esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## 5 Conclusions  
In this study, the compressive strength and the elastic modulus of RPC were tested. The shrinkage after steam-heat curing and the creep of RPC were investigated. The effect of steel fibre on shrinkage and creep, and the influence of the axial stress ratio on creep were discussed. Then, a comparison was made between the experimental results and predictions obtained from existing shrinkage and creep models. Finally, a regression analysis of the experimental shrinkage and creep results for RPC was carried out. The conclusions of this study are as follows:  
1. The compressive strength of RPC is obviously higher than normal strength concrete due to its dense microstructure. The compressive strength and the modulus elastic increase with increasing steel fibre content, as the steel fibre restricts the development of microcracks and transverse deformation.
2. The shrinkage of RPC is much smaller than normal strength concrete with the improved uniformity and the narrowed pore size. The shrinkage decreases with increasing steel fibre content due to the micro-scale water films formed on the steel fibre surface and skeleton formed by the cross and overlap of steel fibre.
3. The creep of RPC decreases with increasing steel fibre content which is obvious during the later period after loading, as steel fibre can prevent the development of microcracks (emerging mainly during the later stage). Besides, the slip between the steel fibre and RPC matrix tends to be stable in the later period, the ability of steel fibre to inhibit creep gradually appears.
4. For axial stress ratios of less than 0.4, the creep strain of RPC varies linearly with the axial stress (RPC is in the linear creep stage).
5. The shrinkage strains of RPC predicted by the ACI 209-82 model, B3 model, and GL 2000 model are significantly greater than the corresponding experimental results. However, the shrinkage strains of RPC predicted by the CEB-FIP 90 model are significantly smaller than the experimental results. Moreover, all four models overestimate the creep strain of RPC. Thus, these models cannot be used for predicting the shrinkage and creep of RPC.
6. Simple shrinkage and creep models for RPC which consider the influence of steel fibre have been developed.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments. All the authors analysed the data and contributed to writing the paper.  
**Acknowledgments:** The authors gratefully acknowledge the financial support of the National Science Foundation of China (Project No. 51678190, No. 51478142).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Comparison of the proposed shrinkage model predictions with experimental shrinkage strain results for the RPC samples.
## 2 Materials and Methods
### Eligibility Criteria  
Studies included in this review met the following predefined eligibility criteria to ensure methodological rigor and relevance:  
1. Only original research articles published in peer-reviewed English-language journals were considered. Conference proceedings, review articles, case studies, and book chapters were excluded, as well as master's or doctoral theses. Studies without full-text availability were also not included.
2. Studies had to examine the effects of badminton shoe design on biomechanical performance and injury risk during sport-specific movements. Research had to focus on at least one of the following footwear parameters: forefoot bending stiffness, torsional stiffness, lateral-wedge hardness, midsole hardness, insole hardness, and shoe sole or heel design. Studies that investigated general footwear characteristics without addressing badminton-specific biomechanical demands were not considered.
3. Methodologically, studies had to present quantitative biomechanical data related to joint kinematics, kinetics, ground reaction forces, or energy transfer. Statistical analyses had to be included, with numerical results provided for biomechanical changes associated with performance or injury risk. Studies lacking biomechanical relevance or statistical analyses were excluded.  
Figure 1: PRISMA flow diagram of the study selection process.  
Although only studies published in English were included, this language restriction was applied based on scientific considerations. Most high-quality biomechanical research in this domain is published in English, and non-English studies often lack methodological transparency or accessible full texts. While this decision helped ensure the consistency and reliability of the included data, we acknowledge that excluding non-English studies may introduce language bias. Future systematic reviews should consider including multilingual sources to enhance comprehensiveness.
# Containment Control-Guided Boundary Information for Semantic Segmentation
## 4 Experiment
### Qualitative Analysis  
Our network places special emphasis on the constraint of boundary information on internal information, resulting in more detailed delineation for classification. The qualitative analysis in Figure 6 highlights the superiority of our approach. In the first two images, our proposed CcNet demonstrates its superior performance by accurately identifying and segmenting small distant objects based on boundary features. For example, in the second image, traffic signs are located to the left of both the car and the second tree. The second traffic signpost on the left blends into the background due to its similar color, resulting in its omission in the PIDNet segmentation output. Although these signs are not annotated in the ground truth, our model effectively detects them, highlighting its robustness in segmenting subtle features. In the third image, multiple overlapping bicycles are closely positioned behind a truck. In this scenario, PIDNet generates a segmentation result that abruptly segments the bicycles, failing to capture their continuity and spatial relationships. This limitation indicates the difficulty of PIDNet in maintaining consistency in scenarios involving overlapping objects. In contrast, CcNet produces higher-quality segmentation results by effectively preserving object continuity and spatial context. The fourth image illustrates the limitations of PIDNet in correctly interpreting the seat portions of bicycles, which are misclassified as pedestrians. This misclassification underscores the challenges of PIDNet in distinguishing between similar objects and fine-grained details within complex scenes, while CcNet demonstrates improved capability in leveraging boundary features for accurate classification. In the fifth image, PIDNet overlooks certain well-defined classes, exposing its shortcomings in boundary delineation and class representation. In the sixth image, CcNet significantly enhances semantic information retrieval, particularly for distant fences; its ability to provide detailed semantic information for objects at varying distances reflects its advanced boundary recognition capabilities and overall robustness in semantic segmentation tasks. Overall, the results indicate that CcNet more effectively utilizes boundary information, providing more accurate and detailed semantic segmentation in complex traffic scenarios compared to PIDNet.  
Figure 7 demonstrate that after weighting through the FWF module, the boundary information achieves desirable constraint effects on both the detailed branch and the contextual branch. The boundaries between different categories are adequately delineated, further confirming the effectiveness of our approach.  
Figure 6: Qualitative analysis on Cityscapes.
###### Abstract  
In the presented research, conventional cyclic tension-compression tests and dynamic tests were performed on two types of asphalt mixes (AM). For the tension-compression tests, the complex modulus was obtained from the measurements of the axial stress and axial strain. For the dynamic tests, an automated impact hammer equipped with a load cell and an accelerometer were used to obtain the frequency response functions (FRFs) of the specimens at different temperatures. Two methods were proposed to back-calculate the complex modulus from the FRFs at each temperature: one using the 2S2P1D (two springs, two parabolic elements and one dashpot) model and the other considering a constant complex modulus. Then, a 2S2P1D linear viscoelastic model was calibrated to simulate the global linear viscoelastic behaviour back calculated from each of the proposed methods of analysis for the dynamic tests, and obtained from the tension-compression test results. The two methods of analysis of dynamic tests gave similar results. Calibrations from the tension-compression and dynamic tests also show an overall good agreement. However, the dynamic tests back analysis gave a slightly higher value of the norm of the complex modulus and a lower value of the phase angle compared to the tension-compression test data. This result may be explained by the nonlinearity of AM (strain amplitude is at least 100 times smaller for dynamic tests) and/or by ageing of the materials during the period between the tension-compression and the dynamic tests.  
Keywords: asphalt mixes; linear viscoelasticity; complex modulus; dynamic measurements; tension-compression tests; frequency response function; back-analysis; finite element method +
Footnote †: journal: Applied Sciences
## 2 The Whole Stage Stability of the ANC System with Online SPM  
Figure 1 shows an online secondary modeling ANC system with auxiliary noise. From the results of previous studies [29], it can be observed that the update of the SPM filter is computed as:  
\[\mathbf{\delta}(n+1)=\mathbf{\delta}(n)+\mu_{s}\mathbf{v}(n)f(n), \tag{1}\]  
where \(\mathbf{\delta}(n)=\left[\delta_{0}(n)\delta_{1}(n)\cdots\delta_{M-1}(n) \right]^{T}\) is the coefficient vector of the SPM filter with length \(M\) and \(f(n)\) is the error signal used to update the SPM and ANC filters. \(\mathbf{v}(n)\) is a Gaussian white noise vector and \(\mu_{s}\) is the step-size factor of the SPM filter. Before using the reference signal \(x(n)\) to update the ANC filter, it must be filtered by the secondary path \(s(n)\). If \(s(n)\) is unknown, \(s(n)\) is replaced by \(\mathbf{\delta}(n)\). The ANC filter update equation is as follows:  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu_{w}\mathbf{x}^{\prime}(n)f(n), \tag{2}\]  
where \(\mathbf{x}^{\prime}(n)=\left[x^{\prime}(n)x^{\prime}(n-1)\cdots x^{\prime}(n -L+1)\right]^{T}\) is the reference signal estimation vector with length \(L\), \(w(n)\) is the coefficient vector of the ANC filter with length \(L\), and \(\mu_{w}\) is the step-size factor of the ANC filter. Then, we will analyze the convergence performance of the ANC system, particularly the influence of the relative error of the secondary path on the system.  
Figure 1: ANC system with online SPM.  
To simplify the analysis, we make the following assumptions:  
1. The reference signal \(x(n)\) is white noise with a Gaussian distribution, the mean value is zero, and the variance is \(\sigma_{x}^{2}\). Therefore, there is no correlation between the reference signal vectors \(\mathbf{x}(n),\mathbf{x}(n-1),\mathbf{x}(n-2),\ldots\);
2. The auxiliary noise \(\mathbf{v}(n)\) is Gaussian white noise with a zero mean and variance \(\sigma_{v}^{2}\). Moreover, \(\mathbf{v}(n)\) and \(\mathbf{x}(n)\) are independent;
3. The coefficient vector \(\mathbf{w}(n)\) and reference signal vector \(\mathbf{x}(n)\) are independent, which is a widely used assumption.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 5 Key Challenges and Future Research Directions
### Heterogeneity Issues  
There are multitude of grid parameters that are required to be sensed for complete grid monitoring such as current, voltage, temperature, humidity, pressure, chemical, gas, and vibration. This calls for a heterogenous mixture of sensors that can sense such diverse and heterogenous data. However, such heterogenous data leads to challenges in data fusion which is an essential procedure while data preprocessing. Inefficient data fusion might lead to the loss of essential fault parameter features. Furthermore, the raw data can be captured in multiple forms such as waveform, values, images, videos, etc. This makes their integration with each other cumbersome and time-consuming. Due to the heterogeneity among different types of sensors, it is also difficult to build a heterogenous sensor network since different sensors will have different accuracy and different losses under the same grid conditions [5]. For example, some sensors like Rogowski coils or GMO sensors may not work well in high voltage networks as efficiently as optical sensors, etc. Furthermore, the output of different sensors varies under the same grid conditions. For example, some sensors might have faster responses than others, and some might have linear whereas others might have non-linear responses for the same grid parameters.
## 2 Materials and Methods
### Ozonation Pretreatment  
Ozonation was conducted in a semi-batch reactor that allows a continuous addition of ozone to a fixed amount of water with RPS (Figure 2). An oxygen-fed generator generated ozone (up to 4 g/h; BMT 802 N, Germany), and the oxygen/ozone mixture was bubbled directly into the reactor by a 4 cm\({}^{3}\) ceramic diffuser, with a nominal pore size of 25 \(\upmu\)m. A glass reactor of 20 cm in height and 7 cm in diameter was filled with a mixture of 10 g RPS in 400 mL DIW. Ozonation was conducted for 30 min at room temperature with continuous magnetic stirring and an average gas flow rate of 0.30 L/min. The concentration of ozone at the input and output was continuously monitored. A 2 mL sample was taken each 10 min. Transfered ozone dose (TOD) is an empirical parameter determining the amount of accumulated ozone that reacts with the examined material. Despite its everyday use for micropollutant elimination prediction in municipal wastewater ozonation, TOD can also determine ozone accumulation in lignocellulosic materials [41]. The ozone dose was estimated by continuous measurements of its concentration in the gas phase at the reactor's input and output. The total consumed ozone dose was 21 mg/g RPS.  
\[\text{TOD}\left[\frac{\text{mg}\,\text{O}_{3}}{\text{L}}\right]=\sum\Bigl{\{} \left(\text{C}_{\text{O}_{3}(\text{in})}-\text{C}_{\text{O}_{3}(\text{out})} \right)[\text{mg}/\text{L}]\cdot\text{GFR}[\text{L}/\text{min}]\Bigr{\}}\cdot \frac{\text{At}}{\text{V}\left[\text{L}\right] \tag{1}\]where \(C_{\text{O}_{3}(\text{in})}\) is the ozone concentration in the input stream, \(C_{\text{O}_{3}(\text{out})}\) is the ozone concentration in the output stream; representing the ozone that did not react, GFR is the gas flow rate, \(\Delta\)t is the time interval between measurements set at 1 minute, and V is the volume of the reactor's content.
## 3 Results and Discussion
### Microscopic of Sands and Bonding Sections  
#### 3.5.1 Analysis Results of Desert Sand and Natural Sand  
As depicted in Figures 7 and 8, SEM and XRD analyses were conducted on washed natural sand and desert sand to determine the differences in raw material properties.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen** & \multicolumn{2}{c}{**Bond Strength**} & \multicolumn{2}{c}{**Average Number of Cracks**} \\
**Number** & \multicolumn{2}{c}{**/MPa**} & \multicolumn{2}{c}{**/N**} \\ \hline ECC & NSPE\(-\)ECC & DSPE\(-\)ECC & NSPE\(-\)ECC & DSPE\(-\)ECC \\
3d-1 & 2.42 & 1.78 & 8 & 5 \\
3d-2 & 2.24 & 1.90 & 9 & 8 \\
3d-3 & 2.29 & 1.14 & 5 & 5 \\
7d-1 & 2.30 & 2.34 & 11 & 8 \\
7d-2 & 2.61 & 2.51 & 12 & 11 \\
7d-3 & 2.36 & 2.10 & 6 & 22 \\
14d-1 & 2.34 & 2.23 & 5 & 4 \\
14d-2 & 2.00 & 2.23 & 32 & 14 \\
14d-3 & 2.40 & 2.38 & 5 & 20 \\
28d-1 & 2.76 & 2.56 & 75 & 13 \\
28d-2 & 2.72 & 2.57 & 22 & 30 \\
28d-3 & 2.68 & 2.78 & 27 & 5 \\ \hline \end{tabular} Note: A-B, A and B represent age and specimen number, respectively.  
\end{table}
Table 7: PE\(-\)ECC bonding properties calculation results.  
Figure 8: Sand diffraction diagram.  
Figure 7: Microstructures of the specimens: (**a**) 500 \(\upmu\)m, (**b**) 50 \(\upmu\)m of natural Sand, (**c**) 500 \(\upmu\)m, (**d**) 50 \(\upmu\)m of desert sand and replacement rate.  
Figure 7 shows the uneven surface of natural sand, which was poured into the cement base as a fine aggregate. The rough surface would expand the reaction area when the cement hydration reaction begins to accelerate the hydration process. Therefore, the ultimate load of NSPE\(-\)ECC at 3 d was higher than that of DSPE\(-\)ECC at the same age. The uneven surface morphology provided a larger bonding surface for cement and increased the bonding strength of the cement base. Desert sand has a smaller particle size, a smoother surface, and higher water absorption compared to natural sand, reducing the cement matrix's bonding properties and the degree of cement hydration reaction. The small grain size of desert sand also filled the micropores in the cement matrix, reducing the number of micropores and enhancing the fiber/matrix interfacial adhesion so that the crack load of DSPE\(-\)ECC was higher than that of NSPE\(-\)ECC.  
As shown in Figure 8, in 20 = 26\({}^{\circ}\) (\(\theta\) is the rotation angle of the sample, and 20 is the rotation angle of the ray collected by the probe), the desert sand has a high SiO\({}_{2}\) content, indicating excellent crystallinity and hardness. Extruding the fiber roughens the fiber surface, which increases the frictional force between the fiber and matrix and prevents cracking. However, the fiber broke more easily during subsequent stresses, causing the test piece to fail; thus, DSPE\(-\)ECC had a high crack load and weak strain capacity. Desert sand was more alkaline than natural sand because Al\({}_{2}\)O\({}_{3}\) (20 = 68\({}^{\circ}\)) was more abundant in alkaline oxides in the desert sand. An alkaline environment induces an alkaline aggregate reaction in the matrix, reducing the matrix's durability and cohesion.
###### Abstract  
Various rehabilitation robots have been developed to assist the movement training of the upper limbs of stroke patients, among which some have been used to evaluate the motor recovery. However, how to understand the recovery of motor function from the quantitative assessment following robot-assisted rehabilitation training is still not clear. The objective of this study is to propose a quantitative assessment method of motor function based on the force and trajectory characteristics during robotic training to reflect motor functional recovery. To assist stroke patients who are not able to move voluntarily, an assistive training mode was developed for the robot-assisted rehabilitation system based on admittance control. Then, to validate the relationship between characteristic information and functional recovery, a clinical experiment was conducted, in which nine stroke patients and nine healthy subjects were recruited. The results showed a significant difference in movement range and movement smoothness during trajectory tracking tasks between stroke patients and healthy subjects. The two parameters above have a correlation with the Fugl-Meyer Assessment for Upper Extremity (FMU) of the involved patients. The multiple linear regression analysis showed FMU was positively correlated with parameters (\(R^{2}=0.91,p<0.005\)). This finding indicated that the above-mentioned method can achieve quantitative assessment of motor function for stroke patients during robot-assisted rehabilitation training, which can contribute to promoting rehabilitation robots in clinical practice.  
upper limb rehabilitation robot; admittance control; rehabilitation training; quantitative assessment; trajectory tracking task +
Footnote †: journal: Applied Sciences
# A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture
## 3 Digital Twin Architecture
### Framework  
In this phase, the layered structure and functionalities of the DT architecture are identified. The layered structure is depicted in Figure 4 and is illustrated next:  
* Greenhouse: Physical asset to optimize; see Figure 3.
* Controller: Respectively, monitors and controls the greenhouse sensors and actuators. It also transmits the acquired sensor data to the gateway and receives the crop treatments and climate control strategies to implement.
* Gateway: Interface between the cyber and the physical domain; see Figure 1. It is responsible for transmitting sensor data to the storage layer and communicating the optimized crop treatments and climate control strategies to the controller for its implementation.
* Storage: Stores current and historical data that are utilized from the DTs for productivity optimization.
* Intelligence layer: Hosts the rules and the knowledge to choose among the different crop treatments and climate control strategies that may optimize the productivity of the greenhouse. It uses the DTs as virtual test beds to assess the evaluated alternatives.
* Digital twins: Utilizes current and historical data to assess the different crop treatments and climate control strategies received from the intelligence layer.
## 3 Proposed Model
### Training Process  
The model shown in Figure 7 was used for the training process. The model estimates the parameters of GPR targets and equalizers, as described in Section 2.1. We used training data to collect signals \(a[j,k]\) and \(y[j,k]\). The Appendix A describes how we converted the signals \(a[j,k]\) and \(y[j,k]\) into vectors \(\mathbf{a}\) and \(\mathbf{y}\), respectively. We calculated the parameters \(\mathbf{R}\), \(\mathbf{T}\), and \(\mathbf{A}\) from \(\mathbf{a}\) and \(\mathbf{y}\). Finally, using the conditions (A11), (A12), (A14), (A15), and (A17), (A18), and applying (A20)-(A22) in the Appendix A, we calculated the coefficients of the GPR targets \(\mathbf{G}_{\rm{2 way}}\), \(\mathbf{G}_{\rm{4 way}}\), and \(\mathbf{G}_{\rm{8 way}}\) and equalizers \(\mathbf{F}_{\rm{2 way}}\), \(\mathbf{F}_{\rm{4 way}}\), and \(\mathbf{F}_{\rm{8 way}}\), respectively. The estimating of the equalizer and target is similar to training the coefficients of the equalizer and target. The parameters of the GPR target were then supplied to the estimator for interference and detection in the testing process.