esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Shearing and Bending Behavior of Kiwifruit Branches Using the Discrete Element Method
## 4 Conclusions  
In this study, a DEM model of the kiwifruit branch was developed. The model could simulate the bending and shearing processes of kiwifruit branches with high reliability. With this model, failure characteristics such as the breaking force and fracture development were observed. The results could provide reference for the investigation of the bending and shearing process of flexible materials and the failure mechanism of agricultural materials.  
Figure 8: Verification of branch shearing behaviors of simulation and experiment: (**a**) first stage of branch shearing simulation, (**b**) second stage of branch shearing simulation, (**c**) last stage of branch shearing simulation, and (**d**) shearing experiment results.  
Figure 9: Verification of branch bending behaviors of simulation and experiment: (**a**) stress–strain state of branch bending simulation, (**b**) fracture first appeared, (**c**) fracture extended to the top, and (**d**) bending experiment results.  
1. The shear modulus and elastic modulus of the kiwifruit branch samples were determined to be 31.38 MPa and 1.21 GPa, respectively, through shearing and three-point bending tests.
2. Four DEM parameters were identified as having a significant impact on the mechanical response of the kiwifruit branch model: effective modulus of bond, cohesion of bond, effective modulus between ball and wall, and normal-to-shear stiffness ratio.
3. Through response surface methodology, the functions relating DEM parameters to the mechanical response of the kiwifruit branch model were established. The optimum parameter combination of the effective modulus of bond, cohesion of bond, effective modulus between ball and wall, and normal-to-shear stiffness ratio was determined to be 2.2 \(\times\)\(10^{9}\) Pa, 2.56 \(\times\)\(10^{8}\) Pa, 1.27 \(\times\)\(10^{8}\) Pa, and 1.6.
4. The verification tests conducted with the optimal parameter combination revealed a relative error of 4.5% between the simulation and experimental results. The displacement-force curves demonstrated a high degree of congruence, affirming the reliability of the developed model.  
Conceptualization: [PERSON] and [PERSON]; [PERSON] reviewed the literature and wrote the initial draft of the paper with assistance from [PERSON]. ([PERSON]). [PERSON]. ([PERSON]) and [PERSON] contributed to revising the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 32101630), the Key Research and Development Program of Shaanxi Province, China (No. 2023-YBNY-226), and a grant from the China Postdoctoral Science Foundation (No. 2020M683578).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 2 Research Methodology  
To ensure a comprehensive review of the literature, a systematic approach was adopted for data collection. The Scopus and IEEE Xplore databases were utilized as the primary sources for identifying relevant studies. This review focused on publications from 2015 to 2025. The search strategy employed a combination of keywords, including \"5G\", \"EMF exposure\", \"electromagnetic field radiation\", \"specific absorption rate\", \"power density\", \"electric field strength\", \"radio resource management\", \"resource allocation\", \"optimization\", \"reconfigurable intelligent surface\", and \"RIS\".  
Figure 1: Organization of the paper.  
Boolean operators such as \"AND\" and \"OR\" were used to refine the search and retrieve relevant articles. The inclusion criteria encompassed peer-reviewed articles with a significant focus on EMF exposure and EMF-aware resource management, as well as contributions that offered empirical data or theoretical insights into the field. Studies that were unrelated to 5G networks, lacked focus on EMF considerations, or were non-peer-reviewed (e.g., editorials or commentaries) were excluded.
## 3 Results and Discussion
### Thermal Conductivity
However, the increasing or decreasing nanoadditive size (observed after synthesis, before being dispersed) may not imply an obvious trend in the thermal conductivity of the nanofluid. SSA should be enlarged by increasing the particle size while maintaining the rest of the parameters involved. However, the effect of changes in shape, porosity, or roughness may have a greater influence on SSA than size increase. Thus, nanoadditive SSA could become a preferred parameter before size for nanoadditive selection, but not the only one. The type of material and the stability of the dispersion, among other variables, will also have a great influence on the improvement in thermal conductivity. Likewise, the observed enhancements could be beneficial for various heat-transfer applications, such as improving the efficiency of heat exchangers, solar energy collectors, and cooling systems. These improvements are in line with the broader goal of optimising energy systems by exploiting the unique properties of nanofluids.
# A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
## 2 Current Status of Research on Grouting Materials  
In recent years, for the fissure rock body, we have generally opted to enhance water sealing and reinforce grouting materials for better effectiveness based on engineering applications and examples. Currently, common grouting materials can be classified into four types, namely, inorganic materials, organic materials, composite materials, and biological materials, as illustrated in Figure 1. The organic-inorganic composite slurry possesses the combined advantages of both inorganic and organic materials, exhibiting excellent injection performance, rapid setting time, and being a more cost-effective alternative to pure organic grouting materials. However, the limited applicability of biological grouting materials stems from their environmental characteristics. Since the applicability of organic and inorganic composite grout to deep underground engineering is not clear at present, we will introduce organic grouting materials and inorganic grouting materials here.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 3 Experimental Verification
### Column Concentration Parameter Fitting Experiment  
The experimental setup for gas column concentration calibration is shown in Figure 6.  
Based on the dual-channel background radiation mapping experiment, to the column concentration calibration system, we added a gas pool with a length of 30 cm to constrain the gas to be measured. During the experiment, the blackbody temperature was kept constant, and different concentrations of gas were injected into the gas pool. The detector passed through the target gas radiation and the gas pool to image the blackbody, and the absorbance of the gas under different column concentrations was obtained through the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Maximum** & **Minimum** & **Mean** \\ \hline \hline \multicolumn{1}{c}{Grayscale image} & 678.4651 & 531.5849 & 604.3265 \\ \multicolumn{1}{c}{Image of temperature distribution (\({}^{\circ}\)C)} & 55.7802 & 54.3816 & 55.1380 \\ \multicolumn{1}{c}{Image grayscale difference} & 4.9083 & 22.8577 & 11.4555 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precision of background radiation conversion.  
Figure 5: Background radiation conversion experiment verification results. (**a**) Background gray image captured through the reference spectrum channel; (**b**) mapped temperature distribution image; (**c**) mapped background gray image; (**d**) gray value difference image.  
grayscale image. After fitting the calibration curve of gas absorbance-column concentration, the calibration parameters were obtained.  
Under standard atmospheric pressure, SF\({}_{6}\) of different concentrations was introduced, the absorbance of ten different column concentrations of gas, ranging from 3000 to 30,000 ppm\(\cdot\)m, was measured, and the fitting calibration parameters \(a=-7.04\times 10^{-10}\), \(b=3.91\times 10^{-5}\) and \(c=0.0254\) were obtained. The calibration curve of SF\({}_{6}\) gas absorbance-column concentration is shown in Figure 7. The fitting determination coefficient R2 of the absorbance-column concentration calibration curve is 0.894, the average relative error (ARE) is 3.8331% and the Root-Mean-Square Error of Cross-Validation (RMS-SECV) is 3.8331.  
Due to the length limitation of the gas pool, the upper limit of the gas column concentration in the calibration curve is 30,000 ppm\(\cdot\)m, this column concentration can already meet most usage needs. In order to invert SF\({}_{6}\) with a column concentration above 30,000 ppm\(\cdot\)m, calibration can be performed on SF\({}_{6}\) gases with higher column concentrations. Although the absorbance of the SF\({}_{6}\) is close to saturation at 30,000 ppm\(\cdot\)m, this method is still effective within a certain range beyond 30,000 ppm\(\cdot\)m, but the accuracy may be reduced.  
Figure 6: Column concentration calibration experimental device.  
Figure 7: Absorbance–column concentration calibration curve.
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
Studies were eligible for inclusion if they met the following criteria:* Centered in Poland, with a focus on telemedicine, eHealth, and digital health.
* The peer-reviewed articles focused on the implementation, evaluation, and development of the field.
* Reported on empirical data, pilot project results, implementation outcomes, and health policy evaluations.
* Provided insights into clinical outcomes, patient satisfaction, technological development, or institutional adoption.
* Clinical studies, pilot programs, policy evaluations, and health technology assessments. The exclusion criteria were as follows:
* The studies were conducted entirely outside of Poland.
* Technical or engineering reports with no clinical or policy relevance.
* Editorials, conference-only publications, opinion pieces, or articles lacking empirical or methodological content.
* Studies unrelated to the Polish healthcare system.
* Purely technical articles lacking clinical or health system relevance.
# Dynamic Performance of a New-Type PSC I-girder for Railway Bridge Application
## 2 Dynamic Performance Evaluation of New Type of PSC I-girder Bridge
### Performance Evaluation by Moving Load Analysis
#### 2.2.1 Performance Evaluation Criteria
The critical speed (\(V_{cr}\)) of the train causing resonance can be calculated by Equation (1) [11], where \(V_{scr}\) is the sub-critical speed; \(w_{1}\) is the natural frequency of the first flexural mode of the bridge; and, \(S_{eff}\) is the effective beating interval of the train load as given in Table 4. Moreover, the sub-critical speed is obtained as an integer ratio of the critical speed as expressed in Equation (2). Besides, resonance suppression is conceptually opposite to resonance that deals with the suppression of the free vibration effect of the bridge caused by the traveling train. In other words, resonance suppression is a phenomenon that occurs when the dynamic response of the bridge is determined only by the load currently positioned in the bridge [14]. Equation (3) gives the span length \(L(\text{m})\) of the bridge for which resonance suppression occurs. \[V_{cr}=w_{1}\times S_{eff}, \tag{1}\]  
\[V_{scr}=\frac{w_{1}}{n}\times S_{eff},\ n=1,2,3,\cdots \tag{2}\]  
\[L(\text{m})=S_{eff}\times(i+0.5),\ i=1,2,3,\cdots \tag{3}\]  
The fact that the resonance suppression span length is determined with respect to the effective beating interval of the train indicates the existence of a span length for which the response at resonance is suppressed according to the train type. Figure 6 plots the resonance suppression span length provided by Equation (3) for each of the five considered trains. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{3}{c}{**Train in Operation**} & \multicolumn{3}{c}{**Train under Plan**} \\ \cline{2-6}  & **KTX** & **Saemaeul** & **Freight** & **HEMU** & **EMU** \\ \hline Composition & 20 coaches & 8 coaches & 20 coaches & 12 coaches & 8 coaches \\ Axle load (kN) & 170–190 & 107.91–215.82 & 196.2 & 130 & 156.8 \\ Effective beating interval (m) & 18.7 & 23.5 & 13.95 & 24.3 & 21.4 \\ Max. speed for analysis (km/h) & 420 & 300 & 220 & 430 & 330 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Characteristics of trains in operation or under plan in Korea. Figure 5: KTX Train. Accounting that the PSC I-girder is mostly applied to span lengths between 30 m and 40 m (Figure 2), the span length of 35 m appears to be the most favorable in term of resonance suppression. Consequently, this span length of 35 m is expected to develop small response under the traveling trains. Due to the discrepancy with the resonance suppression span length for KTX, evaluating the behavior of the bridge by moving load analysis will bear utmost importance. Besides, the span length of 30 m is the closest to the resonance suppression span length for KTX. In such a case, it will be necessary to conduct precise moving load analysis for HEMU and EMU. In addition, the satisfaction of the design criteria including the acceleration, displacement and twist for each of the train loads shall be checked by dynamic analysis.
## 4 MARL-Based Routing Solution
### Action Space  
Actions in this model are performed hop by hop, meaning that the complete path is constructed as a sequence of actions \(\{a_{0},a_{1},...,a_{n}\}\). Each action corresponds to the identifier of the next TSN switch to continue forming the path. If the network is too congested to allocate resources for a specific data flow, the agent must be capable of deciding to stop routing frames. As such, the action space includes the identifier of all of the TSN switches (representing the TFCs and TMSs in the infrastructure) with an additional action to reject the service request.  
To enhance the learning process, the action space is filtered before selecting the actions to perform. This filtering mechanism applies three criteria:  
1. Connectivity filter: Discarding actions whose related node is not connected to the one up to which the path has been created.
2. Resource availability filter: Discarding actions that involve scheduling data frames on a link with insufficient resources. Although the link might have enough free bytes along the hyperperiod, this criterion depends on the availability of all possible positions; if there is at least one position with enough free bytes, then the link can be used.
3. Loop prevention filter: Discarding actions that involve returning to the previous node, as this is not feasible in TSN networks.  
These filtering mechanisms ensure that the agent cannot attempt impossible actions, leading to faster training processes and more reliable final results.
## 4 Performance Evaluation
### Determination of the Optimal \(r\) for Practical Applications  
So far, we have investigated two relationships: one is between the transmission range and energy consumption, and the other is between the transmission range and the network connectivity. Given the network size, the node density and the threshold of the network connectivity, we may determine the optimal transmission range for sensor nodes to operate. In other words, we may adjust the transmission power of sensor nodes such that the energy consumption in transmissions is optimally set.  
It is easy to see that, for a given transmission range, the energy efficiency and the network connectivity can be calculated using Equations (9) and (18), respectively. It is important to point out that the network connectivity \(p_{c}\) is a monotonically increasing function with regard to the transmission range while the energy efficiency \(\epsilon(r)\) is not. Motivated by this, here we propose a simple strategy to find the optimal transmission range.  
First, for a particular threshold of the network connectivity, we can find the lowest transmission range, say \(r_{1}\), by using Equation (18) such that it satisfies the threshold of the network connectivity. Next, we determine the optimal transmission range, say \(r_{2}\), based on Equation (9). Finally, to ensure both the network connectivity requirement and energy efficiency, we simply take the largest value between \(r_{1}\) and \(r_{2}\) as the transmission range for operation.
## 3 Control System Design for the Flexure-Based Manipulator
### DCARC Design in LODCARC
\begin{array}{cc}k_{s1}-k_{s}-k_{1}\theta_{1}+\theta_{2}&-\frac{k_{ 1}\theta_{2}-\theta_{3}}{2}\\ -\frac{k_{1}\theta_{2}-\theta_{3}}{2}&\frac{m_{s}k_{3}^{3}}{2}\end{array} \right]. \tag{13}\]Furthermore, \(u_{s2}\) as a robust feedback term is designed to attenuate the effect of model uncertainties of parametric uncertainty and uncertain nonlinearity. As such, \(u_{s2}\) should dissipate naturally so that it does not affect the adaptive function of the adaptive control part \(u_{\ell}\). Thus, noting Assumption 1 and part 1 of Equation (7), there is a \(u_{s2}\) to satisfy the following constraints:  
\[ \begin{array}{ll}\text{i}&p\{u_{s2}-\varphi_{a}{}^{\mathrm{T}}\widetilde{ \theta}+d\}\leq\varepsilon\\ \text{ii}&pu_{s2}\leq 0\end{array}  \tag{14}\]  
where \(\varepsilon\) is a design parameter, which can be arbitrarily small. Specific form of \(u_{s2}\) can be obtained using the techniques in [20]:  
\[u_{s2}=-\frac{1}{4\varepsilon}h^{2}p \tag{15}\]  
where \(h\) is any function satisfying  
\[h\geq\|\theta_{M}\|\|\varphi_{d}\|+\delta_{d} \tag{16}\]  
where \(\theta_{\mathrm{M}}=\theta_{\mathrm{max}}-\theta_{\mathrm{min}}\). **Theorem 1:**_If the adaptation function in Equation (5) is chosen as_  
\[\tau=\varphi_{a}p \tag{17}\]  
_then the DCARC control term applied in Equation (5) guarantees the following results._  
1. In general, all signals are bounded, and the positive definite function \(V_{s}\) defined by \[V_{s}=\frac{1}{2}\theta_{1}p^{2}+\frac{1}{2}\theta_{1}{k_{1}}^{2}{e_{a}}^{2}\] (18) is bounded above by \[V_{s}\leq\exp(-\lambda t)V_{s}(0)+\frac{\varepsilon}{\lambda}[1-exp(-\lambda t)]\] (19) where \(\lambda=\min\{2k_{s}/\theta_{1\mathrm{max}},k_{1}\}\). 2. If after a finite time \(t_{0}\), there exists parametric uncertainties only (i.e., \(d=0\) and \(\forall t\geq t_{0}\)), then zero final tracking error is also achieved, i.e., \(e_{a}\to 0\) and \(p\to 0\) as \(t\rightarrow\infty\). **Proof. ** Differentiating \(V_{s}\) given by Equation (18) with respect to time and applying Equation (11) yields  
\[\dot{V}_{s}=p\{u_{s}-\varphi_{a}{}^{\mathrm{T}}\widetilde{\theta}+d+(k_{1} \theta_{1}-\theta_{2})\dot{e}_{a}-\theta_{3}e_{a}\}+\theta_{1}{k_{1}}^{2}e_{a }\dot{e}_{a} \tag{20}\]  
Noting \(p=\dot{e}_{a}+k_{1}e_{a}\), we have  
\[ \begin{array}{ll}\dot{V}_{s}&=p\{u_{s2}-\varphi_{a}{}^{\mathrm{T}} \widetilde{\theta}+d\}+(k_{1}\theta_{1}-\theta_{2}-k_{s1})p^{2}+(k_{1}\theta_{ 2}-\theta_{3})e_{a}p-\theta_{1}{k_{1}}^{3}{e_{a}}^{2}\\ &=p\{u_{s2}-\varphi_{a}{}^{\mathrm{T}}\widetilde{\theta}+d\}-[p,e_{a}]A[p,e_{ a}]{}^{\mathrm{T}}-k_{s}p^{2}-\frac{1}{2}\theta_{1}{k_{1}}^{3}{e_{a}}^{2}\end{array}  \tag{21}\]  
As \(A\) is positive definite, then we have  
\[\dot{V}_{s}\leq p\{u_{s2}-\varphi_{a}{}^{\mathrm{T}}\widetilde{\theta}+d\}- k_{s}p^{2}-\frac{1}{2}\theta_{1}{k_{1}}^{3}{e_{a}}^{2} \tag{22}\]  
With condition i of Equation (14) and choosing \(\lambda\) as \(\lambda=\min\{2k_{s}/\theta_{1\mathrm{max}},k_{1}\}\), the derivative of \(V_{s}\) becomes  
\[\dot{V}_{s}\leq-\lambda V_{s}+\varepsilon \tag{23}\]  
Thus, part 1 of Theorem 1 is proved.
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 2 Background and System Model  
### Polar Codes  
Polar codes, a class of linear block codes, are purposefully designed for memoryless channels. To generate polar codes, let \(u_{1}^{N}\) be the encoding input vector \((u_{1},u_{2},\ldots,u_{N})\) comprising both the information set and frozen set. The information bits are placed according to the index set \(\mathcal{A}\), while the frozen bits (set to zero) are allocated based on the index set \(\mathcal{A}^{c}\). In addition, the index set \(\mathcal{A}\) comprises \(K\) bits, while the other set \(\mathcal{A}^{c}\) consists of \(N-K\) bits. An input vector is first formed by identifying the reliable indices of set \(\mathcal{A}\). For the sake of simplicity, a polar code construction method known as the Gaussian Approximation (GA) algorithm [18] is employed to determine the reliable set \(\mathcal{A}\). Additionally, the design signal-to-noise ratio (SNR) [19], corresponding to a given value of \(RE_{b}/N_{0}\), serves as a crucial parameter for initializing polar code construction. The codewords \(c_{1}^{N}\) can be obtained by  
\[c_{1}^{N}=u_{1}^{N}\mathbf{G}_{N}, \tag{1}\]  
where \(\mathbf{G}_{N}\) is the generator matrix and the code length is denoted by \(N=2^{n}\). To obtain the generator matrix, \(\mathbf{G}_{N}=\mathbf{B}_{N}\mathbf{F}^{\otimes n}\). \(\mathbf{B}_{N}\) denotes the bit-reversal permutation matrix. Additionally, a bit-reversal permutation matrix rearranges the elements of a vector in a specific way based on the bit-reversal operation. The Kronecker power of \(\mathbf{F}^{\otimes n}\) is recursively defined as \(\mathbf{F}\otimes\mathbf{F}^{\otimes n-1}\) and the kernel matrix is \(\mathbf{F}=\begin{bmatrix}1&0\\ 1&1\end{bmatrix}\). Fortunately, the encoding process of polar codes can be implemented with the complexity \(O(N\log N)\). For instance, a polar encoder for \(N=8\) bits and the frozen set \(\mathcal{A}^{c}=\{1,2,3,5\}\) are illustrated in Figure 1. The encoding input is first rearranged using the bit-reversal operation before performing calculations.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Calculation Formulas of Main Circuit Parameters
#### 3.2.6 Calculation of the Voltage and Current at the Valve Side  
The AC voltage at the valve side is:  
\[U_{\text{v}}=\frac{U_{\text{dio}}}{\sqrt{2}}\frac{\pi}{3} \tag{39}\]  
The AC current at the valve side is:  
\[I_{\text{v}}=\sqrt{\frac{2}{3}}I_{\text{d}} \tag{40}\]
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.3.2 Controller Placement Subproblem  
For the distributed controller placement problem, we establish four models: delay model, security model, load model, and link cost model. At the same time, according to the requirements of network operators, we combine four objectives into a single-objective problem as a new model for solving the controller placement problem. Since the evaluation criteria for reaching the optimal value of each objective are inconsistent, we normalized them, and the multi-objective based controller placement was calculated by the following formula:  
\[minimize\ \alpha\cdot\frac{D-D_{min}}{D_{max}-D_{min}}+\beta\cdot\frac{S_{ max}-S}{S_{max}-S_{min}}+\gamma\cdot\frac{L-L_{min}}{L_{max}-L_{min}}+\delta \cdot\frac{C-C_{min}}{C_{max}-C_{min}}, \tag{22}\]  
\(s.t.\) (14--20).  
In this formula, to address controller placement for different network scenarios, \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\) represent the weights of the delay model, security model, load model, and link cost model, respectively, which are set by the ISP to match multiple service requirements.