esa-sceva/satcom-chunk-collection huggingface dataset:
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 1 Introduction
By integrating the e-textile sensor directly into the robot's framework, we leverage its inherent flexibility to monitor changes in resistance caused by the robot's bending actions. To improve shape detection accuracy, we have developed a data-driven Convolutional Neural Network (CNN) approach. This CNN method analyzes the complex patterns of resistance variations recorded by the sensors, allowing it to precisely determine the robot's shape and curvature in real-time. This advancement enhances the accuracy and adaptability of feedback control in continuum robots, enabling continuous state monitoring and necessary adjustments in dynamic environments. The structure of this paper is organized as follows: Section 2 details the design of the e-textile sensor with its layered construction. Section 3 outlines the deep CNN learning model utilized for shape sensing and control of the robot. Section 5 showcases the experimental results, highlighting the effectiveness of the proposed soft sensor in curvature sensing and shape control of continuum robots. Finally, Section 6 concludes the paper and proposes directions for future research.
# Influence of Multiple-Support Excitation on Seismic Response of Reinforced Concrete Arch Bridges
###### Abstract  
Although post-earthquake observations identified spatial variation of ground motion (i.e., multiple-support excitation) as a frequent cause of the unfavorable response of long-span bridges, this phenomenon is often not taken into account in seismic design to simplify the calculation procedure. This study investigates the influence of multiple-support excitation accounting for coherency loss and wave-passage effects on the seismic response of reinforced concrete deck arch bridges of long spans founded on rock sites. Parametric numerical study was conducted using the time-history method, the response spectrum method, and a simplified procedure according to the European seismic standards. Results showed that multiple-support excitation had a detrimental influence on response of almost all analyzed bridges regardless of considered arch span. Both considered spatial variation effects, acting separately or simultaneously, proved to be very important, with their relative significance depending on the response values and arch locations analyzed and seismic records used. Therefore, it is suggested that all spatially variable ground-motion effects are taken into account in seismic analysis of similar bridges.  
Keywords:seismic analysis; multiple-support excitation; spatial variability; large-span reinforced concrete deck arch bridges; time-history method; generation of ground motion time histories +
Footnote †: journal: Applied
# Hypernetwork Representation Learning with the Set Constraint
## 1 Introduction
Since there are complex interactions between the hypernetwork topology structure and the hyperedges, it is difficult to incorporate the hyperedges into the existing topology-based network representation learning methods. To deal with the above challenge, this paper proposes a universal hypernetwork representation learning method with the set constraint HRSC to effectively incorporate the hyperedges into the process of hypernetwork representation learning, which is formulated as a joint optimization problem solved by the stochastic gradient ascent (SGA) algorithm. To be more specific, a negative sampling method is utilized to capture the hypernetwork topology structure, which aims to exploit the correlations between the nodes by maximizing the prediction probabilities of the center nodes given their contextual nodes in the random walk node sequences. Besides, the negative sampling method is still utilized to capture semantic correlations between the center nodes and their associated hyperedges by maximizing the prediction probabilities of the center nodes given the hyperedge set associated with the center nodes. The contribution of this paper mainly includes the following three aspects:  
* The hypernetwork is transformed into a traditional network abstracted as a two-section graph approximating the hypernetwork topology structure. Then, on the basis of this traditional network, a hypernetwork representation learning method HRSC is proposed to learn node representation vectors by comprehensively utilizing both the hypernetwork topology structure and the hyperedges. * HRSC incorporates the set of the hyperedges associated with the nodes into the process of hypernetwork representation learning, regarding the learning process of node representation vectors as a joint optimization problem. * Efficient and scalable learning algorithm for HRSC has been developed. Our learning algorithm is able to handle large-scale hypernetworks efficiently. The rest of this paper is organized as follows. In Section 2, we review related works. Section 3 formally defines our studied problem. Section 4 introduces the preliminaries. In Section 5, we introduce the hypernetwork representation learning method with the set constraint in detail. The experimental results are given in Section 6. Finally, we conclude this paper in Section 7.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## SEMS Method
### Calculate the SEMS Transportation Time
For emergency material scheduling, we analyzed the fairness of relief distribution, so the demand satisfaction of the emergency materials of each disaster depot can be regarded as the \"income\" of each group in Equation (3). The scenario coefficient can reflect the basic information of each disaster depot and can be seen as \"population\" of each group. The following Equation (4) defines the SEMS fairness:  
\[F=\sum\limits_{k=1}^{K}\left(\frac{\alpha_{jk}^{p}}{\sum\limits_{j=1}^{l} \alpha_{jk}^{p}}\ln(\frac{\alpha_{jk}^{p}}{\sum\limits_{j=1}^{l}\alpha_{jk}^{ p}}/\frac{\sum\limits_{i=1}^{l}\sum\limits_{r=1}^{R}x_{ijrk}^{p}}{\left|d_{jk}^{p} \right.}\right) \tag{4}\]  
where \(F\) is fairness, \(\alpha_{jk}^{p}\) is the scenario coefficient of disaster depot \(j\) for material \(k\) in stage \(p\), \(x_{ijrk}^{p}\) is the amount of material \(k\) delivered from \(i\) to \(j\) with mode \(r\) in stage \(p\), \(d_{jk}^{p}\) is the actual demand for material \(k\) in disaster depot \(j\) in stage \(p\).
# Hybrid Improved PSO Algorithm for Soil Property Parameter Estimation
## 3 Improvement of the PSO Algorithm
### Inertia Weight of the PSO Algorithm  
The inertia weight \(\omega\) determines how much influence the particle's velocity has on the velocity in the next iteration. There are two types of inertia weight mechanisms: static and dynamic. When the inertia weight is set to a relatively large value, the swarm exhibits strong global search capability in the early stages of iteration but low convergence precision in the later stages. On the other hand, with a smaller inertia weight, the search capability is weaker initially, but the convergence precision improves in later stages. As a result, static inertia weight fails to meet the PSO requirements for both fast search speed and high precision. In their research, [PERSON] and [PERSON] [21] found that using an inertia weight of  
Figure 2: The discrete distribution plot and frequency histogram of SPM sequences and random sequences.  
0.9 at the beginning of the PSO process and gradually reducing it to 0.4 by the end yields better performance. They used the number of iterations as the basis for adjusting the inertia weight, as shown in Equation (19):  
\[\omega(t)=(\omega_{max}-\omega_{min})\Big{(}\frac{K-k}{K}\Big{)}+\omega_{min} \tag{19}\]  
where \(K\) represents the maximum number of iterations, and \(k\) denotes the current iteration. In this method, if the algorithm converges quickly, the inertia weight may differ significantly from 0.4 during the convergence phase. Conversely, if the algorithm converges slowly, the inertia weight may decrease too early, potentially affecting the final accuracy. Therefore, in this paper, fitness is used as the evaluation metric, and the improved formula is presented as in Equation (20):  
\[\omega=\omega_{min}+(\omega_{max}-\omega_{min})\times\frac{fit_{k}}{fit_{1}} \tag{20}\]  
In the equation, \(\omega_{max}\) and \(\omega_{min}\) denote the maximum and minimum values of the inertia weight, respectively. \(fit_{1}\) is the fitness value at the first iteration, and \(fit_{k}\) is the fitness value at the \(k^{th}\) iteration.
# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 2 Materials and Methods
### Working Procedure  
The first step was the estimation of the yield to adjust the fertilizer dose to the crop requirements. For this purpose, 15 machine learning algorithms were used (Figure 2). These algorithms were trained using data from a yield monitor installed on a harvester (variable to be predicted), 14 predictor variables obtained from the S2 satellite (NDVIS from February to June), and one from a DEM. Data were collected in 13 agricultural plots (Figure 1B, red dots). Seventy percent of all the data collected were used to find the algorithm with the best wheat yield prediction ability. The training dataset was randomly selected and comprised information of all plots. The five best-performing algorithms were then validated using the remaining data (30% of the dataset), with the aim of finding the one that had the best predictive capability.  
Figure 1: **(A)** General location of the province of Araba/Alava in Spain. **(B)** Location of 14 wheat plots. The red dots represent the plots belonging to farmer 1 used to train and test the algorithm. The green dot represents the implementation plot (farmer 2).  
Figure 2: Flowchart of the proposed working methodology to obtain a fertilizer prescription map in one agricultural plot without yield data.  
In the next step (Figure 2), the predictive capacity of the selected algorithm was evaluated for a whole plot. For this purpose, the algorithm was retrained using all the data except the data of the plot to be predicted. This process was repeated for each of the 13 plots (Figure 1B, red dots).  
Finally, the algorithm was run using auxiliary data (S2, DEM) from farmer 2, who did not have a yield monitor but wanted to adjust the fertilizer rate. The result of the prediction map was compared with the farmer and, using this map as a baseline, a fertilizer prescription map was made together with him.
# Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
## 1 Introduction
Our proposed DL models obtain performance that is slightly better than that of an MMSE estimator, especially when the channel is stationary with weak or strong turbulence. In addition, when the turbulent channel is non-stationary and variable, we find a significant improvement in system performance, and the BER is decreased by more than 8 dB compared with regular ACO-OFDM using the MMSE estimator. In addition, our DL decoders run faster than the regular ACO-OFDM decoder, which leads to a significant reduction in the energy consumption of the FSO system. The rest of this paper is built in the following manner. Section 2 describes the FSO turbulent channel characteristics and the ACO-OFDM encoder-decoder system. The DL decoders we developed are presented in Section 3. In Section 4, we summarize the results of our simulations. Section 5 presents the conclusions of the study and future perspectives of the work.
## 4 Results and Discussion
### Application Experiment of Multi-Object Positioning for Monocular Image
Because of the error in the calculated camera position, the calculated principal optical axis direction differs significantly from the true principal optical axis pitch angle, which results in a small position error near the image principal point and a large position error at the image edge in the vertical direction. In Trial 1, the target position error at the edge of the image is reduced because of the constraints of inverse depth and structure semantics, which alleviates the problem. It also can be seen from the experiment that the absolute error of object registration accuracy of this method is 0.66 m, while the average error of the camera's own pose is 3.36 m. This is because, although the camera pose error is larger, after position correction, the two main optical axes can intersect near the image center target point, so the target positioning accuracy is higher than the camera's pose. This indicates that this method does not need to rely on the camera's own high-precision pose when locating the object, and our method can achieve high-precision registration.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 6 Open Research Issues and Challenges
### UAV Positioning  
In position-based routing, accurate GPS information is required to discover the shortest routing path [61]. In some cases, such as disaster areas, the GPS signal can be weak or totally absent. Therefore, GPS information alone is not sufficient to locate the neighboring node. A low GPS signal or RSSI has a huge impact on position-based routing protocols [62]. It is not possible to discover a short path based on inaccurate signals.
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 2 Related Work
Our objective hereby is not to propose a novel method for DLP, but rather to introduce a new performance visualization approach capable of effectively visualizing the DLP task and supporting the performance evaluation of existing methods. Nonetheless, Birth-Death diagrams are particularly pertinent for assessing and diagnosing memory-based DGNNs. Indeed, these models work by maintaining a memory state summarizing the history associated with specific nodes or edges over time. The accuracy and usefulness of this memory state is greatly influenced by the times at which these nodes/edges start (Birth time) and stop (Death time) interacting. We emphasize that our study does not consider negative sampling for training, which has its own challenges [39], but rather for _evaluation_ in Link Prediction. Challenges in Evaluating DLP Algorithms. Although many methods for static and dynamic Link Prediction have been proposed in the past, the formal definition and evaluation of this task has been subject to much debate and countless refinement over the years. Many evaluation methods have been proposed, including set-based metrics [26], receiver operating characteristic (ROC) curves and associated area under the ROC (AUC-ROC) curve [40; 41], and average precision [42]. While the above studies used the time information mainly for splitting the train-test data into a training and a test set, [PERSON] et al. [43] presented ways to incorporate the time aspect into a method and evaluation process, demonstrating its positive impact on performance. Subsequently, [PERSON] et al. [15] suggested separating the DLP problem into two tasks: the prediction of either recurring edges or newly observed edges. They propose a metric combining AUC-ROC and area under the precision-recall curve to incorporate these two aspects. In these studies, the impact of negative sampling was often overlooked in the evaluation step. More recently, [PERSON] et al. [8] proposed more challenging negative samples for deep learning-based DLP methods. They introduced three strategies: random, historical, and inductive. In this paper, we extend the literature by proposing a visualization-based method for separating the possible negative samples into categories, with an emphasis on distinctively sampling from overlap and historical edges/nodes. Moreover, we introduce a principle means of scrutinizing the changes in performance over time depending on the negative sampling strategy used for evaluation.
# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
## 3 Results
### Generalization Experiment  
In light of the considerable diversity of companion weeds of soybean in natural environments, to further validate the utility of the model proposed in this paper for soybean weed detection, generalization experiments are conducted on the Aerial Weeds dataset [53]. The dataset uses Unmanned Aerial Vehicles for aerial photography, which contains five kinds of weeds and a variety of crops. The environment is extremely similar to that of soybean weeds, which is conducive to verifying the performance of the model in this paper. The experimental environment and parameter settings remained consistent with those outlined in Section 3.1, with experimental results in Table 5.  
It can be concluded from Table 5 that the EMCF model proposed in this paper is equally effective in the Aerial Weeds dataset. Not only did mAP50 and mAP50:95 improve by 6.4% and 5.2% compared to the benchmark model, respectively, but the accuracy and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP50 (\%)** & **mAP50:95 (\%)** & **GFLOPs (G)** & **Paras (M)** & **FPS** \\ \hline Baseline & 61.5 & 56.4 & 50.1 & 28.3 & 57.0 & 19.9 & 44.5 \\ Ours & 66.4 & 59.3 & 56.5 & 33.5 & 52.5 & 18.0 & 49.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of generalization experiments.  
Figure 7: Visualization results of ablation experiments. Darker colors and broader color ranges indicate higher detection accuracy. **(a)** an original image; **(b)** baseline model visualization; **(c)** our model visualization.
# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 4 Feature Extraction
### Linear Predicative Code  
In traditional linear prediction modeling, the autoregressive (AR) model is widely used in speech signal processing [46], including applications such as speech coding and speech recognition. In this research, we employ the AR model to represent the characteristics of breath sounds. The data \(x(n)\) is modeled as a linear combination of its past \(p\) samples. That is,  
\[x(n)=-\sum_{i=1}^{p}a_{i}x(n-i)+e(n), \tag{3}\]  
where \(e(n)\) is the modeling error/residues. Minimizing the above error with respect to each coefficient leads to the following set of normal equations:  
\[c(i,j)=\sum_{n}x(n-i)x(n-j). \tag{4}\]  
The minimization of the total error results in a set of \(p\) equations that must be solved for the coefficients \(a_{i}\). Equation (4) can be expressed in matrix form by defining the following vectors and matrix:  
\[\textbf{a}=\left[\begin{array}{cccc}a_{1i}&a_{2i}&\cdots&a_{pi}\end{array} \right]^{T} \tag{5}\]  
and  
\[\textbf{c}=\left[\begin{array}{cccc}c_{0i}(0,1)&c_{0i}(0,2)&\cdots&c_{0i}(0, p)\end{array}\right]^{T}, \tag{6}\]  
\[\textbf{C}=\left[\begin{array}{cccc}c_{kl}(1,1)&c_{kl}(1,2)&\cdots&c_{kl}(1, p)\\ c_{kl}(2,1)&c_{kl}(2,2)&\cdots&c_{kl}(2,p)\\ \vdots&\vdots&\ddots&\vdots\\ c_{kl}(p,1)&c_{kl}(p,2)&\cdots&c_{kl}(p,p)\end{array}\right], \tag{7}\]  
and then Equation (4) becomes  
\[\textbf{Ca}=-\textbf{c}. \tag{8}\]  
The solution for **a** can be simplified as  
\[\textbf{a}=-\textbf{C}^{-1}\textbf{c}. \tag{9}\]
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### Optimization Filters and Fusion-Based Algorithms for IPSs
#### 3.3.1 Least Squares Algorithm (LS)  
The least squares method is used to reduce the margin of error in indoor positioning systems by finding the closest path between the coordinates of the tag movement to be tracked. After obtaining the coordinates of the tag movement (\(\kappa_{t}\), \(y_{t}\)) from Equation (8) and the linear equation (Ax = b), the estimated coordinates can be calculated using the least squares method as follows:  
\[A=\begin{bmatrix}2(x_{2}-x_{1})&2(y_{2}-y_{1})\\ 2(x_{3}-x_{1})&2(y_{3}-y_{1})\end{bmatrix}\]  
\[x=\begin{bmatrix}x_{t}\\ y_{t}\end{bmatrix}\]  
\[b=\begin{bmatrix}d_{1}^{2}-d_{2}^{2}+x_{2}^{2}+y_{2}^{2}-x_{1}^{2}-y_{1}^{2} \\ d_{1}^{2}-d_{3}^{2}+x_{3}^{2}+y_{3}^{2}-x_{1}^{2}-y_{1}^{2}\end{bmatrix}\]  
\[x=\left(A^{T}A\right)^{-1}A^{T}b \tag{11}\]  
where A is an m \(\times\) n matrix, x is an n \(\times\) p matrix, and b is an m \(\times\) p matrix. The \"[I]\" in Equation (11) represents the matrix transpose. Using Equation (11), the coordinates of the location of the guessed tag can be calculated depending on the provided coordinates of the anchors' location using the least squares method.  
Figure 8: PDoA positioning algorithm.
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 3 Deep-CNN Shape Sensing
### CNN Model Architectures  
The CNN architecture used in our study is specifically designed for regression tasks, as our goal is to quantify the curvature of the soft robot rather than classify it into discrete categories. This regression output provides continuous values representing the curvature \(\kappa\) and the angle of the curvature plane \(\phi\), which are estimated from an IMU attached to the robot's tip during data collection. The network accomplishes this through two convolutional layers, each responsible for extracting increasingly abstract features from the input image. The first convolution layer uses kernel sizes of 16 and 8, with the hyperbolic tangent (tanh) activation function. These layers are followed by two fully connected layers with 16 and 8 units, respectively, that integrate these features to predict the robot's curvature, as illustrated in Figure 5.
## 1 Introduction
Relay(s) is (are) allowed to forward the signal if and only if direct transmission fails; otherwise, the source continues with the transmission of the next data packet. This approach reduces the energy consumption and total transmission time of a network. Incremental relaying protocols are extensions of incremental redundancy protocols, or Hybrid Automatic-Repeat reQuest (HARQ) [5]. In this research, we first analyse three different communication schemes given in [6]. A comparison of direct communication and incremental cooperative communication schemes for both on-body and in-body WBANs is given in this paper. Performance evaluation shows that two-stage incremental cooperative communication performs well in terms of PER at the cost of EE. To further improve the results achieved in [7], we propose three-stage incremental cooperation in WBANs. We derive analytical expressions in terms of PER and EE of the proposed three-stage cooperative communication scheme. Analysis and simulation results for the proposed scheme show that three-stage cooperative communication outperforms two-stage cooperative communication in terms of reliability and PER. Furthermore, we propose a new WBAN protocol, enhanced incremental cooperative critical data transmission in emergencies for static WBANs (EInCo-CEStat), in which we implement three-stageincremental cooperation and compare its performance to existing WBAN protocols: InCo-CEStat [8] and Co-CEStat [9]. Some of the related work and motivation behind this work is discussed in Section 2. Analytical analysis of the proposed scheme in terms of PER and EE is given in Section 3. Section 4 presents the simulation analysis of PER and EE for the proposed scheme. Implementation of these compared and proposed schemes in WBAN protocols is shown in further sections. Section 5 describes the system models of the proposed protocols. Simulation results are shown in Section 6. Finally, conclusion and performance trade-offs are given at the end of the paper.