esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper proposes a Cooperative-Relay Neighboring-Based Energy-Efficient Routing (CR-NBEER) protocol with advanced relay optimization for MUSN. The utilization of the relay nodes, among all other sensor nodes, makes it possible to achieve node-to-node deployment. The proposed method focuses only on cooperation and relay optimization schemes. Both schemes have previously been implemented, and thus the proposed method represents the extended version of the Neighboring-Based Energy-Efficient Routing (NBEER) protocol. Path loss, end-to-end delay, packet delivery ratio, and energy consumption parameters were considered as part of the performance evaluation. The average performance was revealed based on simulations, where the overall average EED of Co-UWSN was measured to be 35.5 ms, CEER was measured to be 26.7 ms, NBEER was measured to be 27.6 ms, and CR-NBEER was measured to be 19.3 ms. Similarly, the overall EC of Co-UWSN was measured to be 10.759 j, CEER was measured to be 8.694 j, NBEER was measured to be 8.309 j, and CR-NBEER was measured to be 7.644 j. The overall average PDR of Co-UWSN was calculated to be 79.227%, CEER was calculated to be 66.73.464%, NBEER was calculated to be 85.82%, and CR-NBEER was calculated to be 94.831%. The overall average PL of Co-UWSN was calculated at 137.5 dB, CEER was calculated at 230 dB, NBEER was calculated at 173.8 dB, and CR-NBEER was calculated at 79.9 dB. Based on the simulations and evaluations, it was observed that the cooperation and relay optimization scheme outperformed previous schemes.  
marie communication; cooperating routing; relay optimization; CR-NBEER; NBEER; CEER; Co-UWSN; autonomous underwater vehicles; underwater sensors; path loss +
and Engineering
## 4 Gearshift Control Problem
### Inertia Phase Control
#### 4.3.1 Feedforward Control  
The feedforward-feedback control scheme is adopted to solve the tracking problem of the slip speed. Firstly, the inverse kinetic model of the two-speed transmission should be derived. \"Inverse\" means that the rotational acceleration of the input shaft \(\dot{\omega}_{S1S2}\) is taken as known while the input torque \(T_{in}\) is taken as unknown. In the inertia phase, the two-speed transmission is in the neutral gear state, and the forward kinetic model requires typically four quantities known for solution, namely, \(T_{in}\), \(T_{BK1}\), \(T_{BK2}\), and \(T_{out}\). In the inverse model, \(T_{in}\) and \(T_{out}\) are substituted by \(\dot{\omega}_{S1S2}\) and \(\dot{\omega}_{R1}\), respectively. \(T_{BK1}\) equals to zero along the whole inertia phase as a result of total disengagement of BK1. The aim now is to answer the question \"given \(\dot{\omega}_{S1S2}\), \(\dot{\omega}_{R1}\), and \(T_{BK2}\), how do we determine \(T_{in}\dot{\epsilon}^{\prime}\). Among them, \(\dot{\omega}_{S1S2}\) can be attained through an acceleration estimator over the motor speed, which is reported by the motor controller unit (MCU). \(\dot{\omega}_{R1}\) is from the designed trajectory in Equation (34) and should be apparently known. As has been discussed before, during the inertia phase, \(T_{driv}\) is determined by \(T_{BK2}\) only, and therefore \(T_{BK2}\) can be calculated from Equation (32). Thus, \(T_{in}\) is determined as follows:  
\[\begin{aligned} T_{in}=&\frac{1+k_{1}+k_{2}}{1+k_{1 }+k_{2}+k_{1}k_{2}}T_{driv}+\left(J_{S1S2}-\frac{1}{k_{2}+k_{1}k_{2}}J_{C1R2} \right)\dot{\omega}_{S1S2}\\ &-\left(\frac{1+k_{1}+k_{2}}{k_{1}k_{2}}J_{R1}+\frac{k_{1}}{k_{2 }+k_{1}k_{2}}J_{C1R2}\right)\dot{\omega}_{R1}.\end{aligned} \tag{36}\]  
Equation (36) is the feedforward control law for \(T_{in}\). Note that according to Equation (34), \(\dot{\omega}_{R1}(t_{1})=0\). As a result, \(T_{in}(t_{1})=\frac{1+k_{1}+k_{2}}{1+k_{1}+k_{2}+k_{1}k_{2}}T_{driv}(t_{1})\), which is exactly the same as Equation (30). This implies that value of \(T_{in}\) at the end of the torque phase and at the beginning of the inertia phase are the same. Thus, continuity of the motor torque is ensured.  
Considering that inertias \(J_{S1S2}\) and \(J_{C1R2}\) are small and negligible, the second term regarding \(\dot{\omega}_{S1S2}\) in Equation (36) can be omitted. This also cancels the acceleration observer and simplifies the controller. The final feedforward control law is  
\[T_{in}=\frac{1+k_{1}+k_{2}}{1+k_{1}+k_{2}+k_{1}k_{2}}T_{driv}-\left(\frac{1+k _{1}+k_{2}}{k_{1}k_{2}}J_{R1}+\frac{k_{1}}{k_{2}+k_{1}k_{2}}J_{C1R2}\right) \dot{\omega}_{R1}. \tag{37}\]
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### SI Applications in Node Localization  
In WSN, location information can be used for target tracking, network topology construction. Positioning technology is the basic technology of WSNs, which plays a critical role in supporting the follow-up work and application of WSNs [39].  
Reference [40] proposed dimension-based PSO (DPSO) and hybrid DPSO (HDPSO) for node positioning in a 3D environment. The DPSO adopts dimension-based optimization technology to determine the location of the target node by considering each dimension, which works well in all applications. HDPSO adopts the grouping technology based on dimension estimation to realize rapid convergence, and it is suitable for the network scene of intensive deployment. The simulation scenario considers noise measurement, signal irregularities, and other interference factors. Compared with conventional PSO-based methods, the DPSO and HDPSO have better positioning accuracy and average time.  
Reference [41] proposed a butterfly optimization algorithm (BOA) for node positioning in WSN. The BOA algorithm is used to find the optimal position of the local node. By comparing this method with the PSO algorithm and FA algorithm in terms of positioning error, positioning node, and calculation time. The results showed that the BOA based node positioning scheme has better calculation time and accuracy.
# Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications
## 2 Materials and Methods
### Integrating Nonlinear Methods with Neural Networks
This approach ensures both accuracy and reduced energy consumption, enhancing the robot's performance and durability. Weight matrices \(\left(W_{1},\ W_{2},\ldots,\ W_{j}\right)\) are used in each layer of a neural network to transform the input data into a format that the network can use to make decisions or predictions. For instance, \(W_{1}\) is the weight matrix used in the first layer of the network to transform the initial input vector into the first hidden layer's output (\(h_{1}\)). Similarly, \(W_{2}\) transforms \(h_{1}\) into \(h_{2}\), and so on. Neural networks use weight matrices in conjunction with nonlinear activation functions (like the ReLU) to introduce nonlinearity into the network. This nonlinearity allows the network to learn complex patterns beyond what a linear model could achieve. During the training phase, these weight matrices are adjusted to minimize the loss function--the measure of how far the network's predictions are from the actual values. This is typically performed using optimization algorithms like gradient descent, where \(W_{j}\) is updated iteratively. This is described by the following equation:  
\[W_{j}^{(t+1)}=W_{j}^{(t)}-\mu\frac{\partial\mathcal{L}}{\partial W_{j}} \tag{11}\]  
\(W_{j}\) denotes the weight matrix at the j-th layer at iteration \(t\). The gradient \(\frac{\partial\mathcal{L}}{\partial W_{j}}\) tells us how to adjust \(W_{j}\) to reduce errors in the predictions, and \(\eta\) is the learning rate that determines how big each update should be. \(W_{1}\) and \(W_{j}\) are crucial for transforming and processing the input data through each layer of the network, allowing the model to learn from the data and make increasingly accurate predictions or control decisions. The use of multiple weight matrices enables the network to handle a variety of tasks and adapt to different data patterns and complexities, which is especially vital in applications like robotic control where the dynamics can be highly variable and complex. The main loop of the MATLAB code (Algorithm 1) performs the execution of the planned motions:  
Figure 6: Neural network framework for solving nonlinear dynamics optimization in robotic control. Here, the robot states (positions, velocities, etc.) are updated over time using a numerical integration method (ode113), which approximates the continuous time dynamics of the robot. The loss function is defined as the mean squared error between the desired and actual joint angles. The training process involves backpropagation, where the gradients of the loss function are computed with respect to the network parameters and used to update the weights. Loss function. The mean squared error (MSE) between the predicted torques \(\hat{y}\) and the actual torques y is determined as follows:  
\[\Gamma=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-\mathrm{y}_{i})^{2} \tag{12}\]
## 4 Challenges and Future Research Directions
### Quality 5.0  
Integrating Quality 5.0 into UAV-based transportation systems presents promising avenues for future research, particularly in making these systems more human-centric and enhancing cybersecurity [105]. One key area is the development of AI-driven algorithms for optimizing UAV flight routes, communication networks, and predictive maintenance to improve efficiency and system reliability [77]. Additionally, more work is needed to develop real-time data analytics frameworks that can process large volumes of traffic and environmental data, enabling faster incident detection and response [106]. Another important direction is the exploration of UAVs as communication relays in areas with limited or disrupted connectivity, ensuring consistent network performance [40]. Lastly, human-centric interaction models, where humans supervise UAV operations through intelligent dashboards, require further investigation to ensure seamless human-machine collaboration in future intelligent transportation networks [107].
## 6 The Evaluation of Unmatched Receiving of ACE-BOC
### Performance Evaluation  
#### 6.2.1 Acquisition Performance Evaluation  
For a CEM signal as ACE-BOC, different acquisition performance can be achieved by different processing modes. In Figure 2, an evaluation of acquisition performance is conducted for ACE-BOC(15,10,[1,3,1,3]) by utilizing Equation (15), where three receiving strategies are considered, which are matched receiving, IMR and BLR. The matched receiving makes full use of the wide-band ACE-BOC signal. IMR and FMR process the component \(s_{\text{LQ}}\), which is modulated on the lower band and quadrature phase. In this case, \(s_{\text{LQ}}\) occupies 3/8 of the total transmission power. Thus, the central frequency of IMR and matched receiving is at \(f_{0}\), which is the central frequency of the wideband ACE-BOC spectra. The central frequency of BLR is \(f_{0}-f_{sc}\), at the center of lower main lobe. To explicitly show the evaluation scenario of ACE-BOC signal, the related parameters are enumerated in Table 1.  
As can be seen from Figure 2, when the receiving bandwidth is smaller than 28 MHz, BLR achieves the smallest correlation SNR loss, indicating that BLR has the best acquisition performance at small receiving bandwidth. When the receiving bandwidth is higher than 28 MHz, matched receiving achieves better acquisition performance than IMR and BLR, whose correlation SNR loss shows no obvious distinction, because both BLR and IMR make use of a signal component with 3/8 of the total power of ACE-BOC signal. Also, compared with IMR, BLR performs better when the receiving bandwidth is smaller than 46 MHz, which is close to the bandwidth covering two main lobes of ACE-BOC signal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Receiving Central Frequency(\(f\))** & **Receiving Components** \\ \hline BLR & \(f_{0}-f_{sc}\) & \(s_{\text{LQ}}\) \\ IMR & \(f_{0}\) & \(s_{\text{LQ}}\) \\ FMR & \(f_{0}\) & Integral signal \\ \hline \hline \end{tabular}
\end{table}
Table 1: Receiver configuration of the three receiving strategies of ACE-BOC, which is used in the evaluation in Section 6.2.  
Figure 2: The correlation output SNIR loss of ACE-BOC(15,10,[1,3,1,3]), where the central frequency of receiving of Matched receiving and IMR is at \(f_{0}\), and the central frequency of BLR is at \(f_{0}-f_{sc}\). The IMR and BLR receives the component \(s_{\text{LQ}}\), whose power ratio among the useful transmitting power is 3/8.  
From the results above, considering the performance and complexity of the three receiving modes, instructive advice can be obtained for receivers. It can be seen that in terms of acquisition performance, IMR is not recommended because it shows no obvious advantage over the BLR at any bandwidths, while IMR requires more receiver complexity than BLR.
## 3 Experiments
### Model Complexity and Performance Analysis  
To comprehensively evaluate the efficiency and effectiveness of the proposed model, we conducted a comparative analysis of model complexity and segmentation performance across multiple representative baselines. Specifically, we considered three metrics: the number of parameters (M), the theoretical computational complexity measured by GFLOPs, and the segmentation accuracy evaluated using the mIoU (%) metric. The results are summarized on two datasets, HRC_WHU and SPARCS, as shown in Figures 15 and 16.  
On the HRC_WHU dataset, as shown in Figure 15, our model achieves the highest mIoU of 95.30%, while maintaining a relatively moderate parameter size of 95 M and a computational cost of 19.38 GFLOPs. Compared to models such as PSPNet and OCRNet, which achieve comparable performance (94.53% and 94.49%, respectively), our method shows a better balance between accuracy and efficiency. Notably, our FLOPs are only about 20% of that of SegNet (286 GFLOPs), and lower than most mainstream CNN+Transformer hybrid models.  
On the SPARCS dataset, with larger input dimensions (256 \(\times\) 256), all models experience increased computational complexity. After adjusting FLOPs accordingly, as reported in Figure 16, our method still achieves the highest mIoU of 91.35% with only 25.19 GFLOPs. In contrast, models such as FCN8s and PVT reach lower mIoU (87.94% and 86.96%, respectively) while requiring significantly more computation (162.5 and 318.5 GFLOPs).  
In both datasets, our model consistently demonstrates superior segmentation accuracy while maintaining a relatively low computational burden. This confirms the effectiveness of the dual-branch design and the lightweight feature aggregation modules in enhancing feature representation while controlling complexity.  
Figure 14: The segmentation prediction plot of the model on the SPARCS dataset: (**a**) the original image; (**b**) labels; (**c**) models.
## 5 Conclusions  
In this study, the soil moisture at Dahra in Senegal was retrieved using S1A time-series data. The LUT method was used for the retrieval, with the backscattering coefficients being simulated based on the MIMICs and the AIEM. The evaluation of S1A soil moisture with regard to the temporal trend and spatial variation was conducted based on ground measurements and microwave products, respectively. The S1A soil moisture shows a good agreement with the temporal trend of ground measurements at the Dahra site and shows a similar spatial pattern at a coarse resolution to SMAP and SMOS, which are regarded as the best representatives of ground measurements among the microwave products. Because of the insufficient ground measurements, the retrieving algorithm needs more data to confirm its robustness over various land covers. Despite this, the preliminary results will support further studies of Sentinel-1 for soil moisture monitoring. The EOF analysis was conducted to investigate the spatiotemporal pattern of S1A soil moisture. For the geophysical control factors, it was found that the local factors of soil and vegetation are more correlated with the primary soil moisture pattern, while none of these factors have dominant controls on the soil moisture patterns in this area, due to the complexity of the regional water cycle. The discussion in this paper confirms that it is possible to retrieve and study the spatiotemporal patterns of soil moisture using time-series Sentinel-1 data, which will become more time continuous with the constellation of Sentinel-1A and Sentinel-1B.  
The authors would like to thank the National Natural Science Foundation of China (No. 91438203, No. 61371199, No. 41501382, No. 41601355, No. 41771377); Hubei Provincial Natural Science Foundation (No. 2015 CFB328, No. 2016 CFB246); National Basic Technology Program of Surveying and Mapping (No. 2016 KJ0103); and Technology of target recognition based on GF-3(No. 03-Y20A10-9001-15/16). The authors would also like to thank the reviewers and editors for their professional suggestions. [PERSON] contributed materials; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All of the authors contributed to the editing of the manuscript. The authors declare no conflict of interest.
## 4 Results
### Ranging Accuracy Analysis
The code rate and code length of the GPS L5C signal are consistent with those of B2a, so it has a similar ranging performance, which is mutually confirmed with the experimental results. Therefore, this zero-baseline experiment has certain reliability. In addition, the standard deviation of BIC code signal path differences are also significantly greater than that of other frequency points. These results show that B1C has poor ranging accuracy in the zero-baseline experiment. We try to explain this from the difference in signal structure. The particularity of the BIC signal structure may cause some deviation in the ranging results. In Section 2.1, we know that the pilot component of the BIC signal is modulated by QMBOC. In fact, the QMBOC subcarrier is composed of two bipolar subcarriers, so the BIC signal can be regarded as a combination of three bipolar components [31]:  
\[ \begin{array}{c}\mathrm{S}_{(\mathrm{BIC})}^{(i)}(t)=\frac{1}{2}\mathrm{D}_{ (\mathrm{BIC})}^{(i)}(t)\mathrm{C}_{(\mathrm{BIC})}^{(i)}(t)\cdot\mathrm{sign} (\sin(2\pi\mathrm{f}_{a}t))\\ +\sqrt{\frac{1}{12}}\mathrm{C}_{(\mathrm{BIC})}^{(i)}(t)\cdot\mathrm{sign}( \sin(2\pi\mathrm{f}_{b}t))\\ +\mathrm{j}\sqrt{\frac{2}{44}}\mathrm{C}_{(\mathrm{BIC})}^{(i)}(t)\cdot\mathrm{ sign}(\sin(2\pi\mathrm{f}_{a}t))\end{array}  \tag{12}\]where \(\rm f_{a}\)= 1.023 MHz and \(\rm f_{b}\)= 6.138 MHz. In some research, the effect of the subcarrier is interpreted as a selective communication channel that distorts the useful signal [33]. The existence of subcarriers may affect the accuracy of ranging code at SDR reception. For ranging codes, BIC and B2a have the same code length, but the code rate of B2a is ten times that of BIC. A lower code rate may affect the accuracy of signal ranging. In this study, the coherence time of B2a and BIC is 10 ms. BIC signal has wider bandwidth. Although the wide bandwidth of BIC is conducive to combating multipath effects, the existence of subcarriers and lower code rate causes the positioning result to shift. Affected by the above factors, BIC has lower ranging accuracy in this zero-baseline experiment. Although some studies indicate that the ranging accuracy of B2a is lower [34], its testing environment should be affected by adverse environmental factors, which needs further discussion. We will conduct a more detailed study on this issue in future work.
# Seismic Response Analysis of a Curved Bridge under Near-Fault and Far-Field Ground Motions
## 5 Conclusions  
This work was devoted to studying the seismic performance of a curved bridge shaking table test model in the excitation of near-fault and FF ground motions. A 1/10 scale bridge model was established, and three types of ground motions were used to conduct shaking table tests. The following conclusions were drawn:  
1. The seismic performance of the curved bridge is more significant under NP ground motion excitation, which is more destructive than that under FF ground motion excitation. Therefore, paying attention to the impact of NP ground motions on curved bridges in seismic design is necessary.
2. The displacement of the curved bridge has spatial characteristics. The curved bridge moves horizontally and rotates in the plane at the same time in unidirectional seismic excitation(\(\mathrm{X_{P}}\)). NP ground motions in bidirectional excitations (\(\mathrm{X_{P}}+\mathrm{Y_{S}}\)) are easy to excite the rotation effect of curved bridges.
3. The displacement of the beam end and the bearing is significantly affected by the motion state of the curved bridge. The in-plane rotations of the curved bridge likely stimulate the displacement of the beam end and the bearing at the lower pier, which increases the risk of girder unseating and bearing shedding.
4. The fixed piers produce large tangential displacements under seismic action, are considerably sensitive to seismic action, are detrimental to the structure, and should  
Figure 10: Pier displacement response: (**a**) Pier displacement in case 1; (**b**) Pier displacement in case 2.  
be paid attention to. The height of piers has a certain amplification effect on the pier displacement, and a larger displacement is more likely to occur at the higher pier.  
Writing--original draft, P.S.; methodology, X.Z.; investigation, Y.C.; supervision, B.X. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Beijing Natural Science Foundation (8224087) and Central Public-interest Scientific Institution Basal Research Fund (CEAIEF20220206, CEAIEF2022030100). The support is gratefully acknowledged.  
The authors declare no conflict of interest.
## 3 Data Results and Analysis
### Key Deformation Area Cross-Sectional Analysis  
Using the PS-InSAR monitoring of deformation data in two time periods, 31 March 2017 and 30 May 2017, a point was evenly selected every 50 m. The longitudinal section (along the canal direction) was analyzed for the key deformation area of about 1 km canal within Figure 6b. The point locations of longitudinal sections are shown in Figures 6c and 9, respectively. It is obvious that the locations of the four minimum points in Figure 9 correspond to Figure 6c areas A, B, C, and D, which show obvious sinking phenomena.  
Using the settlement in the period of 6 January 2017, the four key monitoring surfaces in Figure 3 were analyzed in cross-section (canal direction). As shown in Figure 10, the west bank of the canal is relatively stable. There are no PS points in the canal, all of them are 0 mm. The east bank of the canal shows an obvious downward trend, and the closer to the water, the larger the canal deformation is, and vice versa, the smaller it is.  
Figure 10: Key monitoring surface cross-sectional sedimentation map.  
Figure 9: Settlement map of the longitudinal section in key deformation area.  
After 14 more images are collected, the deformation rate results of the high fill canal obtained by using PS-InSAR technology are shown in Figure 11b. The sampling time span is from October 2016 to February 2018. Compared with 11a, the shoulders and wave walls on the east and west sides of the canal show different degrees of subsidence, and the deformation on the east bank is more obvious than that of the west bank. The maximum deformation amounts to 74 mm. Therefore, the PS-InSAR technique cannot only obtain the extent and rate of settlement of the South-North Water Diversion Project Canal, but it also concludes that the east bank sinks first and the right bank sinks later.