esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection
#### 2.3.3 STS Test  
A 3D MoCap system (Vicon, Oxford, UK) consisting of 8 Vicon cameras was used to collect lower limb kinematics from a marker placed on the greater trochanter during the test. Ground reaction forces (GRF) were measured using a force platform (AMTI Inc., Watertown, MA, USA) placed in front of a box. Participants were instructed to sit on the box (height: 0.49 m), maintaining the trunk and shank perpendicular to the ground, arms crossed to the chest, and feet placed on the force plate (Figure 1). At the \"start\" command of the operator, participants had to stand (concentric phase) and sit (eccentric phase) from the box five times consecutively as fast as possible. The trial was considered valid if (i) the stance of the feet was unchanged throughout the test and (ii) the trunk reached the vertical position at the end of all concentric and eccentric phases. Otherwise, the test was repeated. To check for these requirements and to ensure safety, an operator was positioned close to the participant. Before commencing the test, all participants performed a 5-min warm-up on a cycle ergometer (Monark 814 E, Monark, Vargerb SE) at 50 W (60 rpm) and four lower-limb active mobility exercises [19]. Following a familiarization session, participants performed two trials of 5 STS tests under four different conditions: body weight (BW), + 12.5% BW, + 25% BW, and + 32.5% BW. The additional weight consisted of a weighted west (Weight Vest bv30, Lacertosus, Parma, IT) worn immediately before the trial and secured to the participants' abdomen with a belt strap. The order of the trials at the different weight conditions was randomized and counterbalanced. Participants were instructed to perform a single sit-to-stand movement before each testing condition in order to familiarize the participants with the different weights. To avoid fatigue accumulation, each condition was repeated twice, with a 3-min recovery between trials and a 5-min recovery between conditions.  
Figure 1: The picture illustrates the initial position of the participants during the test. The participant was seated on the box with the trunk and shank positioned perpendicular to the ground. The force plate, weighted vest, and marker on the trochanter have been highlighted in the figure.
# MSWI Bottom Ash Application to Resist Sulfate Attack on Concrete
###### Abstract  
This research provides a strategy for partially replacing cement with municipal solid waste incineration (MSWI) bottom ash (BA) to improve the performance of concrete against sulphate attack. Mortar strength tests were performed firstly to evaluate the hydration activity of the ground BA. Concrete specimens were cured in standard conditions and immersed in a solution that contained 10% sodium sulfate. Then, the compressive strength of these specimens was measured to investigate the mechanical properties and durability of the concrete. Next, the capillary porosity of the concrete was determined from the volume fractions of water lost in specimens. Finally, the transport of the sulphate solution in concrete was analyzed using capillary rise, crystallization rate, and solution absorption tests. The results indicated that BA had a certain hydration activity. The equivalent replacement of cement by BA decreased the compressive strength of the specimens but increased the durability of the concrete. There was an excellent correlation between capillary rise height, sulfate solution absorption amount, crystallization rate, and coarse capillary porosity. The addition of BA can decrease the coarse capillary porosity and further slow the capillary transport and crystallization of sulfate solution in concrete. Overall, the replacement of cement with BA can improve the durability of concrete and actualize the utilization of MSWI residues as a resource.  
MSWI bottom ash; concrete; sulfate attack; capillary transport; crystallization Article
# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
## 3 Methodology  
Figure 1 illustrates the MSA-YOLO algorithm. This algorithm comprises three main components: a multi-scale adaptive module, an image processing module, and a YOLO module. The image processing module comprises several advanced filters: a defogging filter, a white balance filter, a gamma filter, a contrast filter, a tone filter, and a sharpen filter. The multi-scale adaptive module is employed to optimize the parameters within the filtering module. These filters collaborate to enhance image quality by mitigating the effects of fog, adjusting color balance, optimizing brightness and contrast, refining tonal range, and sharpening details. This comprehensive approach significantly enhances the algorithm's overall performance, especially in adverse weather conditions. The YOLO algorithm integrates both local and global information, substantially enhancing the network's capability to detect objects of varying scales, even in challenging foggy conditions. This integration significantly improves detection accuracy and robustness, making the algorithm highly effective for real-world applications.
## 3 Impedance Matching and 3-dB Axial Ratio Bandwidth Improving Operation  
As clear from Figure 6, Cross shape feed produces a relatively narrow Impedance matching and 3-dB Axial ratio bandwidths. To tackle the issue, some modification has been made as shown in Figure 2d, in the new modification a metal plate working like a parasitic patch is introduced at top of the RDRA at an optimized location with optimized parameters. In order to keep the asymmetry of the design, The dimension of the parasitic patch on top of the RDRA has the same as other feed lengths. By introducing the parasitic patch on top shows significant improvement in the results.
# Highway Speed Prediction Using Gated Recurrent Unit Neural Networks
## 4 Results
### Prediction Performance  
This study compared the GRU with two alternative methods, ARIMA and LSTM, regarding their prediction performance. We analyzed the prediction performance for bus lanes and regular lanes separately. Figure 9 shows the prediction comparisons for bus lanes using MAE for one test day on the section from Seoul toll gate to Sihingal junction. The MAE of GRU was 3.42, the MAE of LSTM was 4.06, and the MAE of ARIMA was 4.24. In a similar fashion, Figure 10 presents the prediction comparisons for regular lanes. The MAE of GRU was 7.36, the MAE of LSTM was 8.37, and the MAE of ARIMA was 10.61.  
In Figure 9, the X-axis has 76 time intervals after removing non-operational time. Each interval (unit) is 15 min. The X-axis of Figure 10 has 96 time intervals because vehicles operated on general lanes during the whole day.  
The results with ten sets of test data in bus lanes are displayed in Table 2, and it can be seen that there are no outperformed methods. This visual impression was confirmed using an ANOVA test. The analysis revealed that prediction performance was not significantly affected by the type of method (\(p=0.0587\)).  
Similarly, Table 3 presents the results with ten sets of test data in regular lanes. It can be seen that the GRU outperformed other methods. This visual impression was confirmed using an ANOVA test. The analysis revealed that prediction performance was significantly affected by the type of method (\(p<0.05\)). A post hoc test (i.e., false discovery rate) indicated that there was a statistically significant difference between ARIMA and other methods (\(p<0.05\)). However, there was no statistically significant difference between LSTM and GRU in prediction accuracy (\(p=0.1137\)).  
In brief, there is no difference among methods for bus lanes. However, there are statistically significant differences among methods for regular lanes. The rationale behind this is that the DTG data include most vehicle information in the bus lane. However, the DTG data were not representative of general lanes because the DTG data did not include private vehicle information. Thus, it is non-trivial to predict general lane speed based on linear mathematics models such as ARIMA.  
Figure 9: Performance comparisons in bus lanes: red color for ground truth and blue color for prediction.  
Figure 10: Performance comparisons in regular lanes: red color for ground truth and blue color for prediction.
## 3 Neural Network Models for Arithmetic Reasoning
### Generic Deep Learning Architectures  
Many researchers have also tried to tackle mathematical reasoning by exploiting generic neural network architectures, hypothesizing that learning to perform numerical reasoning might not be qualitatively different from the acquisition of domain-general \"abstract reasoning\" abilities and should thus be treated as a general (though particularly challenging) learning problem. However, it has been repeatedly pointed out that generic neural network architectures struggle in the extrapolation regimen [16], with sequence-to-sequence or even advanced transformer-based architectures often failing in tasks requiring intermediate calculations [43]. The related question of whether and how recurrent networks generalize to sequences longer than those encountered during training has also been of enduring interest. Definitive solutions to such a challenging issue have not yet been found [42], although recent investigations have shown that relative position embeddings facilitate out-of-distribution length generalization in simple addition tasks [58].  
The research community has been actively exploring novel domain-general neural architectures that might overcome these limitations. One interesting approach to tackle algorithmic learning (a.k.a. \"program synthesis\") has been to augment generic deep learning architectures with external memory modules, which enable systematic abstraction even from few examples and can (at least theoretically) learn to approximate any algorithmic procedure [59,60]. Interestingly, neural models equipped with external memory have in deed been shown as able to generalize well beyond their training range in binary addition and multiplication problems [61]. Subsequent work has further refined these results by improving the format of the external memory, for example, by showing that a grid-like memory representation can significantly improve out-of-distribution generalization on decimal addition, both in terms of digit length and number of operands [40; 62].  
The idea of granting access to an external memory to solve complex problems is reminiscent of the notion of \"material representation\" introduced in anthropology, which has been recently elaborated on in the context of numerical cognition [63]. According to this view, abstract mathematical concepts would be a relatively recent cultural achievement, which have emerged thanks to the spread of numerical manipulation tools [64]. This perspective has recently been explored in computational models based on deep reinforcement learning, which can simulate the active interaction of a learning agent with external numerical representation devices [65; 66]. Related streams of research seek to improve deep learning models by granting them access to external tools [67], constructing modular architectures equipped with discrete knowledge bases and reasoning components [68], or creating multi-modal systems that can ground numerical symbols into visuo-spatial representations [49].
## 5 Conclusions  
The global vegetation phenology, including SOS, EOS, and LOS, was mapped using first-order derivative analysis on satellite remotely sensed images for the past two decades. SOS exhibited a significant early trend in 11.5% of the total area, at a rate 0.19 days/year at the global scale, and EOS presented a significant trend of delay in 5.2% of the global vegetated area, which together significantly extended LOS in 12.6% of the vegetated area. This study illustrated the variations in the spatio-temporal dynamics in the vegetation phenology among vegetation cover types and regions across the globe (SM).  
This study revealed interactions between vegetation phenology and climate changes. Both temperature and precipitation affect vegetation phenology. A higher temperature as a direct consequence of global warming advanced the vegetation green-up date. On the other hand, 75.9% and 20.2% of the vegetated area showed a positive correlation and significantly positive correlation between annual GPP and length of vegetation growing season (LOS), likely indicating an enhancing effect on vegetation productivity, and thus increased carbon uptake from the shifted vegetation phenology. Our study provides a comprehensive view on the changes in vegetation phenology of global terrestrial ecosystems during the last two decades. The interactions between the shifted vegetation phenology and climate changes may provide useful information for better understanding of the future trajectory of global climate changes.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs15092288/s1](https://www.mdpi.com/article/10.3390/rs15092288/s1)]([https://www.mdpi.com/article/10.3390/rs15092288/s1](https://www.mdpi.com/article/10.3390/rs15092288/s1)).  
Conceptualization, Z.S. and Y.X.; methodology, H.F. and M.S.; software, H.F. and M.S.; validation, J.T., X.T. and X.L.; formal analysis, H.F. and D.Q.; investigation, Z.S.; writing--original draft preparation, H.F.; writing--review and editing, Z.S.; visualization, H.F. and W.L.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Nos. 42171447, 41871296, and 41871312) and the Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources (No. 2022 NRM006).  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. The MODIS and meteorological data can all be found from Google Earth Engine at [[https://eartheengine.google.com](https://eartheengine.google.com)]([https://eartheengine.google.com](https://eartheengine.google.com)) (accessed on 28 July 2022). The fluxnet data can be found at [[https://fluxnet.org](https://fluxnet.org)]([https://fluxnet.org](https://fluxnet.org)) (accessed on 10 August 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Methodology
### Permeability Test Procedure  
The same high-pressure triaxial setup was used for the permeability tests by changing the bottom pedestal (Figure 3b). The oven-dried sample for intact permeability was removed from the oven and allowed to cool for 6 h, similar to the fracturing samples. Hydraulically fractured samples under 1 and 7 MPa confinement were dried to remove the water from the samples before the permeability tests were performed on them. As shown in Table 1, permeability tests were conducted for a series of confining pressures: 5, 10, 15, 20 and 25 MPa and gas injecting pressures: 1, 2, 4, 6, 8 and 10 MPa. To identify the possibility of enhancement of natural gas extraction by fracturing, N\({}_{2}\) gas permeability was tested under drained conditions. The system temperature was maintained at a 24 \({}^{\circ}\)C  
Figure 3: (**a**) Bottom pedestal for fracturing; (**b**) bottom pedestal for permeability; and (**c**) configuration of O rings with sample.  
Figure 2: High-pressure triaxial setup used to conduct fracturing and permeability tests.  
constant value and the downstream flow rate was monitored and recorded at one second intervals for each injection pressure using an XFM digital mass flow meter capable of measuring gas flow up to 5000 mL/min. The permeability tests were stopped when the downstream flow rate became constant. The permeability value for each condition was calculated using Equations (3) and (6).
## 3 Results
### Comparative Analysis
Last, Figure 11 shows the trend of the utility value over each iteration of the GPP's optimisation phase for 40 trials (Area 2-Scenario 1). In particular, the values represented at the initial time are the ones obtained at the end of the initialisation phase of each trial. Then, each iteration of the GPP algorithm tries to improve the utility value, which is therefore optimised over time. Moreover, the mean value of the utility at each iteration is shown in red to represent the mean trend obtained thanks to the optimisation procedure. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**AREA 3 (Figure 4c)**} \\ \hline
**Scenario** & **Planner** & **ME [bits]** & **PL [m]** & **O2E [m]** \\ \hline \multirow{3}{*}{First} & Genetic & \(0.51\pm 0.01\) & \(46.6\pm 7.41\) & \(30.4\pm 10.4\) \\  & Random & \(0.51\pm 0.02\) & \(47.0\pm 8.96\) & \(28.4\pm 11.1\) \\  & S-B & \(0.49\pm 0.11\) & \(10.8\pm 16.3\) & \(8.19\pm 11.9\) \\ \hline \multirow{3}{*}{Second} & Genetic & \(0.51\pm 0.01\) & \(46.4\pm 8.42\) & \(26.8\pm 23.3\) \\  & Random & \(0.51\pm 0.03\) & \(47.9\pm 7.14\) & \(25.6\pm 27.0\) \\  & S-B & \(0.45\pm 0.16\) & \(35.1\pm 21.6\) & \(28.5\pm 20.6\) \\ \hline \multirow{3}{*}{Third} & Genetic & \(0.51\pm 0.01\) & \(47.9\pm 6.37\) & \(37.0\pm 14.1\) \\  & Random & \(0.51\pm 0.02\) & \(47.7\pm 7.23\) & \(30.6\pm 26.2\) \\ \cline{1-1}  & S-B & \(0.49\pm 0.07\) & \(21.7\pm 28.0\) & \(16.9\pm 21.1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results, in terms of mean and three times the std. dev., of the 3 planners in 3 scenarios of Area 3. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **AREA 1** & \\ \hline
**Scenario 1** & **Scenario 2** & Scenario 3 \\ \hline Genetic: 85\% & Genetic: 67.5\% & Genetic: 82.5\% \\ Random: 15\% & Random: 27.5\% & Random: 15\% \\ S-B: 0\% & S-B: 5\% & S-B: 2.5\% \\ \hline \multicolumn{3}{c}{**AREA 2**} \\ \hline
**Scenario 1** & **Scenario 2** & Scenario 3 \\ \hline Genetic: 50\% & Genetic: 52.5\% & Genetic: 85\% \\ Random: 0\% & Random: 12.5\% & Random: 15\% \\ S-B: 50\% & S-B: 35\% & S-B: 0\% \\ \hline \multicolumn{3}{c}{**AREA 3**} \\ \hline
**Scenario 1** & **Scenario 2** & Scenario 3 \\ \hline Genetic: 65\% & Genetic: 70\% & Genetic: 80\% \\ Random: 20\% & Random: 22.5\% & Random: 12.5\% \\ S-B: 15\% & S-B: 7.5\% & S-B: 7.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of the ME metric of the 3 path planners over the 3 areas of interest with different starting points. Figure 5: Five best paths Area 1-Scenario 1. (**a**) Genetic; (**b**) Random; (**c**) S-B. Figure 6: Five best paths Area 2-Scenario 1. (**a**) Genetic; (**b**) Random; (**c**) S-B. Figure 8: Mean Entropy over 40 offline executions in scenario 1. (**a**) Area 1; (**b**) Area 2; (**c**) Area 3. Figure 7: Five best paths Area 3-Scenario 1. (**a**) Genetic; (**b**) Random; (**c**) S-B. Figure 10: Origin to End distance of the path over 40 offline executions in scenario 1. Figure 9: Path Length metric over 40 offline executions in scenario 1. (**a**) Area 1; (**b**) Area 2; (**c**) Area 3.
# A Case Study of a Large Unstable Mass Stabilization:
## 2 Materials and Methods
### Limit Equilibrium Analysis  
Stability analysis of the landslide was carried out in its natural state and after implementing the corrective measures based on limit equilibrium methods. These methods consider the static mechanical laws and assume the shear strength of the soil to be totally and simultaneously developed along the sliding surface (failure surface). With such  
Figure 4: New geotechnical investigation works: **(a)** Borehole performance; **(b)** Inclinometer installation; **(c)** Inventory of water points; **(d)** Example of the Quaternary material obtained in the boreholes.  
methods, the slope safety factor may be computed as the ratio between the available shear strength in the sliding surface and the needed shear strength to keep a strict equilibrium of the sliding mass.  
Nowadays, the most common way of applying limit equilibrium methods is using the method of slices. This method divides the soil sliding mass into many vertical slices to solve the stability problem. The method assumes that failure of the soil is governed by the Mohr-Coulomb criterion, slices behave as rigid bodies and no stresses exist inside each slice. The equation system obtained once the equilibrium of forces is established on each slice requires assuming different simplifications and/or hypotheses to solve the system, all of which leads to having several \"sub-methods\". In this work, [PERSON]'s method [44] and the Morgenstern-Price method [45] were used. The former is an approximate method that establishes the equilibrium of vertical forces and bending moments, assumes a horizontal resultant of the interslice forces and does not take into account interslice shear forces. The latter is an exact method that establishes the stability problem using the three equilibrium conditions in slices of differential thickness and assumes that the inclination of the forces between slices is proportional to a given function.  
Several slope profiles were selected to compute the landslide safety factor applying the two mentioned methods. Simulations were run with and without considering the seismic acceleration (0.10 g, with g being the gravitational acceleration).
## 4 System Design and Analysis
### Synergetic Software-defined Sampling and Detection Approach
#### 4.1.3 Lifecycle Management  
In order to implement Sonum, we need to optimize the sampled information in order to reduce the need for storage resources. Deleting old entries and reserving storage space for new flow is an effective way. With reference to the relevant provisions of the OpenFlow protocol timeout release, we perform probabilistic selective deletion of entries whose statistics are much lower than the sampling threshold, and delete flows with longer dwell times.