networks while respecting the shared ISM spectrum.
# Influence of Structural Stiffness and Loss Factor on Railroad Vehicle Comfort
## 3 Complex Modulus versus Viscous Damping for Comfort Assessment  
As stated in the introduction, there are solid theoretical arguments, as well as some experimental evidence, in favor of complex moduli as opposed to viscous terms to model structural damping. Nevertheless, it was also mentioned that, despite the loss in accuracy, most researchers resort to viscous terms. To quantify the difference, the formulation in the previous section can easily be tweaked to include viscous damping. The equivalent of Equation (1) is now  
\[E_{r}Iw^{IV}+\
u I\hat{w}^{IV}+\mu\bar{w}=f \tag{16}\]  
where \(\
u\) is the viscous modulus that, forcing the imaginary parts of Equations (1) and (16) to coincide at the first bending frequency, is given by  
\[\
u=\frac{\eta}{(\pi/l)^{2}}\sqrt{\frac{E_{r}\mu}{l}} \tag{17}\]  
Using the same procedure and notation as in the previous section, the equivalent of Equation (10) is now  
\[\Big{[}k\mathbf{A}+i\omega c\mathbf{A}+\frac{E_{r}I}{\mu}\,\mathbf{B}\mathbf{M }+i\omega vI\,\mathbf{B}-\omega^{2}\mathbf{M}\Big{]}\mathbf{Q}=Z(k+i\omega c )\Big{(}\mathbf{W}_{0}+e^{-i\omega l/\
u}\,\mathbf{W}_{l}\Big{)} \tag{18}\]  
The same frequency analysis described above can be used to quantify comfort indexes at any location along the beam for any given set of parameters. For instance, let all parameters take the values shown in the first row of Table 1. The loss factor can take values between 0.01 and 0.1. Figure 3 shows the evolution of the comfort index with a loss factor at two locations on the beam when computed using viscous damping (v.d. in the figure) as well as when using complex modulus (c.m.). It can be seen that differences between both damping models are quite significant when measured in terms of the comfort index. The viscous model greatly overestimates damping as the frequency increases, thus yielding too optimistic comfort index values, as Figure 3 attests. This result sheds some doubt on previous works and prompts experimental verification. This, however, lies beyond the scope this paper.  
Figure 3: Comfort index at two locations on the beam versus the material loss factor (\(\eta\)) using both the viscous (v.d.) and complex modulus (c.m.) damping models.
## 3 Results and Discussion
### Thick-Walled Cylinder Theory  
As shown in Figure 9, this study involves a two-layer combined cylinder. The interference is \(2I_{r}\). The normal contact stress is \(P\). \(u_{1}\) and \(u_{2}\) are the deformation of the mating surface of the hollow shaft and hub, respectively. The normal contact stress of the non- mating surface is zero. Therefore, the contact stress of the interface can be expressed as \[P=\frac{I_{r}}{E_{1}}\left(\frac{\frac{r_{1}^{2}+r_{2}^{2}}{r_{2}^{2}-r_{1}^{2 }}-\
u_{1}}\right)+\frac{r_{2}}{E_{2}}\left(\frac{r_{3}^{2}+r_{2}^{2}}{r_{3}^ {2}-r_{2}^{2}}+\
u_{2}\right)\] (24) where \(E_{1}\) and \(E_{2}\) are the elastic modulus of the hollow shaft and hub, respectively. \(\
u_{1}\) and \(\
u_{2}\) are Poisson ratios of hollow shaft and hub, respectively. In this study, \(E_{1}=E_{2}\) and \(\
u_{1}=\
u_{2}\)  
Figure 9: Two-layer combined cylinder.
# GAO-FCNN-Enabled Beamforming of the RIS-Assisted Intelligent Communication System
###### Abstract  
The joint beamforming optimization from the perspective of the bit error rate (BER) in a reconfigurable intelligent surface (RIS)-assisted intelligent communication system is studied in this paper. A genetic algorithm (GA) is investigated to address the bottleneck of the system performance based on the dynamic adaptability theory. However, the bottleneck is caused by the interaction between the active and passive beamforming. To tackle the constraints of conventional optimization approaches, the hybrid scheme is proposed to combine the GA optimization (GAO) and fully connected neural network (FCNN) strategy. Specifically, the intelligent collaborative tuning of system parameters is achieved using this proposed technique. Simulation findings indicate that the hybrid scheme not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. Based on this, the signal transmission quality is improved, interference is reduced, and the stable and efficient operation of the RIS-assisted intelligent communication system is ensured in the complex wireless transmission scenario.  
BER; RIS; FCNN; GAO 2024103390/electronics1321417810390/electronics1321417810390/1
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 3 Experimentation and Performance Evaluation
### Dataset and Parameter Settings  
There are 2265 maize leaf data in the dataset [44] with image resolutions of \(2448\times 3264\) and \(3456\times 4608\).To prevent over-fitting and, at the same time, to improve the robustness of the maize leaf detection network, the data are enhanced with the help of flip transform, random clipping, and scale transform to expand the maize leaf dataset to 6795, and they take an RGB image format. The dataset is in PASCAL VOC [45; 46], and it is divided into a training set, a validation set, and a test set according to the ratio of 8:1:1.  
To enhance the robustness of the leaf detection network, we use 5437 images of maize leaves with location annotations for training the maize leaf detection model. The detection result is considered correct when the intersection over union (IOU) between the prediction box and the truth box is greater than 0.5. To verify the effectiveness of the MFF-CNN, we selected four mainstream plant disease algorithms for comparison, including Faster R-CNN based on the two-stage detector, YoLov5s based on the one-stage detector, anchor-free CenterNet, and transformer-based DETR. The Faster R-CNN uses a Resnet50 model pre-trained on ImageNet [47] as the backbone with a learning rate of 0.00001, an input  
Figure 9: SSP improvement module.  
image size of 600 \(\times\) 600, and a training batch of two. CenterNet also uses Resnet50 as the backbone with a learning rate of 0.00001, an input image size of 512 \(\times\) 512, and a training batch of eight. DETR uses Resnet50 as the backbone with a learning rate of 0.00001, input image sizes of 2448 \(\times\) 3264 and 3456 \(\times\) 4608, and a training batch of two. CenterNet, Faster-RCNN, and DETR all have an epoch of 300, and YOLO v5 and the MFF-CNN have an epoch of 150.  
YoLov5s uses Darknet53 as the backbone with a learning rate of 0.00001, an input image size of 640 \(\times\) 640, and a training batch of 16. The MFF-CNN proposed in this paper uses New CSP-Darknet53 as the backbone with an initial learning rate of 0.00001, an input image size of 640 \(\times\) 640, and a training batch of 16. The graphics card for this experiment is RTX3080 ti, with Toolkit CUDA of version 11. and deep neural networks GPU-accelerated library CUDNN of version 8.0.4 developed by NVIDIA Corporation. We installed PyTorch 1.8.2 + cu111 developed by Facebook AI Research, and open source python 3.8.12 on a Linux system.
# Design of SBS-Modified Bitumen Stabilizer Powder Based on the Vulcanization Mechanism
## 5 Optimization Design of the Stabilizer Component
### Kaolin Content  
SBS-modified bitumen was prepared according to the test design, and the content of stabilizer (expressed as S content) was 0.09%. No gelation phenomenon occurred in the preparation process, and the 48-h segregation softening point difference was analyzed as Figure 9 shows.  
As can be seen from Figure 9, with the increase of kaolin content, the 48-h segregation softening point difference of SBS-modified bitumen gradually decreases. When the content of kaolin (relative to S) reaches 1250%, the modified bitumen has almost reached the smallest of the 48-h segregation softening point difference; with the kaolin content continuing to increase, the 48-h segregation softening point difference seems to no longer decrease. The reason was that kaolin can reduce the density difference between bitumen and SBS, which leads to the modified bitumen achieving stabilization. Considering the result of the 48-h segregation softening point difference test, it was determined that the proper content of kaolin (relative to S) was 1250%.  
It can be seen from Table 7 above that the incorporation of kaolin has a certain degree of enhancement on the softening point, the Brookfield viscosity at 135 \({}^{\circ}\)C and the penetration percentage after RTFOT, but the enhancement was not significant in the range of the test content. With the kaolin content increase, the ductility decreases slightly after RTFOT, and the ductility at 5 \({}^{\circ}\)C decreases obviously. From the viewpoint of low temperature and anti-aging performance, the content of kaolin should be controlled.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Component No.** & **S:TMTD:ZnO:BHT: Al2O3-2 SiO2-2H2O** & **Component No.** & **S:TMTD:ZnO:BHT:SiO2** \\ \hline
25 & 60:16:4:20:500 & 29 & 60:16:4:20:500 \\
26 & 60:16:4:20:1000 & 30 & 60:16:4:20:1000 \\
27 & 60:16:4:20:2000 & 31 & 60:16:4:20:2000 \\
28 & 60:16:4:20:3000 & 32 & 60:16:4:20:3000 \\ \hline \end{tabular}
\end{table}
Table 6: The composition with kaolin or carbon-white stabilizer.  
Figure 9: The relationship between kaolin content and the 48-h segregation softening point difference of SBS-modified bitumen.  
\"Technical Specification of Construction of Highway Asphalt Pavement (JTG F40-2004, China)\" [20] regulates that the 48-h segregation softening point difference should be no more than 2 \({}^{\circ}\)C; according to the testing data, when the content of kaolin is up to 1250%, the 48-h segregation softening point difference of SBS-modified bitumen was less than 2 \({}^{\circ}\)C, and considering the test phenomenon, test results and economic factors, it was determined that the proper content of kaolin (relative to S) was 1250%.
# Numerical Simulation of KBKZ Integral Constitutive Equations in Hierarchical Grids
###### Abstract  
In this work, we present the implementation and verification of HiGTree-HiGFlow solver (see for numerical simulation of the KBKZ integral constitutive equation. The numerical method proposed herein is a finite difference technique using tree-based grids. The advantage of using hierarchical grids is that they allow us to achieve great accuracy in local mesh refinements. A moving least squares (MLS) interpolation technique is used to adapt the discretization stencil near the interfaces between grid elements of different sizes. The momentum and mass conservation equations are solved by an implicit method and the Chorin projection method is used for decoupling the velocity and pressure. The Finger tensor is calculated using the deformation fields method and a three-node quadrature formula is used to derive an expression for the integral tensor. The results of velocity and stress fields in channel and contraction-flow problems obtained in our simulations show good agreement with numerical and experimental results found in the literature.  
Keywords:KBRZ integral constitutive equation; tree-based hierarchical grids; deformation fields +
Footnote †: journal: Applied
# Experimental Study of 6 LoPLC for Home Energy Management Systems
###### Abstract  
Ubiquitous connectivity is already transforming residential dwellings into smart homes. As citizens continue to embrace the smart home paradigm, a new generation of low-rate and low-power communication systems is required to leverage the mass market presented by energy management in homes. Although Power Line Communication (PLC) technology has evolved in the last decade, the adaptation of PLC for constrained networks is not fully charted. By adapting some features of IEEE 802.15.4 and IPv6 over Low-power Wireless Personal Area Network (6 LoWPAN) into power lines, this paper demonstrates a low-rate, low-power PLC system over the IPv6 network (referred to as 6 LoPLC), for Home Energy Management System (HEMS) applications. The overall idea is to provide a framework for assessing various scenarios that cannot be easily investigated with the limited number of evaluation hardware available. In this respect, a network model is developed in NS-3 (Version 21) to measure several important characteristics of the designed system and then validated with experimental results obtained using the Hanadu evaluation kits. Following the good agreement between the two, the NS-3 model is utilised to investigate more complex scenarios and various use-cases, such as the effects of impulsive noise, the number of nodes and packet size on the latency and Bit Error Rate (BER) performances. We further demonstrate that for different network and application configurations, optimal data sizes exist. For instance, the results reveal that in order to guarantee 99% system reliability, the HEMS application data must not exceed 64 bytes. Finally, it is shown that with impulsive noise in a HEMS network comprising 50 appliances, provided the size of the payload does not exceed 64 bytes, monitoring and control applications incur a maximum latency of 238.117 ms and 248.959 ms, respectively; both of which are within acceptable limits.  
6 LoPLC; home energy management system; smart micro-grid; low-rate PLC; 6 LoWPAN; impulsive noise; low-power PLC +
Footnote †: journal:  
_Article_
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 4 Physical Properties of Foam Concretes  
### Dry Unit Weight (Density)  
The density of foam concrete can be evaluated under two headings as fresh and dry density. It is recommended that the difference between fresh and dry density is 100-120 kg/m\({}^{3}\). Dry density controls the mechanical, physical and durability properties of foam concrete, while the fresh density ensures the volume required for the design mix and pouring control [57].  
According to the literature, the dry density of foam concrete is directly affected by the amount of foam. Increasing the amount of foaming agent produces more foam, resulting in a larger sample volume. In this case, the dry density of the samples decreases. Compressive strength has a linear relationship with dry density [21; 41; 46; 59]. Compressive strength increases with increasing dry density. In addition, the increase in dry density provides an increase in thermal conductivity [17].  
Density may be affected by gradation and fine aggregate type. It was reported that the density increased as the aggregate ratio increased [57]. Other components used in the preparation of foam concrete also affect density. Adding silica fume to foam concretes prepared with fixed foam content increases the density [48]. Similarly, it was reported that when more than 30% slag was used instead of FA in geopolymer foam concrete prepared with FA, the density decreased [17]. It was reported that there is a relationship between fresh and hardened density of foam concretes with different ash contents. As a result, an equation was proposed to calculate the required pouring density for the dry weight of concrete between 600 and 1200 kg/m\({}^{3}\)[7]. Some unit weight values obtained from different studies are presented in Table 6. Cast density is calculated as (\(\rho_{\text{cast}}\) = cast density; \(\rho_{\text{dry}}\) = dry density) in Equation (1).  
\[\rho_{\text{cast}}=1.034\rho_{\text{dry}}+101.96 \tag{1}\]
## 4 Results of Probabilistic Stability and Performance Assessment
### The Grid SCR Impact on System Stability and Performance
#### 4.1.1 Critical Mode Locus in the S-Plan
It is worth emphasizing that for a much lower SCR (e.g., SCR < 2.5), since the AVC bandwidth increased and became closer to other control-loop bandwidths, the above conclusions could not be entirely applied to them. In summary, the inverter-side current controller and AVC could impact the stability margins in higher SCRs. In addition, grid coupling and control-loop interactions due to PLL and higher grid inductors were the main reasons for damping reductions at lower SCRs. This discussion is verified by participation factor analysis in Table 3 for a system working under nominal power. \begin{table}
\begin{tabular}{c c c c c c} \hline
**SCR** & **10** & **5** & **2.5** & **2** & **1.67** \\ \hline \multicolumn{6}{c}{\(P=0.05P_{n}\)} \\ \hline \multirow{2}{*}{Damping Factor} & \multirow{2}{*}{AVC (0.99)} & \multirow{2}{*}{AVC (0.99)} & \multirow{2}{*}{AVC (0.92)} & \multirow{2}{*}{AVC (0.8), DVC (0.13)} & CC (0.46), \(i_{\text{gdg}}\) (0.23), \multicolumn{1}{c}{} \\  & & & & & PLL (0.07), AVC \\ \multirow{2}{*}{Damping Ratio} & \multirow{2}{*}{HPF (0.3), Delay (0.27), \(i_{\text{dq}}\) (0.17), \(v_{\text{cdg}}\) (0.13)} & \multirow{2}{*}{HPF (33), Delay(0.22), \(i_{\text{dq}}\) (0.21), \(v_{\text{cdg}}\) (0.13)} & \multirow{2}{*}{HPF (0.33), Delay(0.22), \(i_{\text{dq}}\) (0.21), \(v_{\text{cdg}}\) (0.21), \(v_{\text{cdg}}\) (0.25), \(v_{\text{cdg}}\) (0.13)} & CC (0.47), \(i_{\text{gdg}}\) (0.25), \(v_{\text{cdg}}\) (0.13) & CC (0.46), \(i_{\text{gdg}}\) (0.25), \(v_{\text{cdg}}\) (0.05), \(v_{\text{cdg}}\) (0.05) & CC (0.46), \(i_{\text{gdg}}\) (0.25), \(v_{\text{cdg}}\) (0.05), \(v_{\text{cdg}}\) (0.05) \\ \hline \multicolumn{6}{c}{\(P=0.25P_{n}\)} \\ \hline \multirow{2}{*}{Damping Factor} & \multirow{2}{*}{AVC (0.99)} & \multirow{2}{*}{AVC (0.99)} & \multirow{2}{*}{AVC (0.92)} & \multirow{2}{*}{AVC (0.73), \(\text{DVC (0.18)}\)} & CC (0.46), \(i_{\text{gdg}}\) (0.23), \multicolumn{1}{c}{} \\  & & & & & PLL (0.08), AVC \\ \multirow{2}{*}{Damping Ratio} & \multirow{2}{*}{HPF (31), Delay (0.26), \(i_{\text{dq}}\) (0.21), \(i_{\text{dq}}\) (0.22), \(v_{\text{cdg}}\) (0.13)} & \multirow{2}{*}{HPF (34), Delay (0.22), \(i_{\text{dq}}\) (0.25), \(v_{\text{cdg}}\) (0.
# Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
## 4 Experimental Results and Analysis
### Discussion and Further Analysis
In this table, Median and Trimean for \(k_{1}=3\) and \(k_{2}=1\), Best 25% for \(k_{1}=4\) and \(k_{2}=1\), and Worst 25% for \(k_{1}=1\) and \(k_{2}=2\) are all lower than the counterparts for \(k_{1}=2\) and \(k_{2}=2\). But the case with \(k_{1}=2\) and \(k_{2}=2\) has the lowest Mean and almost the best in the rest metrics. Moreover, we find that the values of Worst 25% will fluctuate with as much as 1.11 degrees for \(k_{2}=1\). This implies that the illuminant estimation is not robust \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Threshold Setting** & **Mean** & **Median** & **Trimean** & **Best 25\%** & **Worst 25\%** \\ \hline
0.01 & 2.17 & 1.47 & 1.65 & 0.44 & 5.08 \\
0.05 & 2.14 & 1.43 & 1.61 & 0.41 & 5.04 \\
0.1 & 2.08 & 1.37 & 1.56 & 0.38 & 4.97 \\
0.15 & 2.03 & 1.34 & 1.51 & 0.37 & 4.85 \\
0.2 & 1.99 & 1.32 & 1.48 & 0.36 & 4.78 \\
0.25 & **1.97** & **1.30** & **1.46** & **0.36** & **4.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical metrics on the Gehler-Shi and Cube+ datasets using sparse weight vector and matrix (the lower, the better). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Combinations of Unitary Algorithms** & **Mean** & **Median** & **Trimean** & **Best 25\%** & **Worst 25\%** \\ \hline GW, WP, SoG, GE1, GE2, GGW, PCA-based, LSR & 2.20 & 1.46 & 1.60 & 0.40 & 5.31 \\ GW, WP, SoG, GE1, GE2, GGW, PCA-based & 2.32 & 1.50 & 1.67 & 0.45 & 5.62 \\ GW, WP, SoG, GE1, GE2, GGW, LSR & 2.21 & 1.47 & 1.62 & 0.40 & 5.33 \\ GW, WP, SoG, GE1, GE2, GGW & 2.27 & 1.50 & 1.68 & 0.44 & 5.44 \\ GW, WP, SoG, GE1, PCA-based, LSR & 2.07 & 1.36 & 1.50 & 0.39 & 5.00 \\ GW, WP, SoG, GGW, PCA-based & 2.20 & 1.42 & 1.58 & 0.38 & 5.39 \\ GW, WP, SoG, GGW, LSR & 2.12 & 1.38 & 1.53 & 0.39 & 5.16 \\ GW, WP, SoG, GE1 & 2.19 & 1.43 & 1.60 & 0.41 & 5.29 \\ GW, WP, GE2, GGW & 2.15 & 1.46 & 1.62 & 0.42 & 5.12 \\ Ours (GW, WP, PCA-based, LSR) & **1.96** & **1.28** & **1.42** & **0.36** & **4.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical metrics on Gehler-Shi and Cube+ datasets using different combination of unitary algorithms (the lower, the better). in this case. Therefore, we can say the proposed method using the two-step clustering with \(k_{1}=2\) and \(k_{2}=2\) is better than using the one-step strategy. We suppose that since CDFs and IIFs can be regarded as the representatives of different image characteristics, separately applying CDF and IIF to cluster the images, i.e., using two steps, may result in good performances.