esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Factors Affecting SOM Content
#### 3.3.3 Differences in SOM Content across Different Soil Types  
Different soil types have different soil forming conditions and nutrient sources, so their SOM level is different. The study area includes 11 main types of soil. The ANOVA shows that SOM content was significantly different in different soil types. Comparison of SOM between soil samples from different soils was conducted, with the results shown in Table 5. The mean SOM content of the Time concretion black soil was the highest at 15.94 g\(\cdot\)kg\({}^{-1}\), followed by paddy soil, cinnamon soil, rocky soil, skeleton soil, brown soil, alluvial soil, red clay soil, saline soil, and fluvent soil. The lowest average SOM level, 6.43 g\(\cdot\)kg\({}^{-1}\), was found in aeolian sandy soils. The variation coefficients for all soil types ranged from 11.69% to 100.31%, with red clay soil having the smallest coefficient of variability at 11.69%, indicating that this soil type has a stable SOM content and is conducive to SOM accumulation. The variation coefficient was the largest in Time concretion black soil, indicating that the SOM content varied greatly in this soil type.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **N** & **Min/(g\(\cdot\)kg\({}^{-1}\))** & **Max/(g\(\cdot\)kg\({}^{-1}\))** & **Mean/(g\(\cdot\)kg\({}^{-1}\))** & **CV/(\%)** \\ \hline Sandy loam & 64 & 2.40 & 74.30 & 8.78 & 110.60 \\ Loam & 13 & 5.30 & 13.60 & 8.06 & 29.37 \\ Clay loam & 77 & 3.50 & 22.40 & 10.68 & 32.81 \\ Sandy clay & 56 & 5.20 & 74.90 & 11.36 & 80.60 \\ Loam clay & 30 & 6.15 & 33.66 & 15.32 & 49.68 \\ clay & 6 & 6.90 & 17.09 & 11.11 & 30.28 \\ Silty clay & 4 & 8.80 & 20.30 & 12.28 & 43.95 \\ Loamy sand & 3 & 2.50 & 4.10 & 3.57 & 25.90 \\ Sandy soil & 4 & 1.20 & 5.27 & 3.07 & 56.82 \\ \hline \end{tabular}
\end{table}
Table 4: Analysis of SOM content in different soil textures.  
Figure 4: SOM content of different soil textures. Notes: The maximum mean value is marked with the letter a; the letter b represents the mean value that significantly different from the mean value marked with the letter a; the letter c represents the mean value that significantly different from the mean value marked with the letter b; data with the same letter are not significant at 0.05 level.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 3 Results
### Main Findings
#### 3.2.4 Fatigue Alters Biomechanics & Muscle Activity  
Sprinting under fatigue conditions resulted in earlier activation of the hamstrings during the swing phase, likely as a protective mechanism against muscle strain. Fatigue was associated with reduced hip and knee flexion angles, leading to increased ground contact time and diminished sprinting efficiency.
## 4 Experiments and Discussion
### Experimental Results  
This section shows the prediction results of MFDGCN on traffic flow and compares it with other baseline models. The results are shown in Table 1. We aggregated the MAE, RMSE, and MAPE metrics for the prediction results of the model over the next 15, 30, and 60 min on the PeMS_BAY data set, respectively. In this experiment, five-fold cross-validation was performed on the neural network model and the average value was taken as the final result. Table 1 shows that the non-neural network models (HA, VAR, SVR) perform poorly for traffic flow prediction, mainly because they cannot directly model spatio-temporal dependencies in the traffic flow. The neural network model has strong feature learning ability and can achieve good results in traffic flow prediction. Our MFDGCN model outperformed other baseline models in MAE, RMSE, and MAPE by comprehensively comparing long-term and short-term prediction effects. As shown in the table, MFDGCN has a MAE that is 4% lower for a short-term prediction of 15 min and a RMSE that is 7% lower when comparing with the GMAN model with better long-term prediction performance. MFDGCN has a MAE that is 7% lower for a long-term prediction of 60 min and a RMSE that is 19% lower when comparing with the GWnet model with better short-term prediction performance. In the comprehensive comparison, the MFDGCN model goes further when compared to the GWnet and GMAN models. It fuses spatio-temporal features better, obtains richer spatio-temporal dependencies, and achieves better results in both short-term and long-term traffic prediction. As the prediction duration increases, the long-term prediction performance of each model will decline, but the decline for MFDGCN is smaller and its advantage is more obvious. Compared with STGCN, DCRNN, GWnet, and GMAN modeled using a simple distance graph and STSGCN modeled with a local node correlation graph, MFDGCN uses dynamic and static fusion graph modeling in spatial and temporal dimensions. This can not only capture relevant information from local nodes in the road traffic network, but also capture relevant information from global nodes. It can learn the similar relationship between non-adjacent nodes and improve prediction performance. Compared with STGCN that splits the spatio-temporal association, STSGCN concatenates spatio-temporal information and GWnet adds the stacked spatio-temporal information, MFDGCN uses an adaptive gating mechanism to fuse the convolutional spatio-temporal information. It then fuses the embedded spatio-temporal information through a multi-head attention mechanism, which can capture longer-term spatio-temporal relationships and achieve better long-term prediction performance. MFDGCN shows better short-term prediction performance by using multi-layer stacked and dilated temporal convolutions. It is 4% lower in MAE for a short-term prediction of 15 min and 1% lower in MAE at 30 min when compared with GMAN where a transformer is used. However, MFDGCN is slightly lacking in long-term prediction when
## 1 Introduction  
The application of High-Strength Concrete (HSC) in major national projects within the arid regions of Northwestern China is an inevitable trend due to its distinct economic and technological advantages, aligning with the developmental requirements of the era. However, the environmental Relative Humidity (RH) significantly impacts the shrinkage deformation of concrete. As [PERSON] et al. [1], Ordinary Portland Cement Concrete (OPC) exhibits approximately five times greater shrinkage deformation at 48% RH compared to that at 92% RH, while HSC demonstrates a more substantial sevenfold increase in shrinkage deformation under similar conditions. [PERSON] et al. [2] reported that with an increase in the strength grade of concrete, shrinkage expands, particularly under lower RH conditions. In the arid Northwestern regions of China, where environmental RH typically ranges from 30% to 55%, common techniques to mitigate HSC shrinkage deformation involve the incorporation of expansive agents and steel fibers [3; 4; 5; 6]. [PERSON] et al. [7; 8] investigated the effectiveness of steel fiber in concrete containing expansion cement, finding that the simultaneous addition of steel fiber and an expansion agent can improve the volume stability of concrete under dry conditions. [PERSON] et al. [9] reported that the expansive agent can significantly enhance the interface quality between the fiber and matrix by increasing adhesion. [PERSON] et al. [10] investigated the effect of hybrid CaO and etringite-based expansive agents on the mechanical properties and durability of shrinkage-compensating concrete.  
Considering the lowest average temperature in northwest China is about \(-15\,\mathrm{\SIUnitSymbolCels  
effects in improving the frost resistance of concrete, with polypropylene fibers being a less costly option. Research indicates that the inclusion of steel fibers can enhance the pore structure of concrete [13]. Additionally, the addition of mineral admixtures to fiber concrete can improve its frost resistance [14]. [PERSON] et al. [15] found through experiments that the frost resistance of self-compacting concrete increases with higher fiber content. However, there is a scarcity of literature on the combined impact of expansive agents and fibers on non-air-entrained HSC freeze-thaw resistance, as well as the correlation between HSC freeze-thaw resistance and RH.  
This study primarily employs Portland cement and aluminate expansion agents, along with steel fibers or High Elasticity Module Polyethylene Fiber (HEMPF), as reinforcement materials. Through single additions of expansion agent, different fiber materials, and their composites, the study employs a rapid freezing method to investigate the freeze-thaw resistance of non-air-entrained HSC. It explores the formulation technology of HSC that is suitable for application in the arid climate conditions of the Northwest region.
# Efficiency of Algorithms for Computing Influence and Information Spreading on Social Networks
## 2 Methods
### Algorithms
#### 2.2.1 Complex Contagion Algorithm
After calculations are finished, \(P_{0}(i)\), i.e., paths ending at \(i\) with no nodes left, stores the probability of spreading from \(i\) to \(t\). The value of \(P_{L}(i)\) is calculated by combining the values of \(P_{L+1}(j)\) for all \(j\) which are connected to \(i\). At each step, we are essentially continuing all started paths backwards through all possible edges. In Figure 1, notice how length-one paths ending at \(A\) are used in two different contexts. They are extended with edges \((B,A)\) and \((E,A)\) separately (circled in Figure 1). Because,in the previous round, we have already computed and cached the value for \(P_{2}(A)\), we can use it in multiple contexts without needing to compute it again. Before the computation, \(P_{L_{\max}}(t)\) is initialised with the corresponding Poisson factor from Equation (1). Next, \(L_{\max}\) iterations follow, numbered from \(L_{\max}-1\) to 0. In iteration \(i\), new paths are started from \(t\), and already started paths obtain their \((i+1)\)-th node. For example, in the first iteration, when \(i=L_{\max}-1\), paths of length \(L_{\max}\) obtain their second to last node and paths of length \(L_{\max}-1\) are started from \(t\), with \(t\) as their \(L_{\max}\)-th node. For a fixed end node, the algorithm works in \(\mathcal{O}(E\cdot L_{\max})\) time, where \(E\) denotes the number of edges in the network and \(L_{\max}\) the maximum allowed path length (measured in edges), making the total time complexity of solving the influence spreading matrix \(\mathcal{O}(V\cdot E\cdot L_{\max})\). The \(|V|\) runs can easily be distributed to different cores or computation units as the calculations are completely independent, making the algorithm highly parallelisable. Moreover, the innermost loop's iterations are also independent from each other, and can thus be parallelised, on the CPU instruction level. These factors combined make running the algorithm highly efficient in practice. Memory-wise, the algorithm scales well for large networks as, at any time, we only need the \(P_{L}(i)\) values corresponding to the two latest values of \(L\). Thus, we need to store two floating point numbers for each node per run, making the memory complexity of a single run \(\mathcal{O}(V)\).
# Validation of Drifting Buoy Data for Ocean Wave Observation
## 2 Data and Methods
### GPS Wave Buoy Data  
The wave data from the GPS wave buoys [6; 22] were used to evaluate the performance of the drifting buoy wave data. Table 1 and Figure 1 summarize the locations of the moored GPS wave buoys.  
The distance between the moored GPS wave buoys and the coast was from 10 km to 20 km. Most of the water depths at the locations of the moored GPS wave buoys were greater than 100 m. The surface elevations were measured by the GPS receivers, not by the accelerometers. The sampling interval of the surface elevations was 1 s. The moored GPS-estimated wave height (\(H_{g}\)) and period (\(T_{g}\)) were estimated by the zero-up crossing method (\(H_{g}\) is \(H_{1/3}\), and \(T_{g}\) is \(T_{1/3}\)) from 1024 surface elevations [23]. The wave height (\(H_{g}\)) and period (\(T_{g}\)) were estimated at 20 min intervals. The GPS wave data can be downloaded from [[https://www.mlit.go.jp/kowan/nowphas/index_eng.html](https://www.mlit.go.jp/kowan/nowphas/index_eng.html)]([https://www.mlit.go.jp/kowan/nowphas/index_eng.html](https://www.mlit.go.jp/kowan/nowphas/index_eng.html)) (accessed on 30 June 2021). The resolutions of \(H_{g}\) and \(T_{g}\) were 0.01 m and 0.1 s, respectively. The wave height and period of the closest GPS buoy were compared when a drifting buoy was close to one of the moored GPS buoys.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Buoy** & **Lon. (\({}^{\circ}\) E)** & **Lat. (\({}^{\circ}\) N)** & **Depth (m)** & **Start (Day/Month/Year)** & \(N_{c}\) \\ \hline A & 139.938 & 40.782 & 125 & 6 January 2011 & \\ B & 139.661 & 40.211 & 104 & 1 January 2011 & \\ C & 139.601 & 38.975 & 104 & 1 January 2011 & 1 \\ D & 141.750 & 40.633 & 87 & 1 January 2009 & 14 \\ E & 142.067 & 40.117 & 125 & 16 March 2009 & 13 \\ F & 142.187 & 39.627 & 200 & 1 January 2009 & 19 \\ G & 142.097 & 39.259 & 204 & 1 January 2009 & 1 \\ H & 141.894 & 38.858 & 160 & 1 January 2009 & 1 \\ I & 141.684 & 38.233 & 144 & 1 January 2009 & \\ J & 141.186 & 36.971 & 137 & 26 May 2009 & 6 \\ K & 138.275 & 34.403 & 120 & 12 February 2009 & 1 \\ L & 137.125 & 34.374 & 90 & 26 June 2013 & \\ M & 136.259 & 33.902 & 210 & 1 January 2009 & \\ N & 135.157 & 33.642 & 201 & 1 January 2009 & \\ O & 134.497 & 33.461 & 430 & 20 January 2010 & \\ P & 134.186 & 33.079 & 288 & 1 January 2015 & 11 \\ Q & 133.156 & 32.631 & 309 & 1 January 2009 & 35 \\ R & 131.910 & 32.387 & 407 & 12 March 2014 & \\ S & 134.203 & 33.141 & 100 & 19 June 2004 & \\ \hline \end{tabular}
\end{table}
Table 1: Locations and observation start time of the moored GPS buoys, and number of comparisons (\(N_{c}\)) with JMA buoys in Figure 2. Figure 1 show the locations of the GPS buoys. Buoy S provides wave data until 2006.  
Figure 1: Locations of the moored GPS buoys in Table 1. Blue: Area of \(|\langle\mathbf{U}_{r}\rangle|>0.6\) ms\({}^{-1}\) (from 2001 to 2017). Black squares: Land of ERA5 grids. Blue dashed line: Boundary between sea and land of the ERA5 grids.
## 2 Model Establishment and Parameter Selection
### Parameter Selection  
The macro-mechanical parameters used in this simulation of sandstone are referred to as the data measured in the uniaxial compression experiments in [PERSON]'s paper [25], as shown in Table 1. The RFPA2D Basic V2.0 software introduced the Weibull distribution function to characterize the non-uniformity of the properties of the medium, with the homogeneity coefficient m as the link to establish the medium's macro-mechanical parameters and mesoscopic mechanical parameters. The fine mechanical parameters of the medium can be calculated by the following fitting equation:  
\[\begin{array}{ll}\frac{E_{macroscopic}}{E_{microscopic}}&=0.1412\ln m+0.6476(1. 2\leq m\leq 10)\\ \frac{f_{macroscopic}}{f_{microscopic}}&=0.2602\ln m+0.0233(1.2\leq m\leq 50) \end{array} \tag{1}\]  
where \(E_{macroscopic}\) represents the macroscopic strength of the dielectric material, \(f_{macroscopic}\) represents the macroscopic elastic modulus of the dielectric material, \(E_{microscopic}\) represents the mesoscopic mean of the dielectric strength, and \(f_{microscopic}\) represents the mesoscopic mean of the dielectric elastic modulus.  
The homogeneity coefficient m of strength and elastic modulus is 2, and the homogeneity of Poisson's ratio and density is 100. The calculated meso-mechanical parameters and other input parameters of the model are summarized in Table 2.  
In this simulation study, four different bedding planes were set, and the strength of the bedding plane was taken as 0.3, 0.6, 1.0, and 1.5 times the strength of the sandstone  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Compressive Strength (MPa)** & **Elasticity Modulus (GPa)** & **Poisson Ratio** & **Internal Friction Angle** & **Density** \\ \hline
57.5 & 12.7 & 0.15 & 40 & 2400 \\ \hline \end{tabular}
\end{table}
Table 1: Macroscopic mechanical parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category of the** & **Value of the** & **Name of Parameter** & **Name of Parameter** & **Value of the Parameter** \\ \hline \multirow{4}{*}{Control parameters} & Residual strength/MPa & 0.1 & Internal friction angle/\({}^{\circ}\) & 40 \\  & Residual Poisson’s ratio & 1.1 & Tension and compression ratio & 10 \\  & Failure criterion & Mohr–Coulomb & Maximum compressive strain & Control variable \\  & Maximum tensile strain & 1.5 & Single-step load/mm & 0.001 \\ \hline \end{tabular}
\end{table}
Table 2: Model input parameters.  
matrix, while all other parameters were kept constant. It should be noted that the laminae with 1.0 times the strength of the matrix are homogeneous sandstone, and they were set as the control group to more intuitively reveal the effect of bedding strength on the fracture characteristics of sandstone. Due to the large coordinate parameters required to draw the bedding plane, the vertex coordinates of each bedding plane are listed in Table 3 only with the inclination angle of the bedding plane being 45\({}^{\circ}\). The bedding planes are numbered from left to right.
## 5 Results and Discussion
### Comparison of Multiplier Structures
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Design** & **Type** & **TSB** & **AND** & **XOR** & **MUX** & **Latches** & **Latency** & **CPD** \\ \hline
[8] & Systolic & 0 & \(3k\) & \(3k\) & \(3k\) & \(14k\) & \(3k\) & \(\Pi_{A}+\Pi_{X}+\Pi_{M}\) \\ \hline
[24] & Systolic & 0 & \(4k-1\) & \(2k-1\) & \(k\) & \(12k-7\) & \(3k-2\) & \(\Pi_{A}+\Pi_{X}\) \\ \hline
[26] & Systolic & 0 & \(3k\) & \(3k\) & \(3k\) & \(14k\) & \(3k-1\) & \(\Pi_{A}+T_{X}+\Pi_{M}\) \\ \hline
[38] & Non-Systolic & 0 & \(R1\)\({}^{(1)}\) & \(R2\)\({}^{(1)}\) & 0 & \(R3\)\({}^{(1)}\) & \(k\) & \(\Pi_{A}+(2+[\log_{2}(k)])\Pi_{X}\) \\ \hline
[9] & Non-Systolic & 0 & \(R4\)\({}^{(2)}\) & \(R5\)\({}^{(2)}\) & 0 & \(R6\)\({}^{(2)}\) & \(k\) & \(\Pi_{A}+[\log_{2}(k)]\Pi_{X}\) \\ \hline
[10] & Semi-Systolic & 0 & \(6y\)\({}^{(3)}\) & \(6y+2\) & \(2y\) & \(27y-3\) & \(3y+k+1\) & \(\Pi_{A}+\Pi_{X}+\Pi_{M}\) \\ \hline
[13] & Systolic & \(2k\) & \(3k\) & \(3k\) & \(k\) & \(9k\) & \(3k-2\) & \(\Pi_{A}+\Pi_{X}+\Pi_{M}\) \\ \hline Proposed & Systolic & \(k\) & \(3k+3\) & \(3k+2\) & 0 & \(8k-2\) & \(2k-2\) & \(\Pi_{A}+2\Pi_{X}\) \\ \hline \hline \end{tabular} \({}^{(1)}\)\(R1=5k+3\), \(R2=5k+2q-4\), \(R3=19k+4q-4\), where \(q\) denotes the exponent of the second term in the trinomial expression \((x^{k}+x^{q}+1)\). \({}^{(2)}\)\(R4=7k+6\), \(R5=7k+6\), \(R6=18k-2q-2\), \({}^{(3)}\)\(y=[k/2]\).  
\end{table} 
Table 1: Comparing area and delay complexity: conventional vs. bit-serial multiplier architectures. When considering time complexity, it is evident that the non-systolic multiplier designs mentioned in [9; 38] exhibit significantly reduced latency compared to all the compared designs, including the recommended one. However, they also have a considerably higher CPD. In comparison to the [PERSON] design [8], the proposed multiplier layout has notably lower latency and almost the same CPD (assuming the delay of the MUX is similar to that of the XOR gate). The Fenn multiplier [24] has a higher latency but a smaller CPD with the delay of one XOR gate compared to the recommended design. In the case of [PERSON]'s multiplier structures [10; 13], they exhibit significantly larger latency but almost the same CPD as the proposed design. Given the variations in gate counts, latency, and CPD among the different designs, relying solely on analytical analysis to estimate the required area and computation time for each design may be challenging. Therefore, it is crucial to validate the conducted qualitative analysis through real implementations and performance measurements.
## 3 Results and Discussion
### Light Absorption Contribution of BC and BrC  
The BC\({}_{880}\) concentration calculated using the mass absorption cross-section (MAC) at a wavelength of 880 nm (7.77 m\({}^{2}\) g\({}^{-1}\)) was 1.02 ug m\({}^{-3}\), which accounted for 5.4% (ranging from 1.7% to 14.4%) of the PM\({}_{2.5}\) mass. The BC\({}_{370}\) concentration, calculated using MAC at a wavelength of 370 nm (18.47 m\({}^{2}\) g\({}^{-1}\)), was 1.5 ug m\({}^{-3}\), representing 8.1% (ranging from 2.4% to 27.5%) of the BC\({}_{880}\) concentration. The temporal variation in EC and BC concentrations measured at 880 nm exhibited a similar trend, with the highest levels observed during the morning commute and gradually decreasing in the afternoon before increasing again during the evening commute. These results are attributed to increased vehicular traffic travelling on the four-lane road near the measurement site during commuting hours.  
The absorption coefficients at 880 nm and 370 nm ranged from 1.8 to 20.4 Mm\({}^{-1}\) and 5.2 to 96.3 Mm\({}^{-1}\), respectively, while the absorption coefficient at 370 nm was 3.5 times that at 880 nm (ranging from 2.6 to 5.6). These results indicate that in addition to BC, other carbonaceous particles absorb light in the ultraviolet wavelength range. According to previous studies [34; 35; 36], BB has been identified as a primary source of BrC, and the optical absorption properties of BrC are significantly influenced by fuel types and the conditions of combustion. This study evaluated the BrC\({}_{370}\) absorption coefficient using the BC absorption coefficient measured during observation. Figure 3f shows the temporal variation in the BrC absorption coefficient at 370 nm, evaluated by Equation (1) using the aerosol light absorption coefficients. The average light absorption coefficient of BrC\({}_{370}\) was 9.6 mM\({}^{-1}\), accounting for about 30% of BC\({}_{370}\) (19.1 mM\({}^{-1}\)). The time-resolved BrC absorption coefficients at 370 nm exhibited a trend similar to BC, reaching peak values in the same hours of the day, and the mass absorption coefficient of BrC had higher values at night and a diurnal pattern consistent with enhanced nocturnal primary emission in winter. As a result of comparing the contributions of BrC\({}_{370}\) and BC\({}_{370}\) to the total optical absorption coefficient measured at 370 nm, the daily changes showed contrasting trends. The contribution of BC\({}_{370}\) tended to increase from morning to afternoon, and BrC\({}_{370}\) tended to increase from evening to dawn. These results show that local primary emissions occur at night. It can be inferred that BB and vehicle emissions mainly influence the primary emission sources at the study site during commuting hours and BB activities such as heating and incineration, occurring at night. Table 2 summarized a summary of previous research findings on BC\({}_{370}\) and BrC\({}_{370}\). It compiles data across different studies and provides information on the absorption levels, contribution percentages, and the locations of the studies [10; 27; 37; 38; 39; 40].