esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Gradation Segregation on Mechanical Properties of an Asphalt Mixture
## 3 Results
### Effect of Gradation Segregation on the Asphalt Mixture
#### 3.2.2 Compressive Strength  
In order to study the effect of gradation segregation on the performance of asphalt mixtures under compressive load, \(10\times 10\) cm 2D DEM models were developed based on the method described above. The top wall was controlled to load down at a constant speed of 2 mm/min until the model was broken. During this period, the stress on the top wall and the model strain were recorded, as shown in Figure 7. The compressive strength ratios of the seven kinds of mixtures were calculated, as shown in Figure 8. The compressive strength ratio is defined as the compressive strength of segregation mixtures divided by the compressive strength of standard mixture.  
From Figures 7 and 8, it can be seen that with the gradation from coarse to fine, the compressive strength of mixture gradually increases. When the fine aggregate segregation occurs, the compressive strength of mixture increases with the increase of segregation levels with the increase rate of 5\(\sim\)14%. While, when the coarse aggregate segregation occurs, the compressive strength of mixture significantly  
Figure 8: Compressive strength ratio of the asphalt mixture.  
Figure 6: Distribution of contact force in asphalt mixtures.  
Figure 7: The stress-strain curves of the asphalt mixture.  
decreases with the increase of segregation level. Under high-level coarse aggregate segregation conditions, the compressive strength of mixture is about 75% of that of the standard mixture. Even under low-level coarse aggregate segregation, the compressive strength of mixture is only about 90% of that of the standard mixture. This indicates that the increase of fine aggregates in the mixture is beneficial to improve the compressive strength of the mixture, while if coarse aggregate segregation occurs in asphalt mixture, the compressive strength will decrease significantly with the increase of segregation level, which in turn increase the broken susceptibility of asphalt mixture under loading.  
In order to verify the reliability of the simulation results, compression test was performed on asphalt mixture with MTS. The laboratory test parameters are same as that of the simulation. Figure 9 presents the differences between simulation and laboratory results. It can be found that the simulation results are almost consistent with the laboratory experiment results, which verified the feasibility of using the DEM to evaluate the anti-compression performance of the asphalt mixture.  
Figure 9: The stress-strain curves of the asphalt mixture.
# Optimization Design of the Lower Rocker Arm of a Vertical Roller Mill Based on ANSYS Workbench
## 3 Optimum Design of Response Surface Analysis Method
### Selection of the Optimized Scheme  
After fitting the response surface, the parameters were optimized using the optimization module. The Multi-Objective Genetic Algorithm (MOGA) method is a variant of the popular NSGA, which is suitable for calculating the global maximum or minimum [20; 21]. We selected the MOGA in the response surface to optimize the parameter model of the lower rocker arm. Finally, the minimum mass of the lower rocker arm was set as the goal of this optimization design; the maximum stress of the lower rocker arm was less than 192.8 MPa and the maximum displacement was less than 1.5 mm. These were set as the constraints of this optimization design. The initial sample number was set to 100 groups and 70 samples were iterated each time to improve the accuracy of the optimization model. When solving, the maximum number of iterations was 20, and finally three groups of the best candidate points were set to output. The three groups of optimal design points obtained from the optimized response surface model are shown in Table 3.  
Figure 11: Sensitivity of the input variables to the mass (P8), maximum displacement (P9), and maximum equivalent stress (P10) of the lower arm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **First Group** & **Second Group** & **Third Group** \\ \hline P1/mm & 129.03 & 129.01 & 129 \\ P5/mm & 848.34 & 848.16 & 848.16 \\ P6/mm & 98.441 & 98.446 & 101.52 \\ P7/mm & 168.48 & 168.32 & 168.35 \\ P8/kg & 6206.2 & 6206.2 & 6207.4 \\ P9/mm & 1.1469 & 1.1474 & 1.1404 \\ P10/MPa & 119.86 & 119.93 & 117.77 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three groups of optimal design points after optimization of the lower rocker arm.  
The rounded values of the input variables in the first group were selected as the actual dimensions for the lower arm optimization, as shown in Table 4.  
It is clear, from Table 3, that the optimized P5 increased while the other input variables decreased. After optimization, the mass of the lower rocker arm was reduced from the original 6552.5 kg to 6206.2 kg; the mass decreased by 346.3 kg, and the reduction was of approximately 5.29%.
# Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
## 3 Optimization of Parameters for Vibration Suppression
### Tuning Procedure  
To obtain the optimized vibration-free solution, we can first calculate the time-optimal parameters \(T_{a}\), \(T_{v}\), and corresponding \(t_{1}\), \(t_{2}\) under actuator limits according to Equations (5) and (6) and then modulate them according to the vibration suppression conditions. Note that there exist infinite solutions that can satisfy either one of the two conditions. To improve motion efficiency, it is expected that the trajectory duration can be minimized  
Figure 4: (**Top**) Reference acceleration profile and (**bottom**) induced vibration response when vibration suppression condition C2 is satisfied.  
Figure 3: (**Top**) Reference acceleration profile and (**bottom**) induced vibration response when vibration suppression condition C1 is satisfied.  
while satisfying these conditions. In practice, between conditions C1 and C2, the one that yields a shorter motion duration can be chosen. Alternatively, both conditions can be chosen simultaneously to improve robustness. The optimization problem can be described as follows:  
\[\left\{\begin{array}{l}\min_{t_{1},t_{2},a}T_{tot}=2T_{a}+T_{v}=t_{1}+t_{2}\\ \text{s.t. }vt_{2}=h\\ t_{1}=k_{1}T_{n}\text{ or }t_{2}=k_{2}T_{n}\left(k_{1},\ k_{2}\in\mathbb{N}^{+} \right),\ t_{1}>0,\ t_{2}\geq t_{1}\\ 0<a\leq A_{\text{max}},\ v=at_{1}\leq V_{\text{max}}\end{array}\right. \tag{15}\]
# Nucleotides as Bacterial Second Messengers
## 3 The Novel Cyclic Dinucleotide Second Messengers  
There are other types of signal nucleotides with structures different from those mentioned above, such as cyclic AMP-GMP and cyclic AMP-AMP, and cyclic trinuucleotides, such as cyclic AMP-AMP-GMP (cAAG) and cyclic AMP-AMP-AMP (cAAA). These nucleotides are synthesized by a new superfamily of proteins called cGAS/DncV-like nucleotidyltransferase (CD-NTases), and these CD-NTases are conserved in all bacteria observed. The signaling of these nucleotides can activate effector proteins, such as DNA endonucleases, transmembrane pore-forming proteins, patatin-like phospholipases, and proteases. These effector proteins provide antiphage immunity [55], protecting bacteria against bacteriophage infection.  
The hybrid dinucleotide between AMP and GMP is cyclic AMP-GMP. Cyclic AMP-GMP is produced by the CD-NTase DncV [56], and the homologous DncV is present in a subset of proteobacteria, including the diarrhoea _E. coli_ strain DEC8D. In _Vibrio cholerae_ El Tor, the dncV gene is located in the pandemic island 1 (Vsp-1) of vibrio, which is believed to have contributed to the biotype that caused pandemics. Elimination of _dncV_ gene in _V. cholerae_ inhibits intestinal colonization in a mouse model because DncV represses chemotaxis.  
Another system of bacterial protection against nucleic acid entry events is the CRISPR-Cas system; which is considered a mechanism of immunity. The CRISPR-Cas type III system destroys transcription-dependent DNA. A component protein of the system, Cas10, is considered to be a nuclease that cleaves single-stranded DNA. During the process of cleavage of foreign DNA by Cas10, this protein also synthesizes a cyclic oligoadenylate (cOA, 4 adenine units). COA binds to Csm6/Cssx1 proteins with ribonuclease activity (also belonging to the CRISPR-Cas system) to degrade RNA non-specifically; it can also bind to other RNases, such as RelE and PIN, and to DNAases to cleave RNA and bacterial DNA. COA can be toxic to bacteria, so it is degraded by CRISPR-associated nuclease 1 (Crn1), which cleaves cOA specifically into di-adenylate products in a linear fashion to quench cOA-activated effector proteins [57].
## 3 Results and Discussion
### Energy Utilization in The Convective Solar Dryer  
3.5.1. Total Energy Utilization of The Forced Convection Solar Dryer  
The total energy used by the convective solar dryer to dry the _Arbutus unedo_ L. fruit samples at different temperatures is presented in Figure 9.  
Figure 8: Relationship between effective moisture diffusivity and reciprocal absolute temperature.  
Figure 9: Total electrical energy used in drying the _Arbutus unedo_ L. fruits.  
The total energy consumption decreased as the temperature increased (Figure 9). This means that a decrease in the drying time has an influence on the total energy usage when the air-drying temperature increases. Similar findings for vegetables and fruits were reported by [65]. The electrical energy utilization of _Arbutus unedo_ L. fruit at diverse moisture contents and air-drying parameters was measured by an electric energy meter, it varied from 0.65 to 3.24 kWh. The electrical energy consumption decreased as temperature increased. As a result, the electrical energy utilization depends strongly on the drying time. This latest has a low value in comparison with the total energy utilization.
# A Strain-Transfer Model of Surface-Bonded Sapphire-Derived Fiber Bragg Grating Sensors
## 5 Conclusions  
In this paper, an improved surface-bonded SDFBG strain-transfer model was theoretically proposed and experimentally demonstrated. The SDFBG with a high alumina content was considered an inhomogeneous material in the fiber region, and the measurement structure was abstracted into core, cladding, adhesive, and host material. The transfer function of the analytical model was derived according to shear-lag theory. Strain-transfer characteristics were analyzed using the four- and three-layer, and FE models. The core's radius and the higher alumina content were found to have a significant effect on the ASTR. Simulation results indicated that the larger core's radius and the higher content resulted in a lower transfer rate. For example, the ASTR calculated from the four-layer model decreased by about 20% when the alumina content was fixed at 50 mol %, and the core's radius increased from 5 to 62.5 \(\upmu\)m, while the ASTR decreased by about 8% when the core's radius was fixed at 30 \(\upmu\)m, and the alumina content increased from 0 to 100 mol %. The accuracy of the proposed model was verified by the strain experiment on a universal testing machine. The ASTR decreased by about 5% when the core's radius increased from 10.5 to 30.8 \(\upmu\)m, or alumina content increased from 4.5 to 49.6 mol %. The four- and three-layer models were found to be preferable for predicting the transfer rates of sensors with a high and low alumina content, respectively, and error percentages of less than 0.9% were obtained. This research can be used to instruct practical applications of and guarantee accurate measurements by SDFBG strain sensors.  
Conceptualization, P.Z. and Z.W.; methodology, P.Z.; validation, L.Z.; formal analysis, S.C.; investigation, Z.S.; writing--original-draft preparation, P.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51575032.  
The authors declare no conflict of interest.
# Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11 ax
## 3 Results
### Localization Data and Trilateration Method Analysis  
The localization data for the anchor nodes used in the trilateration algorithm are presented in Table 8, which include both geographical coordinates (latitude and longitude) and Cartesian coordinates (X, Y) in meters. The table lists the positions of the three anchor nodes, along with their respective locations:  
* **Node 1:** Located at the latitude 22.107776 and the longitude \(-\)100.84508, with Cartesian coordinates of 309,669.41 m for X and 2,445,910.071 m for Y;
* **Node 2:** Positioned at the latitude 22.107777 and the longitude \(-\)100.845054, with Cartesian coordinates of 309,672.42 m for X and 2,445,910.03 m for Y;
* **Node 3:** Found at the latitude 22.107705 and the longitude \(-\)100.84510, with Cartesian coordinates of 309,667.31 m for X and 2,445,902.12 m for Y.  
RSSI measurements from the anchor nodes were used to estimate the distances between the unknown node and the reference nodes using signal propagation models. These distances became key parameters for the application of the trilateration algorithm, as shown in Table 9. Figure 10 shows the area where the measurements were taken, as well as the nodes, indicating the locations of the anchor nodes relative to the transmitter for the trilateration method. Subsequently, using the previously mentioned data and the trilateration algorithm, the unknown node was located. The data for the unknown node, obtained using this method, can be observed in Table 10. Analyzing Table 10, the localization data for the unknown node determined through the trilateration algorithm are presented. This table includes the geographical latitude and longitude, as well as the Cartesian coordinates (X, Y) in meters, for the unknown node:  
* Latitude: 22.10779443;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Real Distance [m]** & **RSSI [dBm]** & **Calculated Distance [m]** \\ \hline
1.00 & \(-\)28 & 0.93 \\
2.00 & \(-\)38 & 1.79 \\
3.00 & \(-\)44 & 2.78 \\
4.00 & \(-\)47 & 3.72 \\
5.00 & \(-\)52 & 4.98 \\
6.00 & \(-\)54 & 5.77 \\
7.00 & \(-\)57 & 7.18 \\
8.00 & \(-\)59 & 8.31 \\
9.00 & \(-\)60 & 8.94 \\
10.00 & \(-\)62 & 10.34 \\
11.00 & \(-\)63 & 11.13 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Real distance data, RSSI, distance calculated using RSSI, and percentage error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Latitude** & **Longitude** & **X** & **Y** \\ \hline
22.107777 & \(-\)100.845103 & 309,667.415 & 2,445,910.095 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Localization data for unknown node (latitude, longitude, X, Y coordinates).
## 2 Noise Reduction Mechanism and Frequency Estimation Method Based on the Coupled Lorenz System  
### Theoretical Model and Working Mechanism  
In this paper, a new method is used to detect a weak signal submerged in strong background noise; its SNR is usually less than \(-20\,\mathrm{dB}\), and the equation is  
\[r(t)=s(t)+n(t), \tag{1}\]  
where \(s(t)\) is a weak sinusoidal signal \(A\sin(\omega t+\theta)\), \(n(t)\) is the Gaussian white noise whose probability density follows the normal distribution. Its mean value is 0, its variance is \(\sigma^{2}\), and its expression is \(N(0,\sigma^{2})\). This kind of signal exists in many fields, including power electronics, rotating machinery, clinical medicine and so on. According to the related theory of signal processing, if each signal with different frequencies in the tested signal can be accurately distinguished, fault diagnosis can be achieved by finding the peak value of the power spectrum density and comparing it with the value of the fault characteristic frequency.  
Regarding the above discussions, there are still many limitations in the existing chaotic detection methods that cannot be directly used in fault diagnosis. Therefore, we propose two chaotic systems with the same structure to form a synchronization system to achieve the detection of the fault characteristic signal. The block diagram of the detection system is shown in Figure 1.  
In this system, the chaotic system I is used as the driving end and the chaotic system II is used as the response end. The two ends are coupled by the drive-response method.  
The diagnostic process is divided into three steps. First, the tested signal is injected into the chaotic system I, which results in the deviation of the state variables of the two chaotic systems. Second, the state variables of the two chaotic systems at the same position are obtained simultaneously, and the synchronization error can be obtained. Finally, the power spectral density of the synchronization error \(e(t)\) is calculated and matched with the fault characteristic frequency to realize the fault diagnosis. This system makes full use of the characteristics of the chaotic system and enhances the periodic signal while restraining the influence of white noise, thus improving the signal-to-noise ratio of the tested signal.
## 2 The Fundamental Theories  
### Extreme Learning Machine (ELM)  
Given \(N\) samples \(D=(x_{i},t_{i})\), \(i=1,\ldots,N\), in which \(x_{i}\in\mathbb{R}^{n}\), \(t_{i}\in\mathbb{R}^{m}\) represents the class label of \(x_{i}\), and \(m\) is the number of class labels. The output function of an extreme learning machine with \(L\) hidden layer nodes is expressed as:  
\[f(x)=\sum_{i=1}^{L}\beta_{i}h_{i}(a_{i},b_{i},x)=\beta\cdot H(x) \tag{1}\]  
in which \(a_{i}\) represents input weights between input layer and the ith hidden layer node, \(b_{i}\) represents the bias of the ith hidden layer node, \(h_{i}\) (\(\cdot\)) is the activation function of the hidden layer. \(\beta=[\beta_{1},\ldots,\beta_{L}]^{T}\) represents output weights, and \(H(x)=[h_{1}\ (a_{1},b_{1},x),\ldots\ h_{L}\ (a_{L},b_{L},x)]\) is the output matrix of the hidden layer in which each element \(h_{i}\ (a_{i},b_{i},x)\) is the output of the ith hidden layer nodes. The above formula is expressed as:  
\[H\beta=T \tag{2}\]  
in which \(T\) is the class label matrix of sample set \(D\). \(H\) represents the feature mapping matrix as shown as follows:  
\[H=\begin{bmatrix}h_{1}(a_{1},b_{1},x_{1})&\cdots&h_{L}(a_{L},b_{L},x_{1})\\ \vdots&\ddots&\vdots\\ h_{1}(a_{1},b_{1},x_{N})&\cdots&h_{L}(a_{L},b_{L},x_{N})\end{bmatrix} \tag{3}\]  
According to the orthogonal projection method [29], the Moore-Penrose generalized inverse \(H^{\dagger}\)[30] of matrix \(H\) is obtained to calculate the output weights \(\beta\):  
\[\beta=H^{\dagger}T,\ H^{\dagger}=\left(H^{\mathsf{T}}H\right)^{-1}H^{\mathsf{ T}} \tag{4}\]  
The three-step learning process of ELM is: (1) randomly generate the input weight \(a\) and bias \(b\) of L hidden layer nodes; (2) calculate the feature mapping matrix \(H\); (3) calculate the output weight \(\beta\) according to \(H^{\dagger}\).
## 3 Results and Discussion
### Mechanical Properties  
The mechanical performance of TPS-CH-M and TPS-CH-M-A are shown in Table 1. These values were calculated from the stress-strain curves obtained from uniaxial tensile tests performed until failure. Figure 7 displays the stress-strain curves of TPS-CH-M and TPS-CH-M-A films. Based on the analysis of these curves, both films exhibit two distinct regions: the elastic region and the plastic region. This behavior is characteristic of thermoplastic biopolymers [49]. In the plastic region, the TPS-CH-M film presents a smaller elastic area compared to TPS-CH-M-A. This difference results in a higher elastic modulus for the film containing the API, increasing from 9.27 MPa in TPS-CH-M to 9.38 MPa in TPS-CH-M-A. This increase can be attributed to the incorporation of the API, which introduces an elastomeric behavior at the onset of the elastic region [50]. Additionally, the narrowing of the yield zone in the API-containing film leads to a lower yield point of 17.67 MPa for TPS-CH-M-A compared to 17.17 MPa for TPS-CH-M. The incorporation of the API also affects the failure behavior of the film, causing it to break with minimal non-uniform plastic deformation. In TPS-CH-M, the tensile strength at yield and the breaking point are both 24.21 MPa, whereas in TPS-CH-M-A, these values differ, with a tensile strength at yield of 24.54 MPa and a breaking point of 23.39 MPa. Overall, the incorporation of the API enhances the film's toughness, increasing it by 8%. Consequently, the addition of the API to the edible film improves its mechanical properties.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Mechanical Properties** & **ODF Pristine** & **ODF with API** \\ \hline E (MPa) & 9.27 & 9.38 \\ Yield point (MPa) & 17.76 & 17.67 \\ Tensile strength yield (MPa) & 24.21 & 24.54 \\ Breaking point (MPa) & 24.21 & 23.39 \\ Resilience (MPa) & 17.84 & 15.04 \\ Tenacity (MPa) & 61.28 & 66.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mechanical performance of TPS-CH-M and TPS-CH-M-A.  
Figure 6: DSC curves of the different components used in the production of films with and without API.
## 5 Data Imbalance
### Limitations of Data Imbalance Handling Techniques
#### 5.4.2 Computational Complexity and Model Stability  
Generative Adversarial Networks (GANs) exhibit significant advantages in addressing data imbalance, but their training process is inherently complex. Balancing generators and discriminators requires significant computational resources and parameter adjustments [112]. Additionally, there is a risk of mode collapse, where the model fails to generate diverse minority class samples, affecting its generalization ability across different attack patterns [113]. The challenges of hyperparameter tuning and maintaining training stability further increase the complexity and cost of implementing GANs in practical applications [114].