esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mechanical Property  
The effect of nHAp on the mechanical property of the scaffolds is shown in Figure 6. The compressive strength was enhanced gradually with nHAp increasing from 0 to 15 wt %. Subsequently, the strength declined when the nHAp increased further to 20 wt %. Meanwhile, the fracture toughness exhibited a similar change trend and arrived to the peak value at 15 wt % nHAp content. Peak values of the strength and toughness were 34.46 MPa and 1.41 MPa\(\cdot\)m\({}^{1/2}\), which increased by 68% and 29%, respectively, compared with that of the scaffolds without nHAp.  
The mechanical property improvement was attributed to uniform dispersion of HAp nanoparticles and the corresponding stress transfer. HAp nanoparticles had the strong interfacial bonding with the CaSO4 matrix due to its large specific area (50 m\({}^{2}\)/g). Under the loading, the stress could be transferred between HAp nanoparticles and the matrix. The high-stress region would be released and transferred to another region, and thus enhance the loading resistance of scaffolds. On the other hand, excessive nHAp would form an agglomerate structure and reduce the bonding interface area between the matrix and nHAp, which would result in stress concentration and some inner flaws in the matrix, such as voids and cracks, weakening its mechanical strength [31].  
Figure 5: The pH value changes of SBF solution after the scaffolds soaking for different times. Significant difference between the composite scaffolds and pure CaSO4 scaffolds (* \(p\) < 0.05).
## 3 Probabilistic Seismic Demand Model and Definition of Limit States
### Solving the Equation of Motion in Nonlinear Time-History Analysis  
Considering a long-span gantry crane under ground motions, the nonlinear discrete equations can be written in the global coordinate system as:  
\[\mathbf{M}(t)\ddot{\mathbf{u}}+\mathbf{C}(t)\dot{\mathbf{u}}+\mathbf{K}(t) \mathbf{u}=-\mathbf{M}(t)\ddot{\mathbf{u}}_{\mathrm{g}} \tag{6}\]  
in which vector \(\ddot{u}_{g}\) represents the acceleration of ground motions. \(\mathbf{M}(t),\mathbf{C}(t),\mathbf{K}(t)\) are the mass, damping, and stiffness matrices, respectively, all of which are variable with time. \(\ddot{u},\dot{u},u\) denote the vectors of acceleration, speed, and displacement, respectively.  
The equations can be denoted when \(t=i+1\) and \(t=i\) as:  
\[\mathbf{M}(t)\ddot{\mathbf{u}}_{i+1}+\mathbf{C}(t)\dot{\mathbf{u}}_{i+1}+ \mathbf{K}(t)\mathbf{u}_{i+1}=-\mathbf{M}(t)\ddot{\mathbf{u}}_{gi+1} \tag{7}\]  
\[\mathbf{M}(t)\ddot{\mathbf{u}}_{i}+\mathbf{C}(t)\dot{\mathbf{u}}_{i}+\mathbf{ K}(t)\mathbf{u}_{i}=-\mathbf{M}(t)\ddot{\mathbf{u}}_{gi} \tag{8}\]  
When the time interval is small enough, \(\mathbf{M}(t),\mathbf{C}(t),\mathbf{K}(t)\) can be deemed as constant parameters. Therefore, incremental dynamic equations of motion can be obtained by subtracting Equation (7) from Equation (8) as:  
\[\mathbf{M}(t)\Delta\ddot{\mathbf{u}}_{i}+\mathbf{C}(t)\Delta\dot{\mathbf{u}}_{ i}+\mathbf{K}(t)\Delta\mathbf{u}_{i}=-\mathbf{M}(t)\Delta\ddot{\mathbf{u}}_{gi} \tag{9}\]  
Figure 7: Limit states from pushover analysis. LDR, local displacement ratio.  
Equation (9) is an incremental equilibrium equation with constant mass, damping, and stiffness matrices, which can be solved by the immediate integration method. It can be achieved efficiently in OpenSees.
## 7 Conclusions  
In order to jointly optimize the random access procedure and the following data transmission procedure, we propose a hybrid S-ALOHA/TDMA MAC protocol to address the radio access network congestion problem caused by massive M2M devices. We prove that there exists a packet size threshold \(\overline{\delta}\) when considered the signaling overhead in the random access procedure. The S-ALOHA protocol has superiority in small data transmission when \(\delta<\overline{\delta}\) and the TDMA protocol has superiority in large data transmission when \(\delta>\overline{\delta}\). Then a more rigorous system equilibrium analysis is derived and the results demonstrate that the system performance can be improved dramatically by allocating more resource blocks when it is in a saturation state. However, the improvement becomes negligible when the system is in a non-saturation state. Therefore, the equilibrium analysis could provide the operator with more insights on QoS provision. At last, for the load balance of beta distributed M2M traffic, we design a dynamic pre-backoff scheme. The performance results show that the proposed HSTMAC protocol is superior to both the pure S-ALOHA protocol and pure TDMA protocol in terms  
Figure 12: Performance results of HSTMAC protocol with dynamic pre-backoff scheme (Q = 10, W\({}_{\text{BO}}\) = 20): (**a**) Average success probability of M2M traffic; (**b**) Average resource utilization ratio of M2M traffic.  
Figure 13: Performance results of HSTMAC protocol with dynamic pre-backoff scheme (Q = 10, W\({}_{\text{BO}}\) = 20): (**a**) Average request number of M2M traffic; (**b**) Average delay of M2M traffic (granularity in cycles).  
of average success probability and resource utilization ratio, especially when the system load is high. The results also demonstrate that the proposed DPBO algorithm can improve the access success probability and resource utilization ratio at a much lower cost in delay when compared to the uniform pre-backoff algorithm.  
This work is supported by National Natural Science Foundation of China (No. 61771486, No. 61371123, No. 61301165) and Major Research Plan of National Natural Science Foundation of China (No. 91438115).  
[PERSON] conceived the idea and wrote the paper; [PERSON] proposed the dynamic pre-backoff algorithm; [PERSON] conducted the analysis; [PERSON] performed the experiments.  
The authors declare no conflict of interest.
## 2 Existing Expected Threat (xT) Models Theory
### Theoretical Foundations of Expected Threat (xT) Models  
This section outlines the theoretical foundations of the xT model as proposed by [PERSON] [4].  
The xT model involves segmenting the football pitch into L longitudinal and W lateral sections, creating a grid where each action is located within a grid cell \((i,j)\in\llbracket 1,L\rrbracket\times\llbracket 1,W\rrbracket\). Actions can either be a shot from this location or a move to any of the \(L\times W\) possible grid cells \((i^{\prime},j^{\prime})\in\llbracket 1,L\rrbracket\times\llbracket 1,W\rrbracket\).  
The expected outcome of shooting the ball from grid cell \((i,j)\) is equal to the probability of scoring from that situation. This probability is denoted as \(xG_{i,j}\), representing the Expected Goals from that shot. The expected outcome from moving the ball from grid cell \((i,j)\) to grid cell \((i^{\prime},j^{\prime})\) is given by: \(T_{i,j\to i^{\prime},j^{\prime}}\times xT_{i^{\prime},j^{\prime}}\) where \(T_{i,j\to i^{\prime},j^{\prime}}\) is the probability of transitioning from grid cell \((i,j)\) to grid cell \((i^{\prime},j^{\prime})\). \(xT_{i^{\prime},j^{\prime}}\) is the expected outcome associated with that transition.  
Consequently, the overall expected payoff of a football action occurring in \((i,j)\) grid cell, denoted as \(xT_{i,j}\), is as follows:  
\[xT_{i,j}=s_{i,j}\ \times xG_{i,j}+m_{i,j}\times\sum_{i^{\prime}=1}^{L}\sum_{j^{ \prime}=1}^{W}T_{i,j\to i^{\prime},j^{\prime}}\times xT_{i^{\prime},j^{ \prime}} \tag{1}\]  
where \(s_{i,j}\) represents the probability that the player in possession of the ball opts to take a shot on goal from \((i,j)\)\(grid\) cell and \(m_{i,j}\) represents the probability that the player opts to move the ball.  
The terms \(s_{i,j}\), \(m_{i,j}\), and \(T_{i,j\to i^{\prime},j^{\prime}}\) in (1) refer to the actions taken by players in possession of the ball to transition the game to a state where they have an increased likelihood of scoring. These game states align directly with the states of the [PERSON] model [16]: players shift the game from one state to another through passes or carries until reaching an absorbing state, such as a goal or a possession turnover.
## 2 Soil Salinization
### Impacts of Salinity on Soil Microorganisms  
Microbial composition, diversity, numbers, and functions are all adversely affected by salinity [33]. It is common to notice a decline in enzymatic activity or microbial biomass [34]. The susceptibility of soil enzyme functions to salt differs; salinity severely decreases the functions of \(\beta\)-glucosidase, alkaline phosphatase, and urease [4]. The dose-response connections between salinity and bacterial growth, as well as quantitative distributions of the trait salt tolerance among populations, have been established. The relationship between community salt tolerance and soil salinity was found to be substantial, suggesting that salinity has a considerable filtering influence on microbial populations [19].  
Actinobacteria prevailed in saline soil, whereas Proteobacteria predominated in non-saline soil, according to a biodiversity study of soil microbiome utilizing pyrosequencing of the 16S rRNA gene. _Bacteriodetes, Thuanarchaeota, Firmicutes_, and _Acidobacteria_ were found to be the most common phyla in both saline and non-saline soils, whereas Cyanobacteria, _Verrucomicobia_, and _Gemmatimonadetes_ were the least common [12]. In fungi, a low osmolality reduces spore germination and hyphae development, and it affects the morphology [35] and expression of genes [36].  
Another negative impact of salt is the disruption of the symbiotic relationship among plants and useful microbes. A disruption in plant-bacteria connections, for example, is induced by changes in proteins implicated in the first adhesion phases (adsorption and anchoring) of bacteria to plant roots in symbiotic relationships, as well as the suppression of nitrogen fixation and nodulation functions [37].  
Consequently, the impact is usually more prominent in the rhizosphere due to increased water absorption by plants as a result of transpiration. The basic reason is that living at high salt amounts has a substantial bio-energetic taxation because microbes must preserve osmotic balance between the cytoplasm and the surrounding environment, excluding sodium ions from within the cell. As a consequence, adequate energy for osmoadaptation is necessary [38,39].
## 3 Performance Evaluation
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **RAM** \\ **Utilization** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Storage** \\ **Utilization** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CPU Utilization by** \\ **Utilization** \\ **Utilization** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CPU Utilization** \\ **Utilization** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **CPU Utilization** \\ **Mamdani FIS** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **CPU Utilization by** \\ **Sugeno FIS** \\ \end{tabular} } \\ \cline{1-1}  & & & & **Crisp** & **Linguistic** & **Crisp** & **Linguistic** \\ \hline
75 & 90 & 40 & 42.3 & Normal & 39.5 & Normal \\
68 & 82 & 32 & 31.7 & Normal & 32.3 & Normal \\
56 & 64 & 34 & 49.4 & Normal & 34.8 & Normal \\
72 & 94 & 44 & 35.1 & Normal & 43.4 & Normal \\
38 & 50 & 20 & 49 & Normal & 20.4 & Low \\
56 & 72 & 32 & 44.7 & Normal & 33 & Normal \\  
ow & 37.6 & Normal \\
39 & 63 & 26 & 49.7 & Normal & 24.4 & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Utilization information and FIS model results. between the hardware components are linear and each component is compatible with each other. The compatibility of the system means that there is no necessity for computer designing, and configuration enhancement [6]; that is, the system is managing with the given workload. The models' evaluation results, considering the influence of each factor and its index in the evaluation of the CPU utilization, are not the same. Figure 12 shows the CPU utilization graph for the output of the Mamdani FIS and Sugeno FIS. Figure 12a shows a comparison of the Mamdani FIS with CPU utilization, which is monitored through performance monitoring. The comparison shows that there are some unsuited cases with evaluation, but the results are close to each other. At the bottom of the figure, we show the difference between the real data and the Mamdani FIS result. The Sugeno FIS model shows excellent results, i.e., the model evaluation values are almost the same as the monitoring data (Figure 12b). The training of the inference engine was performed, rather than the rule base inference engine. Figure 11: CPU utilization graph: (**a**) separate dependency of RAM and storage on CPU; (**b**) general dependency of RAM and storage on CPU.
# Feasibility Analysis of a LoRa-Based WSN Using Public Transport
###### Abstract  
LoRa (Long Range) is a proprietary radio communication technology exploiting license-free frequency bands, allowing low-rate information exchange over long distances with very low power consumption. Conventional environmental monitoring sensors have the disadvantage of being in fixed positions and distributed over wide areas, thus providing measurements with a spatially insufficient level of detail. Since public transport vehicles travel continuously within cities, they are ideal to house portable monitoring systems for environmental pollution and meteorological parameters. The paper presents a feasibility analysis of a Wireless Sensor Network (WSN) to collect this information from the vehicles conveying it to a central node for processing. The communication system is realized by deploying a layer-structured, fault-resistant, multi-hop Low Power Wide Area Network (LPWAN) based on the LoRa technology. Both a theoretical study about electromagnetic propagation and network architecture are addressed with consideration of potential practical network realization.  
LoRa; LoRa modulation; Internet-of-Things; LPWAN; wireless communication; multi-hop networks; electromagnetic propagation +
Footnote †: journal:  
_Comp._  
_2018\(1\), _49_; doi:10.3390/asi1040049
###### Abstract  
Nowadays, ion-exchange membranes have numerous applications in water desalination, electrolysis, chemistry, food, health, energy, environment and other fields. All of these applications require high selectivity of ion transfer, i.e., high membrane permslectivity. The transport properties of ion-exchange membranes are determined by their structure, composition and preparation method. For various applications, the selectivity of transfer processes can be characterized by different parameters, for example, by the transport number of counterions (permselectivity in electrodialysis) or by the ratio of ionic conductivity to the permeability of some gases (crossover in fuel cells). However, in most cases there is a correlation: the higher the flux density of the target component through the membrane, the lower the selectivity of the process. This correlation has two aspects: first, it follows from the membrane material properties, often expressed as the trade-off between membrane permeability and permselectivity; and, second, it is due to the concentration polarization phenomenon, which increases with an increase in the applied driving force. In this review, both aspects are considered. Recent research and progress in the membrane selectivity improvement, mainly including a number of approaches as crosslinking, nanoparticle doping, surface modification, and the use of special synthetic methods (e.g., synthesis of grafted membranes or membranes with a fairly rigid three-dimensional matrix) are summarized. These approaches are promising for the ion-exchange membranes synthesis for electrodialysis, alternative energy, and the valuable component extraction from natural or waste-water. Perspectives on future development in this research field are also discussed.  
Keywords:ion-exchange membranes; functional polymers; selectivity; ionic conductivity; hybrid membranes; grafted membranes +
Footnote †: journal: Molecular Sciences
###### Abstract  
Malignant lesions are a huge threat to human health and have a high mortality rate. Locating the contour of organs is a preparation step, and it helps doctors diagnose correctly. Therefore, there is an urgent clinical need for a segmentation model specifically designed for medical imaging. However, most current medical image segmentation models directly migrate from natural image segmentation models, thus ignoring some characteristic features for medical images, such as false positive phenomena and the blurred boundary problem in 3D volume data. The research on organ segmentation models for medical images is still challenging and demanding. As a consequence, we redesign a 3D convolutional neural network (CNN) based on 3D U-Net and adopted the render method from computer graphics for 3D medical images segmentation, named Render 3D U-Net. This network adapts a subdivision-based point-sampling method to replace the original upsampling method for rendering high-quality boundaries. Besides, Render 3D U-Net integrates the point-sampling method into 3D ANU-Net architecture under deep supervision. Meanwhile, to reduce false positive phenomena in clinical diagnosis and to achieve more accurate segmentation, Render 3D U-Net specially designs a module for screening false positive. Finally, three public challenge datasets (MICCAI 2017 LiTS, MICCAI 2019 KiTS, and ISBI 2019 segTHOR) were selected as experiment datasets and to evaluate the performance on target organs. Compared with other models, Render 3D U-Net improved the performance on both overall organ and boundary in the CT image segmentation tasks, including in the liver, kidney, and heart.  
Keywords:render; 3D U-Net; medical image; segmentation; artificial intelligence; deep learning; attention mechanism; deep supervision; false positive classification +
Footnote †: journal: Medical Imaging
# JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)
## 4 Results
### Quantitative: Final Grades
#### 4.1.2 Correlation Between Human and AI Assessment  
The results shown in Figure 4 reveal strong positive correlations for several models. DeepSeek, Qwen, and OpenAI's o3_mini stand out, each with a Pearson coefficient exceeding 0.9, indicating an almost perfect linear relationship with the instructor's assessments. Additionally, models such as GPT-4o, GPT-4.1, and Gemini also achieved high correlations, above 0.8, suggesting that their assessments are consistently aligned with those of the human evaluator.
# Differential Transform Method as an Effective Tool for Investigating Fractional Dynamical Systems
## 3 Results  
### Integration of the Rossler System: DTM vs. Other Methods  
To assess the quality of solutions obtained with the use of the DTM, a number of simulations of the chaotic Rossler system (\(a=0.4079\)) were performed, and their results were compared with results obtained with other methods. The Runge-Kutta 4 (RK4) scheme and the ode45 (ODE) Matlab solver with the sixth-stage fifth-order Runge-Kutta algorithm were implemented. The ode45 function is a versatile high quality function and it is recommended as the first solver to be tried in most problems. However, it has recently been reported [30] that the simple RK4 algorithm yields, in some cases, much better results than the latest Matlab ode functions. In the case of the ode45 function, an optional parameter was used to limit the maximum integration step (\(h_{max}\)). When assessing the quality of results obtained with various methods, we use the convergence criterion, the measure of which is the concurrency time of curves corresponding to two compared solutions. The best convergence of DTM and ODE results was found for the DTM order 10 with \(h_{DTM}=0.01\) s and for \(h_{max}=0.002\) (Figure 1a).  
Figure 1: (**a**) Borderline of the convergence between ODE and DTM solutions with optimal parameters: DTM order equals 10, \(h_{DTM}\) = 0.01 s and \(h_{max}\) = 0.002 s. (**b**) Borderline of convergence between ODE and RK4 results. The time step adopted in the RK4 method was \(h_{RK4}\) = 0.0002.  
The duration of convergence between the ODE and RK4 results was 448 s (Figure 1b). Looking for the longest convergence times, a series of tests was carried out for different step lengths adopted for each method. The obtained results are presented in Figure 2.  
Figure 2a shows the duration of convergence between the DTM and ODE results when several DTM results obtained for different time steps are compared with the optimal ODE result. Results of the inverse comparison-ODE vs. DTM-are shown in Figure 2b. A comparison of the RK4 results with the optimal ODE result (Figure 2c) showed that the best time step was \(h_{RK4}=0.0002\) s.
# Vision Transformer-Based Tailing Detection in Videos
## 4 Experiments and Analysis
### Effectiveness of the Proposed Sampling Method  
In Table 4, we compare the performance of the proposed ORS method with the sliding window-based sampling method. We trained the proposed TSViT tailing detection algorithm with ORS method and then trained the TSViT with the sliding window-based sampling method and reported the performance comparison in Table 4. Table 4 shows the average performance of all the six implementations of TSViT (given in Table 2). It is clear from Table 4 that the ORS method not only increase the amount of training data by augmentation, but improves the performance over sliding window-based sampling method. However, there is a small performance difference when observing the accuracy and precision, but ORS shows a high performance gain in terms of recall.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Layers** & **Hidden Size D** & **MLP Size** & **Heads** & **Params(M)** \\ \hline TSViT-S/256 & 6 & 1024 & 2048 & 16 & 448 \\ TSViT-B/256 & 12 & 768 & 3072 & 12 & 516 \\ TSViT-L/256 & 24 & 1024 & 4096 & 16 & 1408 \\ TSViT-B/512 & 12 & 768 & 3072 & 12 & 1092 \\ TSViT-S/512 & 6 & 1024 & 2048 & 16 & 1216 \\ TSViT-L/512 & 24 & 1024 & 4096 & 16 & 2176 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the various Vision Transformer architectures used in our experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Tailing**} \\ \cline{3-5}
**Model** & **Params (M)** & **Accuracy (\%)** & **Precision** & **Recall** \\ \hline TSViT-S/256 & 448 & 74.65 & 0.7631 & 0.8607 \\ TSViT-B/256 & 516 & 71.35 & 0.7185 & 0.8886 \\ TSViT-L/256 & 1408 & 73.09 & 0.7629 & 0.8245 \\ TSViT-B/512 & 1092 & **76.56** & 0.7333 & **0.9805** \\ TSViT-S/512 & 1216 & 74.65 & 0.7631 & 0.8607 \\ TSViT-L/512 & 2176 & 73.09 & 0.7629 & 0.8245 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the proposed end-to-end TSViT-based tailing detection frameworks.
# Numerical Analysis on Spanwise Correlation of Vortex-Induced Force of Split Double-Box Beam
## 2 Basic Theory and Methods
### Numerical Simulation of Turbulent Flow  
#### 2.2.1 Reynolds Averaging N-S Equation (RANS Equation)  
Reynolds averaging method is to average the N-S equation and turn to solve the time-averaged N-S Equation (5), which can obtain better simulation results while greatly re ducing the computational effort, and is currently a more widely used numerical simulation method [19; 20].  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}\Bigg{(}\mu\frac{\partial u_{i}}{\partial x_{j}}-\rho\overline {u_{i}^{\prime}u_{i}^{\prime}}\Bigg{)}+S_{i} \tag{5}\]  
The continuity equation based on RANS and equation of motion are, respectively:  
\[\partial u_{i}/\partial x_{i}=0 \tag{6}\]  
\[\frac{\partial(\rho\overline{u_{i}})}{\partial t}+\frac{\partial}{\partial x _{j}}\big{(}\rho\overline{u_{i}u_{j}}\big{)}=-\frac{\partial p}{\partial x_{ i}}+\frac{\partial}{\partial x_{j}}\Bigg{(}\mu\frac{\partial\overline{u_{i}}}{ \partial x_{j}}-\rho\overline{u_{i}^{\prime}u_{i}^{\prime}}\Bigg{)} \tag{7}\]  
where: \(i,j=1,2\), \(\rho=1.225\,\mathrm{kg}/\mathrm{m}^{3}\) is density of air, \(\mu=1.7894^{-5}\mathrm{kg}/\mathrm{m}\cdot\mathrm{s}\) is dynamic viscosity coefficient.