esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Attention Network for Human Pose Estimation
## 4 Experiment
### Experiment on 2D Pose of MPII and COCO
#### 4.3.2 Qualitative Results  
With the help of joint learning framework, our approach outputs both 2D and 3D pose from images in the wild at the same time. We visualized example 2D prediction results in Figure 6. We can see that our method is robust in extremely difficult cases. The proposed SAN presents a better performance and can be generalized to unlimited images. This also shows that the impact of rich 2D annotated data will increase 3D performance dramatically. Our approach is helpful.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Head** & **Shoulder** & **Elbow** & **Wrist** & **Hip** & **Knee** & **Ankle** & **Average** \\ \hline SimpleBaseline-152 [35] & 97.0 & 95.9 & 90.3 & 85.0 & 89.2 & 85.3 & 81.3 & 89.6 \\ DLCM [36] & 95.6 & 95.9 & 90.7 & 86.5 & 89.9 & 86.6 & 82.5 & 89.8 \\ StackedHourglass [12] & 98.2 & 96.3 & 91.2 & 87.1 & 90.1 & 87.4 & 83.6 & 90.0 \\ PoseNFS [37] & 97.9 & 95.6 & 90.7 & 86.5 & 89.8 & 86.0 & 81.5 & 90.2 \\ HRNet-W32 [38] & 97.1 & 95.9 & 90.3 & 86.4 & 89.1 & 87.1 & 83.3 & 90.3 \\ CA+SA [39] & 97.1 & 96.0 & 90.7 & 86.4 & 89.4 & 86.8 & 83.3 & 90.4 \\ PRA [39] & 97.1 & 96.2 & 90.7 & 86.4 & 89.8 & 86.9 & 83.3 & 90.5 \\ SkeletalHeatmap [40] & - & - & - & - & - & - & - & 90.6 \\ CU-Net [41] & 97.4 & 96.2 & 91.8 & 87.3 & 90.0 & 87.0 & 83.3 & 90.8 \\ DU-Net [42] & 97.6 & 96.4 & 91.1 & 87.3 & 90.4 & 87.3 & 83.8 & 91.0 \\ KnowledgeGuided [43] & 98.1 & 96.3 & 92.2 & 87.8 & 90.6 & 87.6 & 82.7 & 91.2 \\ MultiContext [13] & 98.5 & 96.3 & 91.9 & 88.1 & 90.6 & 88.0 & 85.0 & 91.5 \\ \hline
**Ours** & **98.7** & **96.6** & **92.4** & **88.2** & **91.1** & **88.9** & 84.8 & **91.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of [EMAIL_ADDRESS] (%) on MPII. It reports the percentage of detections that fall within a normalized distance of ground truth. Higher values are better, with the best being indicated by bold font, and the second best being underlined.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AP** & **AP\({}^{50}\)** & **AP\({}^{75}\)** & **AP\({}^{M}\)** & **AP\({}^{L}\)** \\ \hline Integral-H1 [7] & 66.3 & 88.4 & 74.6 & 62.9 & 72.1 \\ Integral-I1 [7] & 67.8 & 88.2 & 74.8 & 63.9 & 74.0 \\ EfficientPose-B [44] & 70.5 & 91.1 & 79.0 & 67.3 & 76.2 \\ EfficientPose-C [44] & 70.9 & 91.3 & 79.4 & **67.7** & 76.5 \\ PoseNFS-MobileNet [37] & 67.4 & 89.0 & 73.7 & 63.3 & 74.3 \\ PoseNFS-ResNet [37] & 70.9 & 90.4 & 77.7 & 66.7 & **78.2** \\ \hline
**Ours** & **71.8** & **91.5** & **79.8** & 66.9 & 78.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results on COCO test-dev. Higher values are better, with the best being indicated by bold font, and the second best being underlined.
## 3 The Proposed CNN-SNN Conversion Method
### CNN Training for SNN Conversion  
The proposed method takes a CNN-SNN conversion approach to train an SNN model. Hence, we first organize a CNN model architecture that corresponds to an SNN model of interest. For classification tasks, CNN models usually consist of multiple convolutional layers and a few fully connected layers. The performance of the trained CNN models strongly affects that of the converted corresponding SNNs. It is hence important to make the CNN models achieve high performance early on.  
The neurons of CNN models for CNN-SNN conversion are traditional artificial neurons with ReLU activation. ReLU is chosen as the activation function because the firing rate of soft-reset IF neurons without a refractory period can be approximated by the ReLU nonlinearity [24; 25; 26]. The bias terms of neurons in CNN models are set to 0 for smooth conversion from a CNN model to an SNN model. CNN models may make use of pooling operations to reduce the output feature maps of convolutional layers. Max-pooling and average pooling [1] have been widely used in CNNs. Since the neuron activations in an SNN are binary values at each time step, the max-pooling operation would cause significant information loss in the subsequent layers. Consequently, average pooling is used, if needed, for CNN-SNN conversion.  
The CNN models are trained with conventional optimizers such as Adam, where such regularization techniques as dropout [34] can be used to mitigate overfitting on the convolutional layers. The dropout for convolutional layers is the spatial dropout, which randomly drops some channels of the output feature map by setting the elements of the selected channels to zero.
## 3 WSN Protocols
### Assessment of the Characteristics of the Protocols Under Consideration  
Regarding the simulations of the original protocols, we noticed that many limitations appeared. LEACH cluster heads are selected randomly, which can result in uneven energy distribution. Some cluster heads may be located far from the base station (BS), leading to higher energy consumption for data transmission. Some CHs may have no connected nodes, while others may have too many, causing imbalanced energy usage. Cluster heads overlap in coverage, leading to redundant data transmission and wasted energy. LEACH struggles with large-scale networks due to its reliance on random CH selection and single-hop communication. SEP introduces advanced and normal nodes but does not dynamically adjust CH selection based on real-time energy levels, leading to the premature death of high-energy nodes. Cluster heads are selected based on initial energy levels, which can result in inefficient clustering as the network evolves. Some CHs may die early, leaving their clusters disconnected and reducing network reliability. TEEN relies on hard and soft thresholds for data transmission, which can result in data loss if thresholds are not met. Cluster heads may be located at the network's edges or far from the BS, increasing energy consumption and reducing communication reliability. TEEN is designed for event-driven networks and is unsuitable for periodic data transmission applications. DEC uses a deterministic approach for CH selection, which does not adapt to node energy levels or network topology changes. Nodes with high energy consumption will die early, reducing the network's longevity. The limitations of the existing protocols (LEACH, SEP, TEEN,  
Figure 6: Results of implementing the standard DEC protocol using MATLAB: cluster heads are marked as bold plus signs, other nodes are marked as coloured rhombuses, and the base station is marked as a red circle in the centre.  
and DEC) highlight the challenges in energy efficiency, clustering, and network lifetime in Wireless Sensor Networks (WSNs). These limitations justify the need for the proposed KNN-based modifications.
# Effect of Coupled Mechanical-Chemical Activation on Hydration Activity of Copper Slag Powder
## 3 Results and Discussion
### Mechanical Properties
#### 3.2.2 Effect of Coupled Mechanical-Chemical Activation on Hydration Activity of Copper Slag Powder  
In order to further improve the hydration activity of the copper slag powder, CaO was applied as a chemical activator on the basis of mechanical grinding to investigate the effect of mechanical-chemical coupling activation on the hydration activity of the copper slag powder.  
Figure 4 depicts the effect of coupled mechanical-chemical activation on the hydration activity of the copper slag powder. It can be seen from Figure 4a that the compressive strength of the copper slag powder composite cement-hardened paste decreased with the increase in copper slag powder content, and increased with the development of age. When the content of copper slag was the same, the compressive strength of CA series samples at each age was higher than that of CS series hardened paste, indicating that the chemical activator CaO further stimulated the hydration activation of the copper slag and promoted the development of the compressive strength of the copper slag powder-based composite cement at each age.  
Similar to Figure 3b, Figure 4b shows that the compressive strength activity index of 28 d of age was higher than that of 3 d of age, and the compressive strength activity index of 7 d of age was the lowest. For example, the compressive strength activity indexes of CS20-90 at 3, 7, and 28 d were 83.8%, 79.62%, and 85.92%, respectively. The compressive strength activity index of CS40-90 at 3, 7, and 28 d were 60.01%, 54.48%, and 71.12%, respectively. It indicates that the addition of copper slag powder reduced the compressive strength of the composite cement-hardened paste. The reason is that copper slag powder can be regarded as an inert admixture at the early stage of hydration and does not participate in the hydration reaction. The higher the content of copper slag powder, the lower the cement content involved in hydration in the paste, resulting in a large decrease in the compressive strength of the sample.
###### Abstract  
Accurately assessing the carbon sink intensity of China's ecosystem is crucial for achieving carbon neutrality. However, existing ecosystem process models have significant uncertainties in net ecosystem productivity (NEP) estimates due to the lack of or insufficient description of phenological regulation. Although plant developmental factors have been proven to significantly influence autumn phenology, they have not been systematically incorporated into autumn phenology models. In this study, we modified the autumn phenology model (cold-degree-day, CDD) by incorporating the growing-season gross primary productivity (GPP) and the start of growing season (SOS) and used it as a constraint to improve the CASA model for quantifying NEP across China from 2003 to 2021. Validation results showed that the CDD model incorporating developmental factors significantly improved the simulation accuracy at the end of the growing season (EOS). More importantly, compared with flux tower observations, the NEP derived from the improved CASA model based on the above phenology model showed a 15.34% reduction in root mean square error and a 74% increase in the coefficient of determination relative to the original model. During the study period, China's multiyear average total NEP was 489.67 \(\pm\) 38.27 Tg C/yr, with the highest found in evergreen broadleaf forests and the lowest detected in shrublands. Temporally, China's NEP demonstrated an overall increasing trend with an average rate of 1.75 g C/m\({}^{2}\)/yr\({}^{2}\). However, the growth rate of NEP remained far below concurrent carbon emissions from fossil fuel combustion totally, especially for eastern China, while the northeastern regions performed relatively better. The improved regional carbon flux estimation framework proposed in this study will provide important support for developing future climate change mitigation strategies.  
autumn phenology; carbon sink; light use efficiency; net ecosystem productivity; phenology model +
Footnote †: journal: Remote Sensing
## 4 Study Area and Data Selection
### Input Data  
The hybrid activation function was proposed to forecast the water surface elevation, which is an input data with time series characteristics in this study. The purpose of this study was to evaluate the performance of the forecasting model based on the use of the hybrid activation function. Therefore, the type of input data, quantity, and correlation of input data to be used for forecasting the water surface elevation were considered. [PERSON] et al. (2018) have investigated the accuracy of forecasting according to a combination of input datasets in Hangang River. They found that the combination of precipitation, outlet discharge of dam, water surface elevation and tidal level showed a good performance of forecasting water surface elevation in view of forecasting accuracy. Therefore, we also applied the same input data as that previous study to forecast the water surface elevation in Hangang River. Therefore, the input data used for forecasting the water surface elevation are precipitation (1) in Figure 2), outlet discharge of Paldang dam (2) in Figure 2), the water surface elevation of Hangang River bridge (3) in Figure 2), the tidal level of Incheon (4) in Figure 2), wind speed (1) in Figure 2) and the concentration of dissolved oxygen (3) in Figure 2). The observed data for 10 years (from 2009 to 2018) on an hourly basis from the Korea meteorological administration, the Hangang River flood control center, and the Korea hydrographic and oceanographic agency, Water Environment Information System, respectively, were used. The location of the station and the retention periods of the collected data in this study are presented in Table 1 and the units of observed data are as follows. The unit of precipitation is millimeters, the unit of outlet discharge is cubic meters per second, the unit of water surface elevation is meters, the unit of tidal level is centimeters, the unit of wind speed is meters per second, and the concentration of dissolved oxygen is milligrams per litter.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Stations** & **Items \({}^{*}\)** & **Latitude** & **Longitude** & **Period** \\ \hline
1 & Seoul & P & \(37^{\prime}34^{\prime}\) & \(126^{\prime}57^{\prime}\) & \\
2 & Paldang & O & \(37^{\prime}31^{\prime}\) & \(127^{\prime}17^{\prime}\) & \\
3 & Hangang Bridge & E & \(37^{\prime}30^{\prime}\) & \(126^{\prime}57^{\prime}\) & 10 years \\
4 & Incheon & T & \(37^{\prime}27^{\prime}\) & \(126^{\prime}35^{\prime}\) & (2009–2018) \\
5 & Seoul & W & \(37^{\prime}34^{\prime}\) & \(126^{\prime}57^{\prime}\) & \\
6 & Hangang Bridge & D & \(37^{\prime}30^{\prime}\) & \(126^{\prime}57^{\prime}\) & \\ \hline \hline \end{tabular} \({}^{*}\) P = Precipitation; O = Outlet Discharge; E = Water surface elevation; T = Tidal level; W = Wind speed; D = Concentration of Dissolved Oxygen.  
\end{table}
Table 1: Information about hydrological stations.  
Figure 2: Study area (Hangang River).
# Effects of the Fruit Harvest Date and Shelf-Life Nexus of Apples on Different Quality Perspectives
## 1 Introduction
[21] initially presented Temporal Dominance of Sensations (TDS) as a sensory technique that examines a product's dominant feeling sequence throughout a specified duration of time [22]. Whenever human subjects notice a shift in their dominating sensations, they can choose a new dominant trait. According to some definitions [22; 23], a 'dominant' feeling is one that draws attention, has the most striking perception, or emerges suddenly. It does not need to be the most intense sensation. Long-term training is not necessary for TDS judges, as TDS can assess multiple qualities at once [24]. Mastication and salivation cause sensory perceptions of food and drink that alter dynamically throughout their intake [22; 25; 26]. Thus, during food consumption, alterations in the evaluation of these dynamic sensory perceptions may cause a variety of food-evoked emotions. According to certain theories, self-report measures only show the emotions that a person becomes aware of (explicit measurements), hiding certain aspects of the intricate emotion process in other subsystems [27; 28; 29; 30; 31]. Conversely, implicit measurements, such as facial expressions, may reveal more details about rapidly shifting moods when consuming food. Previous research using multiple bite assessments to measure dynamic changes in sensory perceptions has shown that various food components interact dynamically during consumption, causing a perceptual shift in sensory characteristics from bite to bite [32]. As a result, several bite intakes affect the temporal dynamics of sensory perceptions, which may lead to a shift in hedonic and emotional assessments, both within and between bites [33]. To explore this possibility, this study captured facial expressions while panelists consumed apple samples. Therefore, the main objective of this study comprised employing different quality perspectives in analyzing the physical and sensory characteristics of two apple varieties at different harvest dates over a 15-day shelf life period. The two main working hypotheses outlined in this study were: (i) different sensory sensations are associated with both harvest date and shelf life; (ii) emotions change depending on both the harvest date and shelf life. The expected outcomes were that there would be a recognizable pattern in sensory and emotional changes depending on harvest date and shelf life.
# Design of a Modified Compact Coupler with Unwanted Harmonics Suppression for L-Band Applications
## 2 Design Procedure
### Vertical Resonator Design  
The vertical resonators can be designed similar to horizontal resonators. The initial vertical resonator is designed based on the horizontal one. The layout and frequency responses of the initial vertical resonator are shown in Figure 9a,b.  
Figure 8: Final horizontal resonator: (**a**) layout and (**b**) frequency response. All dimensions are in the millimeter unit.  
Figure 9: Initial vertical resonator: (**a**) layout and (**b**) frequency response. All dimensions are in the millimeter unit.  
To improve the frequency response, the layout of initial vertical resonator is improved, which the improved initial vertical resonator is obtained by adding a stepped impedance resonator. The layout and frequency response of the improved initial vertical resonator is illustrated in Figure 10a,b.  
The final vertical resonator can be obtained by adding another stepped impedance resonator to the improved initial vertical resonator. The layout and frequency response of the final vertical resonator is shown in Figure 11.
# DSCope: Development of Automatic Program for Detecting Fractures and Measuring Dip Angles
## 3 Application to the Field Drilling Cores
### Comparison with Visual Analysis  
The red and blue indicate the settings of 4.5\(\sigma\) and 5.0\(\sigma\), respectively. The bars indicate the distribution of the B-threshold, and the dashed line indicates the Gaussian profile fitting of each distribution. When the \(\sigma\) value increases, a lower B-threshold is required because the drilling core fractures become weaker. Nevertheless, neither the histogram (bar in Figure 8) nor the Gaussian profile fitting (dashed line in Figure 8) showed significant differences.  
The averages obtained by Gaussian profile fitting for the B-thresholds of 4.5\(\sigma\) and 5.0\(\sigma\) were 7.101% and 6.126%, indicated by the red and blue letters, respectively, in Figure 8. The standard deviations were 0.003680 and 0.002922, respectively. These values were tested for statistical significance at a confidence level of 95% (Z = 1.960) using the Z-test as presented in Equation (3). The Z-test value was 1.438, which was smaller than 1.960, indicating that the two groups did not have a statistically significant difference:  
\[Z=\frac{\overline{x_{1}}-\overline{x_{2}}}{\sqrt{\frac{s_{1}^{2}}{n_{1}}+ \frac{s_{2}^{2}}{n_{2}}}} \tag{3}\]  
In addition, the effect of the \(\sigma\) value on the result was verified once again using a scatter plot and Pearson correlation. Because the corresponding relation could not be recognized by the average and standard deviation, the actual linear correlation was established and examined. Figure 9 shows the results, where the \(x\)-axis and \(y\)-axis represent B-thresholds of 4.5\(\sigma\), and 5.0\(\sigma\), respectively. The solid line corresponds to where both the B-thresholds were identical, and the dashed lines indicate values \(\pm\)0.002. The color of the dot represents the discrepancy between the two B-thresholds. If the color is closer to black, it means the B-threshold of 4.5\(\sigma\) had a larger value. Conversely, if the color is closer to white, it means that the B-threshold of 5.0\(\sigma\) had a larger value. It can be seen that most dots are located above the solid line or within the range of the dashed lines. In the additional analysis, the Pearson correlation was found to be approximately 0.87, indicating that the two values had high correlation.  
Figure 8: Histogram of the bottom thresholds at two different sigma values.
## 3 Experiments and Results
### Scenario 2: Demographics and Academic Features  
The second scenario includes demographics and academic features. Performance of the obtained prediction models is reported in Table 7. The best performance by the end of term 1, is the LR method with the term performance represented _by courses_, whether single or cumulative, with 64.4%. When the prediction is performed later, we observe similar results with the scenario 1, i.e., the best performance is mainly obtained with the LR method, with the term performance represented _by cumulative GPA_. Please note that GNB shows a slightly superior performance for the term 4, and the second best for the terms 5 and 6. Looking at Figure 5(b),d, we observe that the performance of cumulative models are improving from 64.4% (term 1) to 94.9% (term 6) with decreasing prediction window size. Again, same as for the scenario 1, the models using only one past term reach a performance pick mostly when the prediction is performed by the end of term 4 or term 5 (see Figure 5(a),c).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **ML Algoth** & **1 st Term** & **2 nd Term** & **3 rd Term** & **4 th Term** & **5 th Term** & **6 th Term** \\ \hline \multirow{4}{*}{**Single**} & \multirow{4}{*}{**Course**} & LR & **0.644** & 0.670 & 0.743 & 0.816 & 0.815 & 0.798 \\ \cline{3-8}  & & RFC & 0.624 & 0.614 & 0.677 & 0.788 & 0.773 & 0.774 \\ \cline{2-8}  & & GNB & 0.556 & 0.640 & 0.746 & 0.822 & 0.805 & 0.789 \\ \cline{2-8}  & & LR & 0.614 & 0.652 & 0.734 & 0.820 & 0.827 & 0.820 \\ \cline{2-8}  & & RFC & 0.597 & 0.634 & 0.689 & 0.776 & 0.783 & 0.764 \\ \cline{2-8}  & & GNB & 0.554 & 0.624 & 0.741 & 0.812 & 0.829 & 0.796 \\ \hline \multirow{4}{*}{**Cumulative**} & \multirow{4}{*}{**Course**} & LR & **0.644** & 0.695 & 0.781 & 0.855 & **0.921** & 0.937 \\ \cline{2-8}  & & RFC & 0.624 & 0.659 & 0.758 & 0.843 & 0.895 & 0.912 \\ \cline{2-8}  & & GNB & 0.557 & 0.654 & 0.744 & 0.803 & 0.860 & 0.894 \\ \cline{2-8}  & & LR & 0.614 & 0.693 & **0.798** & **0.866** & **0.916** & **0.949** \\ \cline{2-8}  & & RFC & 0.597 & 0.660 & 0.754 & 0.857 & 0.905 & 0.925 \\ \cline{2-8}  & & GNB & 0.554 & **0.699** & 0.778 & 0.840 & 0.897 & 0.931 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy results of the ML Models with the academic and demographic features.  
Figure 5: Accuracy plots along the terms of the ML Models only with the academic features.