esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Contact Angle of DACBB
However, once the misalignment angle is higher than 0.1 mrad, the contact angle is in existent in the lower half part. Meanwhile, in the upper half part, the contact angle increases rapidly with the increase of angular misalignment. For the right row of DACBB, the contact angle of upper half part decreases with the increase of angular misalignment. However, when the inner ring misalignment angle is greater than 0.3 mrad, some rolling balls will no longer contact with the raceway and the contact angles at the corresponding azimuth positions are inexistent. In the lower half part, contact angle significantly increases with the increase of misalignment angle. Figure 14: Contact angle of each row of DACBB under various external loads: (**a**,**b**) contact angle of the left and the right rows under external radial load; (**c**,**d**) contact angle of the left and the right rows under external axial load. As for the influence of negative angular misalignment of inner ring, the results shown in Figure 15c,d demonstrate that the contact angles of each row of DACBB show similar variations compared to those shown in Figure 15a,b. The main difference is that the situation is reversed for each row. In addition, the value of the contact angle under positive angular misalignment is not equal to that under negative condition.
# Hardware-in-the-Loop Test of a Prosthetic Foot
###### Abstract
A novel approach for testing of foot prostheses could therefore be Hardware-in-the-Loop (HiL). HiL is a combination of model-based and robot-based testing, where the amputee is modeled and the prosthetic foot is tested experimentally. Human modeling is extremely complex and requires a great understanding of gait dynamics. The modeling of the human gait requires some simplifications. Nevertheless, a HiL approach allows studying the dynamic interaction without the need for test subjects. The modeled amputee and the prosthetic foot exchange information in real-time to couple them, that is to analyze their dynamic interplay. This idea is visualized in Figure 1.  
While the idea of testing prosthetic feet using HiL was already proposed before by [7,11], the realization/implementation has not been performed yet. This paper therefore literally aims at taking this first step, i.e., implementing a HiL test for prosthetic feet. Specifically, the aim of this article is to show the basic feasibility of conducting a HiL test that is able to replicate the dynamic interaction between an amputee and a prosthesis. This first proof-of-concept makes use of a simple human gait model that is able to emulate the gait characteristics during the stance phase. A modified version of the Virtual Pivot Point (VPP) model is used in this work to simulate the amputee, and one gait cycle is performed. We hypothesized that the results of the HiL test would qualitatively resemble the gait characteristics of the VPP model and therefore emulate human gait dynamics. Figure 1: To investigate the gait dynamics of an amputee wearing a prosthesis, the application of HiL is proposed. The simulated amputee is coupled with the experimentally tested prosthetic foot. The coupling is achieved by an actuator (Stewart Platform in the figure) and a force–torque sensor. The prosthetic foot is moved by the actuator according to the calculations of the simulated amputee. The picture of the amputee is adapted from [12], license CC BY 4.0. The remainder of the paper is structured as follows. The groundwork with an introduction to HiL and human gait modeling is laid in Section 2. In Section 3, the implementation is described. For that purpose, the model of the amputee is explained and the Equations of Motion (EoMs) are derived. Furthermore, the parameters used and the experimental setup are described. The results of one gait cycle are presented and discussed critically in Section 4. Finally, a brief summary of the paper is given and a conclusion is drawn in Section 5.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 3 Problem Formulation  
Despite significant advancements in path planning algorithms, they still encounter critical challenges. A primary issue is that tree expansion near obstacles involves extensive collision detection, which significantly hampers search efficiency. Additionally, the generated paths often lack smoothness and tend to be excessively tortuous. This shortcoming contradicts the fundamental principles of vehicle kinematics. Consequently, the pressing question is how to enhance path planning algorithms to meet the stringent requirements of robot kinematics while addressing these inefficiencies and ensuring smoother trajectories. This paper will delve deeply into this issue with the aim of balancing the generation of efficient paths as well as the stability of robot operation.  
The structure of this paper is as follows: the remaining part of this section reviews the existing path planning methods and their advantages and disadvantages; the fourth part details our method and its implementation process; the fifth part presents our experimental results and compares the performance of our method with other methods; finally, the sixth part summarizes the main contributions of this paper and discusses future research directions.
# Assessing the Sustainability of Alternative Shaft Construction Methods
## 5 Discussion Regarding Sustainability
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analysis**} & \multicolumn{3}{c}{**CO\({}_{2}\) Emissions**} & \multicolumn{3}{c}{**Displacements**} & \multicolumn{2}{c}{**Lining Forces**} \\ \cline{2-7}  & **Volume (m\({}^{3}\))** & **CO\({}_{2}\)** & **CO\({}_{2}\)** & **\(\delta\)** & **\(\delta\)** & **Hoop** & **Bending** \\  & **(m\({}^{3}\))** & **(t CO\({}_{2}\)/m\({}^{3}\))** & **(t CO\({}_{2}\))** & **h/H(\%)** & **v/H(\%)** & **Force** & **Moments** \\ \hline SBE-Ref & 16,033 & 0.70–1.05 & 11,223–16,835 & 0.015 & 0.007 & A & A \\ SBE-Ring & 15,675 & 0.70–1.05 & 10,972–16,459 & 0.015 & 0.007 & A & A \\ SBE-0.75 & 7786 & 0.70–1.05 & 5450–8175 & 0.033 & 0.011 & A & A \\ \hline EBS-1.50 & 15,675 & 0.35–0.50 & 5486–7837 & 0.076 & 0.065 & A & NA \\ EBS-0.75 & 7786 & 0.35–0.50 & 2725–3893 & 0.096 & 0.083 & A & NA \\ EBS-0.35 & 3621 & 0.35–0.50 & 1267–1810 & 0.129 & 0.128 & A & A \\ EBS-0.35-2 m & 3621 & 0.35–0.50 & 1267–1810 & 0.153 & 0.142 & A & A \\ EBS-0.35-3 m & 3621 & 0.35–0.50 & 1267–1810 & 0.175 & 0.163 & A & A \\ \hline EBS-0.35-Seq & 3621 & 0.25–0.35 & 905–1267 & 0.103 & 0.097 & A & A \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the results. Figure 9: Comparison of the results of all analysis. Figure 9 summarizes the obtained data regarding CO\({}_{2}\) emissions and displacements. As expected, the SBE analyses present the best performance in terms of displacements due to the initial installation of the diaphragm wall (i.e., prior to any excavation taking place) but have a very detrimental impact in terms of CO\({}_{2}\) emissions. In contrast, the EBS methodology is much more environmentally friendly, particularly if PCL is employed, but it induces higher deformations, which increase with a reduction in the stiffness of the lining and/or with an increase in the excavation step. As a result, and given the small displacements precited in all cases, it would have been more acceptable to use EBS methods to construct the shaft or, at least, to reduce the thickness of the diaphragm wall to 0.75 m, which would itself lead to a reduction of 50% in the associated CO\({}_{2}\) emissions. Although the economical aspect was not analyzed in this study, it should be mentioned that, typically, the costs and the duration of works associated with EBS methods are smaller than those associated with SBE, which would make the decision to adopt EBS even more suitable for the present case.
## 3 Description of Comprehensive Techno-Economic (CTE) Model
### Business Level in the CTE Model  
The second level in this model is the Business Level (BL). There are four areas: the \"Products Development Area\", the \"Services Development Area\", the \"Sales and Customer Care Area\" and the \"Human Resources Area\".
## 3 Methodology
### NBRCS Calculation  
The parameter \(\sigma_{0}\) mentioned in Equation (2) above is also the NBRCS. In this study, the NBRCS is applied as the sea surface roughness-sensitive parameter, also known as the observable, to retrieve the sea surface wind speeds [30]. To calculate the NBRCS (\(\sigma_{0}\)), Equation (2) can be rewritten as follows [33]:  
\[\sigma_{0,\tau,f}=\frac{P_{\tau,f}^{g}(4\pi)^{3}R_{t}^{2}R_{r}^{2}}{P_{t} \lambda^{2}G_{t}G_{r}}/\iint\limits_{A}\Lambda^{2}(\tau)S^{2}(f)dA, \tag{3}\]  
where most of the terms are the same as those in Equation (2), however, \(P_{\tau,f}^{g}\) is the power calibration result of \(\left\langle\left|Y(\tau,f)\right|^{2}\right\rangle\), and \(R_{t}\) and \(R_{r}\) are the specular distances to the transmitter and receiver, respectively. These two range loss terms from the integration are based on the assumption that the ranges are approximately equal near the specular point. Because the averaged NBRCS used to retrieve the winds is only a small area around the specular point, the uncertainty of this assumption regarding the range loss is nearly negligible.  
It should be noted that the content in the double integration of Equation (3) is determined only by the geometry. Once the positions and velocities of the receiver and transmitter are determined, the specular point can be calculated numerically. Then, a grid is built around the specular point to compute the contributions in each delay and the Doppler frequency bin. As the other geometry terms, like the antenna gains and range losses, are not considered integrally, \(\sigma_{0}\) only has good accuracy near the specular point. Therefore, a 1.5-chip and 2000-Hz delay Doppler domain was selected to calculate the NBRCS (\(\sigma_{0}\)) in this study, which can also be considered as the DDMA.
## 3 The Proposed Optimization Algorithm
### Complexity Analysis  
The proposed two-stage algorithm to solve (7) is summarized in Algorithm 1. The first stage comprises steps 2 to 17, and the second stage comprises steps 18 to 25. In the first stage, the key operation is to calculate the achievable sum rate in (10), which needs a determinant operation with computational complexity \(\mathcal{O}(N!)\). Thus, the complexity for the first stage in the worst case is \(\mathcal{O}(N!K(M+1)T_{1})\). Here, \(T_{1}\) is a predefined maximum number of user association rounds. In the second stage, the key operation is to calculate \(\mathbf{v}_{m,k^{\prime}}\gamma_{m,k^{\prime}}\eta_{m,k}\) and \(p_{m,k^{\prime}}\). The complexity to calculate those parameters is \(\mathcal{O}\big{(}N^{3}\big{)}\), \(\mathcal{O}(N)\), \(\mathcal{O}(N)\) and \(\mathcal{O}(N)\), respectively. Thus, the complexity of the second stage is \(\mathcal{O}\big{(}\big{(}T_{2}^{in}(M+1)KN+(M+1)KN^{3}\big{)}T_{2}^{out}\big{)}\). Here, \(T_{2}^{out}\) and \(T_{2}^{in}\) represent the maximum iteration numbers for the outer and inner iterations, respectively.  
```
1 Initialization:\(\alpha_{m,k}=0\), \(p_{m,k}=P_{k}^{max}\), \(\rho_{k}\), \(\gamma_{m,k}\), \(\eta_{m,k}\), \(\forall m\in\mathcal{R}\), \(\forall k\in\mathcal{K}\), set \(\mathcal{X}_{1}=\emptyset\), \(\mathcal{X}_{2}=\mathcal{K}\); all the UEs set their preference lists \(\{Pre_{k}\}\);
2 fork=1 tocard(\(\mathcal{X}_{2}\))do
3 while\(\{Pre_{k}\}\
eq\emptyset\)do
4 UE \(k\) sends a request to the AP (denoted by AP \(i\)) with the highest priority in \(\{Pre_{k}\}\);
5 Calculate \(R_{i}^{up}\) in (10);
6 if\(R_{i}^{up}<=C_{i}\)then
7\(\alpha_{j,k}=1\);
8\(\mathcal{X}_{1}\leftarrow\mathcal{X}_{1}\cup k,\mathcal{X}_{2}\leftarrow\mathcal{ X}_{2}\setminus k\);
9 break;
10 else
11\(\{Pre_{k}\}\leftarrow\{Pre_{k}\}\setminus i\);
12
13 end if
14
15 end for
16
17 if\(\mathcal{X}_{2}\
eq\emptyset\)then
18 All the UEs within \(\mathcal{X}_{2}\) reset their preference lists and return to step 4;
19
20 end if
21 repeat
22 Update \(\mathbf{v}_{m,k}\), \(\forall m,k\) in (25);
23 repeat
24 Update \(\gamma_{m,k}\) and \(\eta_{m,k}\), \(\forall m,k\) in (15) and (16), respectively;
25 Compute \(p_{m,k}\), \(\forall m,k\) in (19);
26
27 until Convergence;
28
29 Compute \(R_{m}\), for all \(m\) to obtain \(\bar{R}\), and update \(p_{m,k}\) for \(m\in\bar{R}\), \(k\in\mathcal{U}_{m}\) in (22);
30 Output:\(\{\mathbf{v}_{m,k^{\prime}}^{*},p_{m,k}^{*},\mathbf{v}_{m,k}^{*}\}\)
```  
**Algorithm 1**The proposed two-stage algorithm to solve (7)  
According to the above analysis, the total computational complexity for our proposed algorithm is \(\mathcal{O}\big{(}\big{(}T_{2}^{in}(M+1)KN+(M+1)KN^{3}\big{)}T_{2}^{out}+N!K(M+ 1)T_{1}\big{)}\).
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 1 Introduction
This method effectively modulates/demodulates CTC messages while maintaining transparency to the ongoing ZigBee communication. * Last but not least, we implement our design on USRP N210 with GNURadio and evaluate the performance in practical environments. The experimental results indicate that our design is compatible with ZigBee devices and throughput of this CTC can reach up 1000 times higher than the state-of-the-art CTC design. The rest of this paper is organized as follows: Section 2 introduces some background and motivation. In Section 3, we present our main design. Our experiment results and some discussions are shown in Section 4. Section 5 shows the related work. The Discussion is stated in Section 6. At last, Section 7 concludes this paper.
# Detection of Frozen Soil Using Sentinel-1 SAR Data
## 2 Radar Signal Modeling
### Radar Backscattering Model  
In this study, the Integral Equation Model modified by [PERSON] (IEM_B) was used to simulate soil surface scattering in a context of bare agricultural fields. In addition to SAR instrumental parameters (radar wavelength, incidence angle and polarization), the radar signal depends on the soil dielectric constant and surface roughness (_Hrms_) [15; 16; 17; 18; 19]. Only the surface scattering was considered in this study, with one permittivity layer.  
The IEM_B was used since it provides simulated SAR backscattering coefficients with a good accuracy (RMSE about 2 dB) [20]. In fact, several studies reported important discrepancies between backscattering coefficients simulated by the original IEM developed by [PERSON] et al. [21] and those measured by SAR sensors [22; 23; 24]. According to [PERSON] et al. [25], this discrepancy is mainly due to the in situ measured correlation length \"\(L\)\" which is difficult to measure in the field with good accuracy. To reduce such discrepancies between simulated and measured backscattering values, [PERSON] et al. [8; 9; 26; 27; 28] proposed a semi-empirical calibration of the original IEM in order to improve the accuracy of the simulated backscattering values. This semi-empirical calibration consists of replacing the in situ measured correlation length by a fitting parameter, called Lopt. Lopt depends on surface roughness conditions and SAR configurations (incidence angle, polarization and radar wavelength). The calibrated IEM_B was carried separately at X-band in [27], C-band in [8; 9] and L-band in [26]. The proposed calibration reduces the number of IEM's input soil parameters from three to two parameters (root mean square surface height \"_Hrms_\" and soil moisture content \"_mv_\" only, instead of _Hrms_, \(L\) and _mv_).
## 2 Background  
### Combined Model Studies  
In the multi-classification domain, developing improvements on the basis of individual models makes it difficult to achieve the best results in all categories. This is an important reason behind the difficulty related to improving accuracy in the multi-classification domain. Chinese agricultural news classification is inherently a five-category task and, in order to improve the classification accuracy more significantly, we should consider the use of other methods to improve the classification accuracy of the model.  
Traditional approaches to improving model accuracy often involve first training multiple models with different hyperparameters, then selecting the single model that performs best on the validation set while discarding the rest [22; 23]. Alternatively, different techniques may be used in the different stages of a single model, in order to improve the accuracy of the model [24; 25]. While research on combining multiple models remains scarce, recent studies have shown that combined models (i.e., those that combine the outputs of multiple models) can outperform the best single models.  
The improvement in effectiveness through model combination is clear, and a Google study--which conducted systematic experiments using 82 models--has shown that, even with some low-accuracy models, their combinations may be useful. By combining a high-accuracy model with a low-accuracy model, the accuracy was improved by 7% in an experiment [20], comprising a significant improvement. Therefore, in order to improve the classification accuracy for agricultural news, we considered the use of a combination model approach for classification.  
When using a combined model for classification, it is important to adjust the weights of each model across the structure, in order to achieve better results, as some incorrect weight adjustments may lead to a reduction in the effectiveness of the model. Therefore, we used the GreedySoup strategy to adjust the weights of the model. Google proposed GreedySoup as a strategy for adjusting the output weights of multiple models in 2022 [21]. GreedySoup first sorts the models from highest to lowest, according to their experimental accuracies in the data set, then combines some of them and adjusts the weights of each model's output; if a weight adjustment can improve the accuracy of the combined model, then that weight is used. The GreedySoup weighting strategy allows the combined model to classify no less effectively than any of its sub-models. Therefore, for this paper, we adopted this approach to adjust the model weights, that is, weighting the output of each model and experimentally obtaining the weight set leading to the best classification accuracy.  
To improve the classification accuracy on agricultural texts, we apply this multi-model combination approach to the field of text classification and propose the E3W classification model, which combines four sub-models and uses the GreedySoup strategy to select the models that perform best in the sub-domains, as well as to weigh the outputs of the sub-models, resulting in significant accuracy improvements.
# Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM
## 2 Methods
### Sequential Forward Floating Selection  
The main goal of feature selection is to select a subset of \(d\) features from the original data set of \(D\) features (\(d<D\)) according to a certain evaluation criterion and improve the performance of the pattern recognition system, such as higher classification accuracy, lower computational cost and better model interpretability.  
Assuming that a suitable evaluation criterion function has been chosen, feature selection is reduced to a search problem that detects an optimal feature subset based on the selected measure. Many search strategies have been designed to avoid the exhaustive search even though the feature set obtained may be suboptimal. The sequential forward floating selection (SFFS) algorithm is a relatively new strategy, and has been widely applied in practice [20; 21; 22].  
The main procedure of SFFS can be briefly described as follows [23; 24].  
Suppose \(k\) features have already been selected from the original data set \(O=\{f_{j}|j=1,2,\ldots,D\}\) to form set \(S_{k}\) with the corresponding criterion function \(J(S_{k})\). The search procedure includes two basic steps:  
* Step 1 (Inclusion). Select the best feature \(s_{k+1}\) from the set of available features \(O-S_{k}\) : \[s_{k+1}=\arg\max_{f\in O-S_{k}}J(S_{k}+f)\] (9) and add to \(S_{k}\). Therefore: \[S_{k+1}=S_{k}+s_{k+1}\] (10)
* Step 2 (Exclusion). Find the worst feature \(s\) in the set \(S_{k+1}\) : \[s=\arg\max_{f\in S_{k+1}}J(S_{k+1}-f)\] (11) and if \(s=s_{k+1}\), set \(k=k+1\) and return to Step 1; if not, exclude \(s\) from \(S_{k+1}\) and execute Step 2 again.  
The algorithm is usually initialized by setting \(k=0\) and \(S_{0}=\varnothing\), and run until a set of \(d\) features is obtained. The flow chart of SFFS algorithm is shown in Figure 1.  
Figure 1: The flow chart of SFFS algorithm.
# Cross-Entropy Method in Application to the SIRC Model
## Appendix A Optimization by the Method of Cross-Entropy
### The Vehicle Routing Problem
The cost function is then the sum of all the arcs visited (including the arc from the customer where the failure occurred back to the plant) and the penalty for that customer. In addition, the penalties for the remaining customers who were not visited will also be imposed. Thus, a given route can have a range of cost function values associated with it. The objective is to find the route for which the expected value of the cost function is minimum compared to all other routes. Let us adopt the \"here-and-now\" optimization approach. It means that the operator decides the sequence of vertices to be visited in advance and independent of the demands encountered. Such an approach leads to a discrete stochastic optimization problem with respect to the discrete set of finite routes that can be taken. Let \(G(r):=\mathbf{E}[H(r,D_{r})]\), where \(H(r,D_{r})\) is the deterministic cost function of the route \(r=(r_{1},\ldots,r_{N})\), \(R\) - is the discrete and finite (or countable finite) feasible set of values that r can take, and demands \(D_{r_{i}}\) are a random variable that may or may not depend on the parameters \(r_{i}\). The class of discrete stochastic optimization problems we are considering here are those of the form  
\[\min_{r\in R}\{G(r)\}.\]  
Thus, \(H(r,D_{r})\) is a random variable whose expected value, \(G(r)\) is usually estimated by Monte Carlo simulation. The global optimum solution set can then be denoted by  
\[R^{*}=\{r*\in R:G(r^{*})\leqslant G(r),\forall r_{\in R}\}.\]  
The determination of \(R^{*}\) can be done by CE (v. [PERSON] [5; 16; 51]). In this case, also the basic idea is to connect the underlying optimization problem to a problem of estimating rare-event probabilities (v. [52] in the context of the deterministic optimization). Here the CE method is used in the context of the discrete stochastic optimization and the Monte Carlo techniques are needed to estimate the objective function. In solving the problem by CE methods, some practical issues should be solved, as to when to draw new samples and how many samples to use.