esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of LoRa Transmission over Seawater
## 4 Experimental Results and Analysis
### Obstructed LOS Scenario  
This group of experiments had the aim of giving a deeper insight into LoRa links behavior over seawater when the LOS is completely blocked. We carried out experiments at both \(868\,\mathrm{MHz}\) and \(434\,\mathrm{MHz}\) using different settings and antennas. The trajectory was \(28\,\mathrm{km}\) long, see Figure 5.  
Using low-cost off-the-shelf rubber-duck antennas, we were unable to establish a LoRa link on either frequency. Since we were on the boundary of the reception margin, we replaced the antennas on transmit side at both frequencies with wide-band three element collinear dipoles with higher gains: \(8.5\,\mathrm{dBi}\) at \(868\,\mathrm{MHz}\), and \(6.5\,\mathrm{dBi}\) at \(434\,\mathrm{MHz}\). The results of the experiment were the same as previously: there was no reception for any of the LoRa parameters. In the next step, we replaced the antennas at the receive side also with collinear dipoles at both frequencies of interest. Since this case is not suitable for most IoT commercial applications due to costly and bulky antennas at both sides, we tested only one set of LoRa parameters: \(BW=125\,\mathrm{kHz}\), \(SF=10\), and \(CR=4/6\). The results at \(868\,\mathrm{MHz}\) were the same as the previous ones: no reception. On the other hand, we had a reception for \(434\,\mathrm{MHz}\), and the results are given in Figure 21.  
Analyzing RSS, SNR, and PRR combined, it can be concluded that two different kinds of interferences affected transmission: (1) radio-interference, which caused main packet loss between \(95\) and \(175\,\mathrm{min}\) of the experiment; and (2) interference due to multipath propagation which started after \(140\,\mathrm{min}\) of the experiment. Radio-interference can be recognized by short dips in SNR while moving average values remain high. In the interval \(140\)-\(175\,\mathrm{min}\) of the experiment, both radio-interference and interference caused by the multipath propagation affected transmission and PRR. This can be recognized due to short dips in SNR which reach link margin values, and by a drop in moving average values, respectively. Multipath propagation, in this case, could be caused by meteorological conditions and different refraction indexes of air masses between the transmit and receive side, as well as by the diffraction on knife-edge obstacle, made of natural rocks, which blocks LOS propagation (see Figure 5).  
Figure 22 presents the distribution of RSS and SNR moving averages, and the difference between moving average values and their current values. The results are similar to all other results obtained for \(434\,\mathrm{MHz}\) band, except for the RSS fluctuations. Multimodal distribution (three modes) is observed in this case, which can indicate the presence of three intervals affected by different sources of interference combined; in order to confirm this claim, additional experiments need to be performed.  
Figure 21: RSS, SNR, and PRR for ODT at \(434\,\mathrm{MHz}\) in obstructed path for \(BW=125\,\mathrm{kHz}\), \(SF=10\), and \(CR=4/6\).
## 3 Establishment of Hydro-Mechanical Properties Creep Damage Constitutive Model
### Model Establishment
#### 3.3.2 Creep Stage  
\(\mathrm{d}d=0,\;\lambda^{\xi p}=0\), according to the conditions of damage, coupling and plastic consistency, can be obtained from Equation (20):  
\[\left\{\begin{array}{l}\mathrm{d}f_{s}=\frac{\partial f_{s}}{\partial\omega }\mathrm{d}\zeta+\frac{\partial f_{s}}{\partial\xi}\mathrm{d}\xi+\frac{ \partial f_{s}}{\partial\alpha_{p}}\frac{\partial\alpha_{p}}{\partial\tau_{p} }\mathrm{d}\gamma_{p}=0\\ \mathrm{d}\zeta=\mathrm{d}\left[(1-\omega)\alpha_{p}\big{(}1-e^{-\gamma t} \big{)}\right]=\left(\frac{\partial\zeta}{\partial\alpha_{p}}\frac{\partial \alpha_{p}}{\partial\tau_{p}}\mathrm{d}\gamma_{p}+\frac{\partial\zeta}{ \partial t}\mathrm{d}t\right)/\left(1-\frac{\partial\zeta}{\partial\omega} \right)\\ \mathrm{d}\zeta=\frac{\partial\zeta}{\partial\omega}\mathrm{d}\zeta+\frac{ \partial\zeta}{\partial\epsilon^{p}}:\mathrm{d}\varepsilon^{p}\end{array}\right. \tag{20}\]  
By substituting the plastic flow law under the coupling effect of hydro-mechanical properties into the damage, coupling, and plastic consistency conditions, the time-dependent plastic multiplier \(\lambda^{\xi t}\) can be obtained as Equation (21):  
\[\lambda^{\xi t}=\frac{\left(\frac{\partial f_{s}}{\partial\omega}+\frac{ \partial f_{s}}{\partial\xi}\frac{\partial\zeta}{\partial\omega}\right)\frac{ \partial\zeta}{\partial\alpha_{p}}\frac{\partial\alpha_{p}}{\partial\gamma_{p} }-\left(\frac{\partial f_{s}}{\partial\xi}\frac{\partial\zeta}{\partial \epsilon^{p}}:\frac{\partial\xi}{\partial\sigma}+\frac{\partial f_{s}}{\partial \alpha_{p}}\frac{\partial\alpha_{p}}{\partial\gamma_{p}}\right)\left(1-\frac {\partial\zeta}{\partial\omega}\right), \tag{21}\]  
Among them, the time-dependent plastic hardening modulus \(H_{\xi t}\) under the coupling effect of hydro-mechanical properties can be expressed as Equation (22):  
\[H_{\xi t}=-\left(\frac{\partial f_{s}}{\partial\omega}+\frac{\partial f_{s}}{ \partial\xi}\frac{\partial\zeta}{\partial\omega}\right)\frac{\partial\zeta}{ \partial\alpha_{p}}\frac{\partial\alpha_{p}}{\partial\gamma_{p}}-\left(\frac{ \partial f_{s}}{\partial\xi}\frac{\partial\zeta}{\partial\epsilon^{p}}:\frac{ \partial\xi}{\partial\sigma}+\frac{\partial f_{s}}{\partial\alpha_{p}}\frac{ \partial\alpha_{p}}{\partial\gamma_{p}}\right)\left(1-\frac{\partial\zeta}{ \partial\omega}\right), \tag{22}\]  
Based on the plastic flow rule, the derived instantaneous plastic multiplier \(\lambda^{\xi p}\) and time-dependent plastic multiplier \(\lambda^{\xi t}\) can be substituted into the total strain decomposition Formula (15) to obtain the coupled constitutive model of hydro-mechanical properties creep damage. The hydro-mechanical properties creep damage constitutive model includes factors such as instantaneous damage, time-dependent damage and the coupling variable, which can describe the creep deformation characteristics of rocks under the hydro-mechanical properties effect.
## 3 Overview of the Design Method
### Framework  
Figure 2 illustrates the design flowchart of the optimization strategy proposed for the hairpin filter. We propose a method for designing and optimizing the hairpin filter using neural network modeling and reinforcement learning techniques. In this method, we train an ANN model using EM simulation data of the hairpin filter. The trained ANN model is then used as a substitute for EM simulation and an RL agent is established to interact with the design environment of the hairpin filter. The design solutions are evaluated using the ANN model and reasonable rewards are given to the agent, allowing it to learn the strategies for designing the hairpin filter during this process. Therefore, the entire design process can be divided into two stages.  
The initial stage, known as the ANN modeling stage, involves simulating and modeling the electrical behavior of the hairpin filter. To acquire different physical characteristics of the filter under various designs, we scan the geometric parameters that might influence its performance within a chosen design space and conduct simulations. We then create a database that maps the relationship between the geometric and performance parameters, serving as the foundation for training the ANN model. This enables us to develop a cost-effective model that provides the circuit performance, specifically the S-parameters in this instance, with significantly lower computational and time costs compared to traditional electromagnetic simulation software.  
Figure 2: The flowchart illustrates our design process for optimizing geometric parameters. It involves two stages: the ANN modeling stage and the reinforcement learning stage.  
In the second stage, namely the reinforcement learning phase, we employ the PPO algorithm. PPO retains the concepts of the actor network and critic network within the Actor-Critic (AC) architecture: the actor network is a policy function that determines what action to take based on the current state of the environment to achieve the maximum reward; the critic network's task is to estimate the value of the current state, minimizing the error between the predicted state value function and the actual reward. The actor makes decisions based on the critic's evaluation and optimizes the policy through feedback from the environment. The critic updates the value function based on the actual reward and the action chosen by the actor, providing a more accurate value assessment. Through this mechanism, the agent continuously interacts with the environment by taking actions, and optimizes its own strategy by observing the current state and feedback from the environment, ultimately leading the agent to adopt a strategy that aligns with our expectations and completes the optimization of the hairpin filter.  
Our proposed method is not limited to hairpin filters. This method can be extended to the sizing design of other microwave circuits as well.
## 3 Results and Discussion
### Pasting Properties of Flour
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **Peak Viscosity (cP)** & **Trough (cP)** & **Breakdown (cP)** & **Final Viscosity (cP)** & **Setback (cP)** \\ \hline \textless{132} \(\mu\)m & & & & & \\ C, \textless{132} \(\mu\)m & 3667 \(\pm\) 144 \({}^{\text{A,a}}\) & 2099 \(\pm\) 63 \({}^{\text{A,b}}\) & 1568 \(\pm\) 116 \({}^{\text{A,a}}\) & 4949 \(\pm\) 111 \({}^{\text{A,a}}\) & 2850 \(\pm\) 56 \({}^{\text{A,a}}\) \\
15\%, 80 \({}^{\circ}\)C & 499 \(\pm\) 149 \({}^{\text{C,c}}\) & 308 \(\pm\) 47 \({}^{\text{C,d}}\) & 241 \(\pm\) 52 \({}^{\text{C,ef}}\) & 491 \(\pm\) 106 \({}^{\text{D,d}}\) & 183 \(\pm\) 58 \({}^{\text{D,f}}\) \\
30\%, 80 \({}^{\circ}\)C & 1170 \(\pm\) 13 \({}^{\text{B,b}}\) & 606 \(\pm\) 27 \({}^{\text{B,c}}\) & 565 \(\pm\) 36 \({}^{\text{B,c}}\) & 1413 \(\pm\) 28 \({}^{\text{B,b}}\) & 807 \(\pm\) 16 \({}^{\text{B,c}}\) \\
15\%, 120 \({}^{\circ}\)C & 355 \(\pm\) 57 \({}^{\text{C,c}}\) & 248 \(\pm\) 25 \({}^{\text{C,d}}\) & 122 \(\pm\) 19 \({}^{\text{C,f}}\) & 363 \(\pm\) 66 \({}^{\text{D,d}}\) & 115 \(\pm\) 42 \({}^{\text{D,f}}\) \\
30\%, 120 \({}^{\circ}\)C & 1059 \(\pm\) 3 \({}^{\text{B,b}}\) & 619 \(\pm\) 3 \({}^{\text{B,c}}\) & 441 \(\pm\) 2 \({}^{\text{B,cd}}\) & 1126 \(\pm\) 12 \({}^{\text{C,c}}\) & 507 \(\pm\) 11 \({}^{\text{C,e}}\) \\ \hline \textgreater{132--200} \(\mu\)m & & & & & \\ C, \textgreater{132} \(\mu\)m & 3585 \(\pm\) 93 \({}^{\text{A,a}}\) & 2642 \(\pm\) 191 \({}^{\text{A,a}}\) & 943 \(\pm\) 111 \({}^{\text{A,b}}\) & 4861 \(\pm\) 106 \({}^{\text{A,a}}\) & 2219 \(\pm\) 86 \({}^{\text{A,b}}\) \\
15\%, 80 \({}^{\circ}\)C & 496 \(\pm\) 27 \({}^{\text{C,c}}\) & 295 \(\pm\) 8 \({}^{\text{C,d}}\) & 201 \(\pm\) 20 \({}^{\text{C,ef}}\) & 463 \(\pm\) 21 \({}^{\text{C,d}}\) & 168 \(\pm\) 12 \({}^{\text{D,f}}\) \\
30\%, 80 \({}^{\circ}\)C & 1065 \(\pm\) 20 \({}^{\text{B,b}}\) & 579 \(\pm\) 12 \({}^{\text{B,c}}\) & 486 \(\pm\) 19 \({}^{\text{B,c}}\) & 1377 \(\pm\) 25 \({}^{\text{B,b}}\) & 797 \(\pm\) 17 \({}^{\text{B,c}}\) \\
15\%, 120 \({}^{\circ}\)C & 463 \(\pm\) 69 \({}^{\text{C,c}}\) & 285 \(\pm\) 37 \({}^{\text{C,d}}\) & 178 \(\pm\) 38 \({}^{\text{C,f}}\) & 466 \(\pm\) 73 \({}^{\text{C,d}}\) & 181 \(\pm\) 37 \({}^{\text{D,f}}\) \\
30\%, 120 \({}^{\circ}\)C & 1023 \(\pm\) 26 \({}^{\text{B,b}}\) & 671 \(\pm\) 14 \({}^{\text{B,c}}\) & 351 \(\pm\) 17 \({}^{\text{B,de}}\) & 1307 \(\pm\) 23 \({}^{\text{B,bc}}\) & 635 \(\pm\) 9 \({}^{\text{C,d}}\) \\ \hline \end{tabular}  
* Different superscripts (A–D) in columns indicate significant differences between particle sizes, while different lower case (a–f) indicates significant differences within the analyzed parameter (\(p<\) 0.05).  
\end{table} 
Table 2: RVA measurement results.
## 3 Proposed Algorithm for Energy-Efficient Node Mobility
### A Time Interval Calculated According to the Moving Speed and Distance of a Mobile Node
#### 3.2.2 Estimation of Distance to Escape the Transmission Range of a Parent Node  
As shown in Figure 4, when a mobile node approaches or recedes from a parent node, the mobile node must travel a distance (e.g., \(d_{e}\) in the figure), required for the mobile node to escape the transmission range of the parent node. Then, the mobile node is allowed to find the next parent node rapidly to form another DODAG by defining the interval to be the time taken to travel the distance to escape from the transmission range of the parent node. In order to get the distance \(d_{e}\), the values of \(\theta\) and \(d_{f}\) are required where \(\theta\) and \(d_{f}\) are the angle and the distance between the mobile node and the parent node (node Number 5 in the figure) respectively. In this paper, we assume that the direction of movement can be measured by applying such methods as sideband filtering, offset carrier demodulation, in-phase/quadrature demodulation, etc., to the Doppler frequency sent from the router node [16; 17]. Therefore, the value of \(\theta\) to get the direction can be obtained from Equation (1), and \(d_{f}\) can be estimated from the received signal strength indicator (RSSI) value.  
Figure 4: Estimation of the distance required to escape the transmission range of a parent node.  
The current method estimates the distance based on the fact that the RSSI varies with distance. By transmitting a signal of fixed strength from the router node, the distance can be estimated using the strength of the attenuated signal received by the mobile node. Therefore, the transmission loss (\(L\)) in the transmitted signal is calculated as the difference between the two signals and can be found using the Friis free space propagation model [18; 19; 20].  
\[L=20\log_{10}{\cdot}4\pi d_{f}/\lambda\quad[dB] \tag{3}\]  
In Equation (3), \(\lambda\) represents the wavelength of the EM wave. Equation (3) can be rearranged as Equation (4) in order to estimate the distance between the mobile and router nodes.  
\[d_{f}=\lambda/4\pi\cdot 10^{L/20}=c/4\pi f_{r}\cdot 10^{L/20} \tag{4}\]  
Another factor required to calculate the dynamic time interval is to obtain \(d_{e}\) shown in Figure 4. Since the line segments \(d_{f}\), \(r\) and the angle \(\theta\) are known, the \(d_{e}\) can be obtained by applying the second law of cosines where \(r\) is the transmission range of the router node. By calculating the Equation (5), the distance value can be obtained.  
\[d_{e}=d_{f}\cdot\cos\theta+\sqrt{(d_{f}\cdot\cos\theta)^{2}+r^{2}-{d_{f}}^{2}} \tag{5}\]
## 4 Materials and Methods
### Instrumentation  
#### 4.2.1 Proposed Design of Probe and Fabricated Prototype  
Based on the simulation results presented in the previous Sections 3.2-3.4, a probe as depicted in Figure 5a was designed for the diagnosis of cervical precancer. Figure 5b displays the design of the tip of the probe which consists of two concentric stainless steel tubes. Inner tube with inner and outer diameters of 340 and 410 \(\upmu\)m respectively contains 19 fibers of diameter 50/65 \(\upmu\)m. The outer tube has 1.16 mm inner and 5 mm outer diameter and it contains the first tube at the center and 58 fibers of diameter 100/125 \(\upmu\)m between the vacant space. The proximal end of the probe is a stainless steel cylindrical body (diameter 5 mm and length 15 cm) containing fibers. All the fibers at the proximal end of the probe are polished at 45\({}^{\circ}\) (the level angle) with respect to the axis of the cylinder. At the distal end, these fibers are bifurcated and can be connected to the detectors or the source individually.  
The proposed probe was fabricated at FiberTechOptica, Ontario, Canada and is shown in Figure 5c. The beveled proximal end and the tip of the probe are depicted in Figure 5d,e respectively. Because of manufacturing limitations, the fabricated prototype of the probe does not have the symmetric arrangement of the inner ring fibers as proposed in the design (Figure 5b).
## 3 Results
### Experimental Setup
Furthermore, we used the nickel alloy targets with a roughness of 0.01 and 0.1, respectively, and increased the width of the target seam to investigate the effect of roughness on the simulation and experimental results while AL was 0.42. The images are shown in Figure 7 and the statistical results with the roughness of 0.01 as a reference are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline
**Target** & **Source** & **SSIM** & **MSSIM** & **PSNR/dB** \\ \hline \multirow{3}{*}{Diver} & Unoptimized simulation & 0.494 & 0.999 & 29.71 \\  & Optimized simulation & 0.527 & 0.999 & 30.05 \\  & Experiment & 0.534 & 0.992 & 27.04 \\ \hline \multirow{3}{*}{Double slit} & Unoptimized simulation & 0.503 & 0.999 & 23.72 \\  & Optimized simulation & 0.53 & 0.998 & 24.44 \\ \cline{1-1}  & Experiment & 0.553 & 0.993 & 22.87 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of image quality evaluation indicators between simulation and experimental results with the change of AL. \begin{table}
\begin{tabular}{c c c c c} \hline
**Target** & **Source** & **SSIM** & **MSSIM** & **PSNR/dB** \\ \hline \multirow{3}{*}{Double Slit *} & Simulation & 0.318 & 0.998 & 15.31 \\  & Experiment & 0.367 & 0.989 & 14.07 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of image quality evaluation indicators between simulation and experimental results with the change of roughness. Figure 7: Experimental and simulation images of the ’Double-Slit’ target under AL = 0.42. (**a,b**) Experimental results with roughness of 0.01 and 0.1; (**c,d**) simulated results with roughness of 0.01 and 0.1. Figure 7 shows that due to the change of the roughness of the target, the fluctuation of the microsurface elements on the target surface increased, so that the photons reflected at the target became divergent, reducing the brightness and contrast of the image. Since the transmission and reception were coaxial in the simulation, the background noise would be stronger than the experimental results, but the longitudinal comparison results in Table 2 prove the applicability of the IMC method to the simulation of different roughness. Finally, in order to quantitatively investigate the impact of water quality on imaging, we continued to carry out experiments under different ALs and supplemented with simulation analysis.
# Prediction of Manufacturing Quality of Holes Based on a BP Neural Network
## 4 Training and Verification Experiment of BP Neural Network Model
### Model Verification
The drilling thrust required by octahedral composite drilling is relatively small; thus, the occurrence of delamination and tearing of holes was also relatively low after the experimental studies and theoretical analysis. However, the geometric parameters of the drilling tools, which affect the size of the drilling force under the same cutting conditions, also affect the generation of delamination defect. During the hole-making process, the chipped, fracture, or excessive wear of the cutting edge of drilling tool will cause the working noise of the CNC equipment to increase greatly; the CFRP material will overheat, and the axial force will increase. Therefore, when using the process parameters of the BP neural network prediction model to make holes in stacks materials, these abnormal working phenomena must be paid attention to. Based on the BP neural network prediction model, the manufacturing quality of the holes for stack materials composed of T300 CFRP and 7050-T7 Al alloy with different diameter and types was predicted, and the optimal hole-making process parameters were obtained. Some optimization parameters applied to normal production are shown in Table 6, and the total number of holes generated was more than 1000. In practical applications, the qualified rate of manufactured holes (\(\Phi\)3-\(\Phi\)8 mm) is as high as 97%. According to the measurement of holes, other technical indicators are also very good. The highest hole diameter tolerance of CFRP material can meet the requirements of H9, and the surface roughness of most holes is lower than Ra 3.2, which greatly shortens the development cycle of new hole-making processes and improves the manufacturing quality of stack materials composed of T300 CFRP (thickness 3 mm) and 7050-T7 (thickness 2 mm) Al alloy.
## 1 Introduction
### Related Work
Furthermore, the Successive Interference Cancellation (SIC) technique may be exploited by the receiver for detecting multiple users [22; 23]. Hence, an improved spectral efficiency may be achieved when the NOMA is employed by the D2D pairs in order to simultaneously forward the cellular users' data and convey its own data [24; 25]. As we know, NOMA technology has not been considered in overlay D2D cooperation communications in [13; 14; 19; 20; 21]. Considering the combination of NOMA technology and cooperative overlay D2D communication, the transmission power allocated by the DT for the cooperative communication and the D2D transmission may directly affect the transmission rate and the energy consumption of the D2D devices. blueIn order to achieve high energy efficiency, a new framework which aims to find the appropriate data centre is proposed [26] and how to locate the lowest energy consumption route between the user and the designated data centre is addressed [27; 28]. Moreover, energy efficiency is achieved by integrating least possible services to satisfy the user requirements given the large number of resources available [29]. In D2D-enabled cellular network, both D2D communications and concurrent transmissions were utilized to achieve high energy efficiency [30]. NOMA is used for concurrent transmissions which is D2D links and cellular links. However, how to allocate power for DT and EU is significant. More explicitly, assuming the transmission power of the DT is limited, if the DT consumes lower transmitter power to forward EU's data, while conveys itself data at higher power, the D2D transmission may achieve an increased rate and consume reduced energy. However, the QoS requirements of EU may not be satisfied. Conversely, if the DT assigns higher power for cooperative transmission and lower power for the D2D communication, the QoS requirement of EU may be guaranteed at the cost of reducing the rate achieved by the D2D communication and increasing the DT's energy consumption for the cooperative communication. Therefore, a NOMA-based optimal power allocation strategy may be required for satisfying the QoS requirement of both the EU and D2D when both the NOMA technology and the cooperative overlay sharing scheme are exploited.
## 3 Validation Cases  
In this work, the model is validated in two validation areas: Walnut Gulch (WG) and Jornada Experimental Range (JER). These sites are part of the CYGNSS calibration and validation sites within the contiguous U.S. Each site has multiple in situ soil moisture sensors at various depths and a small weather station for precipitation monitoring. The sites are part of the SoilSCAPE network [21]. In addition, local surface roughness measurements were collected from all sites at various locations. Details of the validation sites and the roughness measurements are discussed in the following subsections.
# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 3 Proposed Model  
The proposed hybrid model, the SCA-JADE optimizer, is designed to combine the complementary strengths of the sine cosine algorithm (SCA) and JADE (a self-adaptive variant of differential evolution) to address complex optimization tasks that require both global exploration and local exploitation. This model leverages SCA's sinusoidal and cosinusoidal mechanisms for exploration, enhancing its capability to avoid premature convergence by maintaining solution diversity. Meanwhile, JADE introduces adaptive control parameters and enhanced mutation strategies, refining solutions in the latter stages of the optimization process. By harmonizing these two approaches, the hybrid model aims to improve overall convergence speed, accuracy, and robustness in locating the global optimum for high-dimensional and non-linear optimization problems.