esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
## 3 Variance Characteristics of Surface Albedo for Essential Land Types and Scales
### Albedo Variances for Typical Land Types  
#### 3.1.1 Soil Albedo  
Soil albedo is generally used as the background value of scenarios and varies with various factors, such as soil surface irregularities, wetness, soil colour, the SZA, and biochar.  
Figure 4: Citation connections for highly cited papers. Colours indicate different clusters with similar research subjects, and sizes vary with citation frequency.  
Figure 5: Density plot of keywords. Colouring from red to blue indicates decreasing frequency.  
For tillage soil, there is a positive correlation between surface albedo and the SZA, while a negative relationship is observed with an increase in soil roughness and wetness [83]. Specifically, highly rough soils show stable surface albedos as the SZA increases, while the smooth soil surfaces show a gradual increase in albedo. Therefore, soil roughness can act as an indicator of the diurnal variation in soil albedo [84]. Meanwhile, soil albedo is more sensitive to surface roughness under dry conditions than wet soil. In addition, soil moisture has been used to tune soil colour mapping and albedo [85], and deep soil moisture also shows a link to decreases in bare soil and canopy albedos [86]. With an increasing mass of soil accumulated salt from non-saline conditions, soil albedo can reach as high as 0.3 [87]. Moss and biocrusts significantly reduce (43.4%) bare soil albedo due to the increased darkness (43.7%), roughness (90.4%) and moisture (20.7%) of the biocrust layer [88]. Biochar application shows decreased soil albedo [89], which results in a reduction of its overall climate mitigation benefit in production, but which shows a non-negligible advantage for soil amelioration [90; 91]. Spectral albedos of desert surfaces (i.e., Gobi and sand dunes) in China were measured at spectral ranges of 0.35 to 2.5 \(\upmu\)m in the spring and autumn [92], and lower (0.05-0.11), obviously higher (0.20-0.30), and maximum values (0.37-0.49), and more variability in surface albedos at UV wavelengths, approximately 0.6 \(\upmu\)m, around 1.8-2.2 \(\upmu\)m, and Gobi surfaces were found, respectively.
# Cooperative Terrestrial-Underwater FSO System: Design and Performance Analysis
## 5 Results
Figure 3: Average bit error rate of the cooperative FSO-TU as a function of the SINR for strong, moderate and weak atmospheric turbulence intensities considering weather conditions corresponding to very clear air, clear air, and haze, and a jitter with \(\sigma_{\rho}=10\) cm. Weak oceanic turbulence and clear ocean water condition are assumed. Following the approach described at the beginning of the section, the results of the impact of the underwater link degradation factors on BER performance are drawn in Figure 5. Again, the most favourable conditions are assumed for the terrestrial channel, i.e., weak atmospheric turbulence, a jitter due to the pointing error of 0.1 m and a path loss, optical-wireless networks \(h_{l}=1\). Specifically, Figure 5 shows the average BER of the cooperative FSO-TU system as a function of SINR obtained analytically, using Equation (36), and numerically, using Monte Carlo simulations, for three different underwater turbulence conditions and three different depths under clear ocean water conditions. As in Figure 3, the solid, dashed and dotted line curves represent the BER analytical results for oceanic conditions with strong, moderate and weak turbulence, while the blue, red and black colors indicate depths of 20, 30 and 40 m, respectively. Numerical results are represented with circular markers. It is worth noting that, again, a perfect match is shown between the simulated results and those obtained from the closed-form expression derived in this work. Figure 5 shows much more clearly than Figure 3 the appearance of the BER floor explained above. We note that as the SINR increases, all curves tend to a constant BER value that no longer decreases. The specific BER floor value depends on the level of ocean turbulence and the depth of the link, although both factors are, in turn, related. In addition, the results shown in Figure 5 indicate that ocean turbulence has a determining influence on the BER floor. Thus, even for a link as shallow as 20 m, an increase in ocean turbulence conditions from weak-to-moderate to moderate-to-strong causes a strong increase in the BER floor from \(10^{-7}\) to \(10^{-4}\). Likewise, for the most favourable turbulence conditions, an increase in the link from 20 to 30 m causes an increase in the BER floor from \(10^{-7}\) to \(10^{-5}\), and a further increase in the depth from 30 to 40 m increases the BER floor to \(10^{-3}\). It is clear from the results in Figure 5 that the underwater link has a huge impact on system performance, as even over short distances there is high degradation. Therefore, the underwater link requires the most attention in the design of the cooperative FSO-TU system. Figure 4: Average bit error rate of the cooperative FSO-TU system as a function of the SINR for different levels of pointing errors and three atmospheric transparency conditions. Weak oceanic turbulence and clear ocean water condition are assumed.
# Influence of Gender Composition in Pedestrian Single-File Experiments
## 3 Results And Analysis
### Comparison of Group Compositions for the Experiments Performed in Germany
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \([\mathbf{0.15},\mathbf{0.25}]\) & \([\mathbf{0.55},\mathbf{0.65}]\) & \([\mathbf{0.85},\mathbf{0.95}]\) & \([\mathbf{1.05},\mathbf{1.15}]\) & \([\mathbf{1.25},\mathbf{1.35}]\) & \([\mathbf{2.05},\mathbf{2.15}]\) & \([\mathbf{3.05},\mathbf{3.15}]\) \\ \hline Female, Germany & \(1.07\pm 0.04\) & \(1.10\pm 0.05\) & \(0.62\pm 0.14\) & \(0.58\pm 0.12\) & \(0.41\pm 0.14\) & \(0.10\pm 0.07\) & \(0.06\pm 0.06\) \\ \hline Male, Germany & \(1.17\pm 0.09\) & \(1.11\pm 0.10\) & \(0.61\pm 0.19\) & \(0.48\pm 0.23\) & \(0.44\pm 0.21\) & \(0.11\pm 0.09\) & \(0.06\pm 0.08\) \\ \hline Gender alternating, Germany & \(1.17\pm 0.16\) & \(1.14\pm 0.15\) & \(0.74\pm 0.24\) & \(0.43\pm 0.22\) & \(0.30\pm 0.19\) & \(0.10\pm 0.07\) & \(0.15\pm 0.06\) \\ \hline Gender random order, Germany & \(1.26\pm 0.14\) & \(1.11\pm 0.07\) & \(0.54\pm 0.30\) & \(0.55\pm 0.15\) & \(0.35\pm 0.16\) & \(0.07\pm 0.07\) & \(0.04\pm 0.06\) \\ \hline Female, Palestine & & & \(1.14\pm 0.01\) & \(0.80\pm 0.11\) & \(0.68\pm 0.07\) & & \\ \hline Male, Palestine & & & \(0.94\pm 0.20\) & \(0.81\pm 0.13\) & \(0.74\pm 0.13\) & & \\ \hline Gender alternating, Palestine & & & \(1.00\pm 0.18\) & \(0.70\pm 0.14\) & \(0.50\pm 0.18\) & \(0.11\pm 0.05\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean values and standard deviation for the velocity \((\overline{v}\pm\sigma)\) in seven different density intervals for different group compositions in Germany and Palestine. Equal colors in an interval indicate equality between the corresponding groups. systematic equality or inequality between different group compositions in the low-density regime. In the density range between 1.25 m\({}^{-1}\) and 1.45 m\({}^{-1}\), the mean values of the homogeneous group compositions are equal to or higher than those of the heterogeneous group compositions. Between 1.45 m\({}^{-1}\) and approximately 2.15 m\({}^{-1}\), the two means are significantly equal for all group compositions except gender random order. For the gender random order group, the velocity is the lowest of all groups. At a density of 2.15 m\({}^{-1}\) and above, there is equality for all group compositions. In addition to Table 3, the boxplots in Figure 4 also illustrate the results of the Tukey test. The left-hand Figure 4a shows the first interval \([0.15,0.25]\) at which there is only equality between male and gender alternating groups, and the right-hand one Figure 4b shows the last interval \([3.05,3.15]\) at which all group compositions are equal. This equality or inequality is indicated by the letters above the boxes. The same letters represent equality between group compositions. In addition, the boxplots show the minimum and maximum values, the median within the box, and the lower and upper quantiles, the boundaries of the box, and those of the individual group compositions. The darker the color of the box, the higher the median.
## 5 Conclusions and Future Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{DPSO} & \(c_{0}\) & \(c_{1}\) & \(c_{2}\) & \(c_{3}\) & \(c_{4}\) & \(c_{5}\) & \(c_{6}\) \\  & 0.507646 & 0.438668 & 1.000000 & 0.308361 & 1.000000 & 0.655705 & 0.970521 \\ \hline \multirow{2}{*}{PSO} & \(c_{0}\) & \(c_{1}\) & \(c_{2}\) & & & \\  & 0.649608 & 0.594950 & 0.175723 & & & \\ \hline \multirow{2}{*}{GA} & \(c_{0}\) & \(c_{1}\) & & & & \\  & 0.899098 & 0.233414 & & & & \\ \hline \multirow{2}{*}{DE} & \(c_{0}\) & \(c_{1}\) & & & & \\  & 0.470144 & 0.544929 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Global best self-tuning parameters for 14 dimensions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **DPSO** & **PSO** & **GA** & **DE** \\ \hline \(f_{0}\) Ave & 7.483\(\times 10^{31}\) & **2.379\(\times 10^{6}\)** & 2.393\(\times 10^{14}\) & **2.127\(\times 10^{2}\)** \\ \hline Std & 0.000 & 4.657\(\times 10^{-10}\) & 9.375\(\times 10^{-2}\) & 8.527\(\times 10^{-14}\) \\ \hline \(f_{1}\) Ave & **7.640\(\times 10^{3}\)** & 1.753\(\times 10^{5}\) & _1.680\(\times 10^{5}\)_ & **3.327\(\times 10^{2}\)** \\ \hline Std & 2.728\(\times 10^{-12}\) & 5.821\(\times 10^{-11}\) & 8.731\(\times 10^{-11}\) & 5.684\(\times 10^{-14}\) \\ \hline \(f_{2}\) Ave & **7.064\(\times 10^{5}\)** & 8.116\(\times 10^{31}\) & 1.452\(\times 10^{24}\) & **2.590\(\times 10^{5}\)** \\ \hline Std & 1.164\(\times 10^{-10}\) & 3.603\(\times 10^{16}\) & 2.684\(\times 10^{8}\) & 0.000 \\ \hline \(f_{3}\) Ave & _2.018\(\times 10^{31}\)_ & **1.638\(\times 10^{26}\)** & 1.943\(\times 10^{32}\) & **2.578\(\times 10^{17}\)** \\ \hline Std & 1.126\(\times 10^{16}\) & 6.872\(\times 10^{10}\) & 3.603\(\times 10^{16}\) & 6.400\(\times 10^{1}\) \\ \hline \(f_{4}\) Ave & 7.842\(\times 10^{26}\) & _1.378\(\times 10^{12}\)_ & **4.856\(\times 10^{10}\)** & **3.923\(\times 10^{11}\)** \\ \hline Std & 2.749\(\times 10^{11}\) & 0.000 & 1.526\(\times 10^{-5}\) & 1.221\(\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fitness w.r.t. each function problem with 14 dimensions and the resulting ranking: _rank 1 (best)_, **rank 2**, _rank 3_, and rank 4.
## 2 Techniques Used for Adaptive Equalization
### Particle Swarm Optimization  
PSO is a population-based stochastic optimization algorithm inspired by social behavior [36]. In the context of adaptive equalization, PSO is utilized to optimize the equalizer's coefficients by iteratively exploring a multidimensional search space [37]. PSO works by simulating the movement of particles, where each particle represents a potential solution. By leveraging the best experiences of the swarm and their own experiences, particles dynamically adjust their positions in the search space to find optimal solutions [38]. PSO provides a global search capability, allowing it to handle complex and nonlinear optimization problems [39]. This makes PSO suitable for adaptive equalization tasks that require optimal filter coefficients and enhanced convergence [39]. The detailed analysis of the PSO algorithm and its variants for adaptive equalization is discussed in later sections.
## 3 Results and Discussion  
### Characterization of the DP Composite  
#### 3.1.1 Microstructure of the Diatomite and DP Composite  
As shown in Figure 2a, the diatomite exhibited a disc-shaped structure with many unconnected pores on its surface, while some of the pores were blocked by impurities. Figure 2b shows the microscopic morphology of diatomite after adsorbing the paraffin, in which the number and size of pores were significantly reduced compared with the pure diatomite. It can be inferred that the pores were filled up by paraffin. The relative pressure (P/P0) range of 0.4-0.8, as shown in Figure 3a, can be attributed to the capillary condensation during mesoporous adsorption, which shows that the majority of pores were mesopores [37]. Besides this, it can be seen from Figure 3b that the pore size of diatomite was generally below 220 nm, and the macropores (larger than 50 nm), mesopores (2-50 nm) and micropores occupied 17.26%, 48.66% and 34.08% of the total volume, respectively. Due to the significant amounts of mesopores and micropores, it can be stated that the absorption capacity of diatomite is high enough to store paraffin, as these pore structures have a high surface tension and capillary force.  
Figure 2: SEM images of (**a**) diatomite, and (**b**) diatomite/paraffin.  
Figure 3: N\({}_{2}\) adsorption–desorption isotherm of diatomic: (**a**) isotherm linear plot, and (**b**) pore size distribution and cumulative pore volume.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 3 Multi-Attention Video Description Model
### Attention Calculation
#### 3.2.2 Channel Attention  
Through the space attention, we obtain new video features: \(X_{att-s}^{(t)}=\{v_{1-s},\ v_{2-s},\ \ldots v_{n-s}\}\). We will convert \(X_{att-s}^{(t)}\) to \(U=\{u_{i},\ldots u_{c}\}\), where \(u_{i}\in R^{K*K*N}\) and \(C\) denotes the number of feature channels. Then we can adopt the average pooling operation and obtain the channel feature vector of the video: \(c=[c_{1},\ldots,c_{i}],\ c\in R^{C}\), where the scalar \(c_{i}\) represents the mean value of the vector \(u_{i}\), which denotes the channel feature value. The attention can be calculated by Equations (13)-(16):  
\[\mathbf{b}=\tan\ln((W_{att-c}\otimes c+b_{c})\oplus W_{hc}h_{t-1}) \tag{13}\]  
\[\beta=\text{softmax}\Big{(}W^{\prime}\mathbf{b}+b_{i}^{\prime}\Big{)} \tag{14}\]  
where \(\otimes\) is the symbol of the outer product and \(\oplus\) denotes the addition of matrices and vectors. The dimension of \(\beta\) is \(C\) and we apply \(\beta\) on \(v_{i-s}\):  
\[v_{i-c}=v_{i-s}*\beta \tag{15}\]  
\[X_{att-c}^{(t)}=f_{att-c}(X_{att-s},\beta^{(t)}\Big{)} \tag{16}\]  
\(f_{att-c}\) indicates applying the channel attention operation on all \(v_{i-s}\), as in Equations (13)-(15) and we obtain the video features after channel attention operation: \(X_{att-c}^{(t)}=\{v_{1-c},v_{2-c}\ldots v_{n-c}\}\).
## 6 Conclusions
In 2016, NOAA Office of Coast Survey issued provisional charts for the Yukon River, relying solely on satellite images to create the electronic navigational charts that only display shoreline and shoals [47]. The usage of the EO techniques outlined in this study and proposed by CHS in 2014 [12] to update dynamic systems is a cost-effective technique and creates up-to-date products. Although the use of multibeam data is still the most accurate approach, this technique is not cost efficient for the Mackenzie River since it is a highly dynamic system. Thus, the advantages of having centimeter accurate survey data is lost by the rapidly changing water depths. Therefore, approaches based on the use of EO techniques and crowdsourced information that are less accurate than traditional techniques can provide a better representation of the river when performed more frequently. Conceptualization, methodology, supervision, project administration and funding acquisition, R.C. ; formal analysis, investigation and validation, K.O., R.A. and M.S. ; Writing--original draft preparation, R.C., K.O., R.A., and M.S. ; and writing--review & editing, R.C., K.O., R.A., and M.S. To complete this work The CHS received support through the Canadian Space Agency's Government Related Initiatives Program (GRIP) and the Data Utilization Application Plan (DUAP). **Acknowledgments:** Thank you to [PERSON] for her help in the review and editing of the article. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Question Difficulty Estimation Based on Attention Model for Question Answering
## 4 Experiments
### Performance Change according to Data Ratio  
The proposed model is based on the transformer encoder designed to consider the relationships among all components in a question. It is known that a number of training examples are required to train the transformer encoder. Thus, one possible question about the proposed model is whether the training data in QA data sets are sufficient enough to train it. Since the proposed model adopts a pre-trained language model, BERT-Base, and fine-tunes it, it does not require too many training examples actually. This is proved empirically by showing the performance change according to the ratio of data used to train the proposed model.  
Figure 6 depicts the performance changes, where the X-axis is the ratio of data used to train the proposed model and the Y-axis represents F1-score. In all QA data sets, the more the training data are used, the better the predictions of the proposed model are. In QuAC and RACE, the performances of the proposed model converge after 90% data are consumed. This implies that the proposed model is trained well for the data sets. However, a different phenomenon is observed in DramaQA data set with which the performance increases continually. This continual increase is believed to be affected by a larger number of information components in DramaQA. The number of information components in DramaQA is three, while it is two in other data sets. In addition, the F1-score is around 90 when 100% of data are used to train the proposed model. Thus, even if more data are provided, the improvement by them would not be great.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**QA Model** & **Diff. 1** & **Diff. 2** & **Diff. 3** & **Diff. 4** & **Overall** & **Diff. Avg.** \\ \hline Multi-level context matching model [9] & 75.96 & 74.65 & 57.36 & 56.63 & 71.14 & 66.15 \\ \hline + Difficulty level & 76.12 & 74.82 & 59.12 & 57.33 & 73.83 & 66.85 \\ \hline \end{tabular}
\end{table}
Table 6: Accuracy of question answering with the question difficulty estimation.
# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 1 Introduction  
Face keypoint detection is a vital task in computer vision, aimed at locating specific keypoints on the face both effectively and efficiently. It is essential for various applications, including face recognition, expression analysis, and 3D face reconstruction, and holds significant potential for broader applications. Current methods for face keypoint detection fall into two main categories: traditional manual feature extraction techniques and more recent deep learning-based approaches. The prevailing research trend works on the end-to-end regression-based deep learning methods. To enhance efficiency, some network compression algorithms such as model pruning are commonly used. However, existing methods typically apply task-independent pruning techniques to improve detection efficiency.  
Despite their effectiveness in general conditions, current face keypoint detection methods struggle with challenges such as large angles, occlusion, side views, partial occlusion, uneven lighting, and drastic expression changes. In such scenarios, the detection results become unstable. Efficiency is also a concern, as the precision of pruning-based model compression typically decreases with higher pruning ratios, making it difficult to balance efficiency and accuracy. Regression-based deep learning methods rely on the face's inherent global template and lack robustness in extreme cases. Inspired by the heatmap approach used in human keypoint detection, we propose to enhance robustness by focusing on local apparent features. To address the diminishing learning ability due to pruning, external supervision signals, such as those provided by a teacher model through knowledge distillation, can be introduced. This approach aims to maintain accuracy while benefiting from combining pruning and distillation.  
This paper proposes an effective, efficient, and lightweight method for face keypoint detection to address issues of unstable detection and accuracy loss in extreme conditions.  
Figure 1 illustrates the model's approach. The approach combines regression with a heatmap for joint training, leveraging both global and local features for joint optimization. Additionally, we introduce a method that combines pruning and distillation. Specifically, after each pruning step, a distillation model is used for fine-tuning to maximize retention of pre-pruning detection performance and minimize accuracy loss. Extensive experimental results demonstrate that integrating a heatmap and regression improves accuracy, while the combination of distillation and pruning maintains performance with minimal accuracy loss. In Section 2, we review effective and efficient methods related to face keypoint detection. Section 3 presents our proposed method for combining distillation with pruning and regression with a heatmap. Section 4 details the experimental results, and Section 5 provides a summary and conclusions.