esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The results indicated that phenology-based \(\varepsilon_{0}\) is more appropriate for GPP estimation in the paddy field ecosystem. In another relevant study, [PERSON] et al. (2022) [11] found seasonal variations of \(\varepsilon_{0}\) in agroecosystems and re-parameterized \(\varepsilon_{0}\) in the green-up and senescence stages to provide high-accuracy GPP estimates. These studies have demonstrated that phenology-based \(\varepsilon_{0}\) performs better than static or constant \(\varepsilon_{0}\) in GPP estimation because it is more consistent with phenology variations in the vegetation. In terms of the global terrestrial ecosystem, phenological stages diverge as a result of the changes in photosynthesis efficiency and rate caused by plant physiological characteristics [10; 11], and they are appropriated for estimating \(\varepsilon_{0}\) in vegetation phenological stages. In order to evaluate the improvement of phenology-based \(\varepsilon_{0}\) in GPP estimates across different terrestrial ecosystems, the extraction of identical phenology indicators is a key step. The phenological stage indices, including the start (SOS), end (EOS), and length (LOS) of the growing season, have been successfully derived from remotely sensed imagery or ground observations with various algorithms [18]. Recent studies have showed that extraction algorithms such as the hybrid generalized additive model (HGAM) and double logistic function (DLF) are robust and appropriate for estimating SOS/EOS/LOS from satellite normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and in situ GPP observations [19; 20; 21]. Moreover, automated digital camera imagery collected through the Phenology Camera (PhenoCam) observation network can provide 'near-surface' observations of vegetation phenology with high temporal resolution [22; 23]. These PhenoCam images have been successfully used to evaluate satellite-derived phenology [24; 25; 26] and test phenology algorithms in diverse ecosystems [27; 28; 29]. Therefore, a thorough evaluation of the implications of phenology extraction algorithms on phenology-based \(\varepsilon_{0}\) and a comprehensive comparison of phenology-based \(\varepsilon_{0}\) across different ecosystems are required. In an effort to address the issue of the current \(\varepsilon_{0}\) estimation approach not fully taking into account the identical phenological stages of various PFTs, we proposed a framework for phenology-based regulation of \(\varepsilon_{0}\) and made a comparison of GPP estimates from phenology-based \(\varepsilon_{0}\). Specifically, the objectives of the study were to: (1) compare phenology-based \(\varepsilon_{0}\) estimated from NDVI, EVI, and camera-derived greenness index based on two well-known algorithms (HGAM and DLF) among forest, grassland, cropland, and wetland ecosystems and (2) assess the degree of improvement in GPP estimation by use of phenology-based \(\varepsilon_{0}\) in the LUE model.
## 2 Materials and Methods
### WRPM: Weather-Robust Preprocessing Module
#### 2.4.3 Film  
To make full use of the two paths of features extracted from the FFA-Net and LS-UNet paths, we designed the FIM. This module adopts a multilevel attention mechanism to adaptively fuse and weight-fuse feature information at the spatial, channel, and pixel levels. In this way, the FIM can effectively integrate feature information of hazy regions with different concentrations. It ensures the overall clarity of the image and avoids the possible local detail loss or information redundancy that may occur when processing with a single path.  
The most significant innovation of the FIM is the design of a multilevel attention mechanism. In this module, we redefined the channel attention and pixel attention. Moreover, according to the characteristics of streetscape data, we introduced the spatial attention mechanism to learn the spatial dimension information of the city.  
Channel attention \(CA_{\text{film}}\) weights each channel through global context information. It assigns weights to different channels according to their importance, enhancing the global consistency and semantic information of the feature representation and improving the feature capture ability of channels. The processing of input features by it is reflected in Equation (6).  
\[CA_{\text{film}}=\text{Conv}(\delta(\text{Conv}(\gamma(x)))) \tag{6}\]  
Spatial attention \(SA_{\text{film}}\) captures important spatial positions in the image by calculating the channel average and maximum values. It adjusts the feature weights of each position according to the spatial distribution of the image, enhancing the feature representation of the haze-covered areas and improving the ability of model to recognize haze. The processing of input features by it is reflected in Equation (7). Here, \(\beta\) represents the max-pooling operation, and \(Concat\) represents the channel concatenation operation.  
\[SA_{\text{film}}=\text{Conv}(\text{Concat}(\beta(x),\gamma(x))) \tag{7}\]  
Pixel attention \(PA_{\text{film}}\) combines spatial and channel information to refine processing at the pixel level. It enhances image details and edge features by refining each pixel position, resulting in a clearer and more natural dehazed image. Equation (8) demonstrates the processing of input features by the pixel attention (\(PA_{\text{film}}\)) module. Here, \(\sigma\) represents the sigmoid activation function, the unsqueeze operation is used to add a dimension, and rearrange denotes the tensor dimension rearrangement operation, which is used to restore the original dimensions.  
\[PA_{\text{film}}=\sigma(\text{Conv}(\text{Rearrange}([\text{Unsqueeze}(x), \text{Unsqueeze}(y)]))) \tag{8}\]
## 3 Results and Discussion  
### Crystal Structure, Surface Morphology, and Magnetic Properties of HWWC  
Figure 1b shows the XRD patterns of the prepared HWWC. The strongest peak observed at approximately 20 = 35.58\({}^{\circ}\) indicated a reduction in the (119) diffraction of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)[28]. The peaks observed at approximately 30.08\({}^{\circ}\), 43.16\({}^{\circ}\), 57.16\({}^{\circ}\), and 62.80\({}^{\circ}\) corresponding to (205), (0012), (1115), and (4012) were also in good agreement with those of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)[28; 29]. Other impurity peaks at approximately 33.30\({}^{\circ}\), 54.10\({}^{\circ}\), and 63.40\({}^{\circ}\) agreed with \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)[30]. These results indicated that the magnetically separated HWWC was a mixture mainly consisting of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) and \(\alpha\)-Fe\({}_{2}\)O.  
Figure 2 shows the SEM images and EDS spectra of the HWWC particles. The morphology of the particles was approximately spherical (Figure 2a). Based on the elemental analysis (O = 32.23%, Fe = 66.98%) (Figure 2b), it was determined that the sample mainly consisted of Fe and O, therefore confirming that the produced powder was Fe\({}_{2}\)O\({}_{3}\), which agreed with the XRD results.  
Figure 3 shows the VSM analysis result of the HWWC particles. The saturation magnetization of the HWWC was determined to be 34.14 emu/g, which was sufficient (>16.3 emu/g) for it to be magnetically recovered from solution using a conventional magnet [31; 32]. Thus, HWWC can be easily recovered from water through magnetic separation and reused.  
Figure 1: (a) pH\({}_{\rm{pzc}}\) measurement and (b) X-ray diffraction pattern of prepared hand-warmer waste catalyst.
## 2 Materials and Methods
### Experimental Protocol
#### 2.3.5 Global Positioning System and Accelerometer Device (GPS)  
To determine the physical demands, all players were equipped with a 10 Hz portable GPS (Viper pod 2, STATSports, Belfast, Northern Ireland), which was attached to a lycravest between the shoulders. Each field player carried individual units during matches and used the same unit in both matches to reduce measurement error [40]. The duration and frequency of movement activities were quantified with respect to the percentage of time and meters traveled by the players based on six activity zones bounded by various speeds [41]: walking (0-6.0 km/h); low-intensity running (6.1-12.0 km/h); medium-intensity running (12.1-14.0 km/h); high-intensity running (14.1-18.0 km/h); very high-intensity running (18.1-25.0 km/h); sprinting (>25.1 km/h). Except for the total distance, all variables analyzed were relativized per minute of match play.
## 4 Results and Discussions
### System Identification Analysis
#### 4.2.2 Testing Setting Evaluation  
In this section, the parameters calculated in the training setting for the models 8-NARX and 3-ANFIS are utilized to estimate the behavior of the building equipped with TMD-2.39 controller system (Figures 3 and 6b). The performances of two models are evaluated in Table 5. Figure 10 displays the observed and identified behavior of the controlled structure using by two above-mentioned models.  
The models' performance evaluation shows that the two models can be used for the detection of the behavior of structures equipped with passive TMD controller systems. The \(R^{2}\) is obtained as closed values for two models while model 8-NARX works more effective. In addition, it can be shown that the MAE and RMSE values for the model 8-NARX are decreased by 50% approximately relatively to the model 3-ANFIS. However, the model 8-NARX can be used in our case to detect the behavior of the controlled structure. Moreover, Figure 10 demonstrates that the two predicting models results are close to the observations of acceleration response. A high correlation is observed with best fitting required that is shown with model 8-NARX. Therefore, it can be concluded that these two models are acceptable models to predict the behavior of structure in our case, and model 8-NARX seems to be more effective at identifying the behavior of the structure equipped with the TMD controller system. Herein, it should be mentioned that this study is an experimental study; therefore, to utilize these models with full scale case, the full scale monitoring and evaluation of these models should be studied.  
Figure 10: TMD-2.39 testing the capability of response prediction by two models: (**a**) model 8-NARX; and (**b**) model 3-ANFIS models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(R^{2}\) & **MAE (g)** & **RMSE (g)** \\ \hline Model 8-NARX & 0.98 & \(2.55\times 10^{-3}\) & \(3.68\times 10^{-3}\) \\ Model 3-ANFIS & 0.96 & \(4.79\times 10^{-3}\) & \(6.78\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Testing models performance evaluation.
# Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System
## 4 Experimental Setup
### Comparison with PredRNN Baseline  
To provide a comparative baseline, we also trained PredRNN [18], a well-established recurrent neural network for spatiotemporal predictive learning, on the same dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **in\_channels** & **out\_channels** & **kernel\_size** & **stride** & **padding** \\ \hline Layer 1 & 3 & 16 & 3 & 2 & 1 \\ \hline Layer 2 & 16 & 32 & 3 & 2 & 1 \\ \hline Layer 3 & 32 & 64 & 3 & 2 & 1 \\ \hline Layer 4 & 64 & 128 & 3 & 2 & 1 \\ \hline Layer 5 & 128 & 256 & 3 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the encoder part.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **in\_channels** & **out\_channels** & **kernel\_size** & **stride** & **padding** \\ \hline Layer 1 & 256 & 128 & 3 & 2 & 1 & 1 \\ \hline Layer 2 & 128 & 64 & 3 & 2 & 1 & 1 \\ \hline Layer 3 & 64 & 32 & 3 & 2 & 1 & 1 \\ \hline Layer 4 & 32 & 16 & 3 & 2 & 1 & 1 \\ \hline Layer 5 & 16 & 3 & 3 & 2 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the decoder part.  
Training and evaluation were conducted under identical conditions using the same image sequences, training epochs, and image resolution. We used publicly available PyTorch implementations of PredRNN and adapted them to our data format.  
Both our proposed model and PredRNN were evaluated using the same three metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image-Patch Similarity (LPIPS). These metrics were computed for each generated frame in the sequence, allowing us to assess both image quality and temporal consistency throughout the drawing process.
# _Applied Sciences_
## 2 The Gbt-HIPS Method
### Path Extraction and Filtering  
The first step is to extract the decision path of the explanandum instance \(\mathbf{x}\) from every decision tree in the GBT model \(g\). This design choice means that, unlike in IML methods, the rest of the model is ignored when generating the explanation for the given classification event \(g(\mathbf{x})\). This filtering reduces the size of the search logarithmically and is justified because there is only one possible path for \(\mathbf{x}\) down each tree. So, none of the other paths contribute to the final output.  
In the multi-class case, GBT models consist of \(K\) one-vs-all binary logistic classifiers. So, classification is normally modified such that the winning class is determined by the \(k\)th classifier that has the largest positive value. gbt-HIPS uses only paths from this \(k\)th classifier.  
Path extraction simply records the detail at each decision node as it is traversed by the explanandum instance on its way to a terminal node. The decision path consists of this set of decision nodes along with the real-valued output from the terminal node. Recall  
Figure 1: Conceptual diagram of Gradient Boosted Trees High Importance Path Snippets (gbt-HIPS). There are four steps: (**A**) path extraction and filtering, (**B**) decompose the paths and redistribute the predicted logit values among the nodes, (**C**) scoring and ranking, (**D**) merging and pruning.  
that GBT uses regression trees, whose aggregated outputs make a log odds prediction. The extracted paths are then filtered to retain only the paths whose terminal node output has the same sign as the ensemble output (always positive for multi-class settings but could be either sign in binary settings). This stage of filtering is justified because those paths that do not agree with the overall ensemble \"lose the election.\" The decision nodes in the retained paths contain all the information about the model's output. The excluded paths are expected to capture noise, or perhaps attributes that are more strongly associated with the alternative class.
# Effect of Inense Ash on the Engineering Properties of Cement-Based Composite Material
## 4 Conclusions  
1. Incense ash has a high specific surface area and a high surface energy that expedites the hydration reaction, which caused the initial and final setting times to shorten.
2. When 5% of cement was replaced with incense ash, all the specimens had greater compressive strength than the control group. When more than 5% was replaced, the changes in compressive strength were nonsignificant, suggesting that 5% may be the optimal threshold value.
3. At a higher curing temperature, the activation energy provided by the heat can facilitate the formation of Ca(OH)2 and increase the number of collisions between material molecules, thus hastening the hydration reaction and improving the strength of specimens.
4. Thermal conductivity dropped as the replacement ratio grew. Increases in incense ash caused the voids in the cement to grow in size and number, and the increased convective heat transfer and radiant heat transfer through the air in the voids caused the thermal conductivity coefficient to drop.
5. Incense ash is finer than cement. Therefore, an increase in incense ash caused voids in the cement to increase in size and number, increasing water absorption.
6. The diminishing of Ca(OH)2 and SiO2 peaks in XRD results indicated their consumption; a higher replacement ratio required a longer curing time for hydration to be completed.  
Figure 7: XRD diffraction patterns of incense ash pastes (W/B = 0.4; 120 days).  
**Author Contributions:** Conceptualization, W.-T.K. and T.-Y.C.; Data curation, C.-U.J. and T.-Y.C.; Formal analysis, W.-T.K. and C.-U.J.; Investigation, W.-T.K.; Methodology, W.-T.K. and T.-Y.C.; Project administration, W.-T.K.; Resources, W.-T.K. and T.-Y.C.; Supervision, W.-T.K.; Validation, W.-T.K. and C.-U.J.; Visualization, T.-Y.C.; Writing--review and editing, W.-T.K. and C.-U.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Context Reasoning
### Proposed Hybrid Reasoning Process
#### 4.2.2 Application Layer  
The application layer is responsible for performing reasoning that results in tentative actions since the actuation is closely related to the application. The application logic refers to all the rules that define events, actions and services supported by the application. It retrieves high-level contextual information and decides upon equivalent actions. The actuation is excluded from the middleware layer, thus if specific actions are needed in the domain scope, an appropriate universal application should be built to tackle them. In this way, it is easier to handle the permissions and privileges of actions and allow the distribution of a reasoning load.  
Collision detection is necessary when conflicting rules are applicable. Similarly, collision may happen when entities with antagonistic profiles exist in an area. Collision issues are addressed application-wise but are executed server-side to involve all participating entities. Thus, the implementation and execution of application logic is executed on the server, while the actuation is performed by the client. For example, the calculation of a user's phone ringing volume is executed on the server in order to decide whether it collides with the restrictions of the area and other users.
# Bio-Inspired Dielectric Resonator Antenna for Wideband Sub-6 GHz Range
## 2 Bio-Inspired Spiral Shell DRA  
The Button snail Modulus modulus modulus (spiral seashell) presented in Figure 1a was the bio-inspiration for the device of the SsDRA shown in Figure 1b. The SsDRA is placed in the center of a ground plane, a squared substrate with side LSUB, in the origin of the reference system; the dielectric thickness t and the height h\({}_{\mathrm{DRA}}\) of the antenna are also indicated. The considered dielectric material has a permittivity of 2.7 and negligible loss tangent (0.008) in the frequency range of interest [36; 37]. The DRA is fed by a coaxial cable (Z\({}_{0}\) = 50 \(\Omega\), conductor diameter \(\mathcal{O}\) 0.48 mm, insulation diameter \(\mathcal{O}\) 1.52 mm) placed in the antenna origin: the pin height is indicated by h\({}_{\mathrm{PIN}}\).  
The shape of the SsDRA was generated by [PERSON] superformula explicated in Equation (1) [38]:  
\[\mathrm{GT}(\theta,\,\mathrm{f}(\theta),\,\mathrm{a},\,\mathrm{b},\,\mathrm{m }_{1},\,\mathrm{m}_{2},\,\mathrm{n}_{1},\,\mathrm{n}_{2},\,\mathrm{n}_{3})= \mathrm{K}\cdot\mathrm{f}(\theta)\cdot\left[\frac{1}{\mathrm{a}}\cos\left(\frac{ \mathrm{m}_{1}}{4}\theta\right)\right]^{\mathrm{m}_{2}}+\left[\frac{1}{\mathrm{ b}}\sin\left(\frac{\mathrm{m}_{2}}{4}\theta\right)\right]^{\mathrm{m}_{3}} \right]^{-1/\mathrm{n}_{1}} \tag{1}\]
# Calculation Method of Earth Pressure Considering Wall Displacement and Axial Stress Variations
## 2 Theoretical Analysis
### Earth Pressure Zoning  
According to the five special points in the soil behind the wall proposed above, the earth pressure on the retaining wall can be divided into six partitions, as shown in Figure 3.  
1. Active failure zone: \(\varepsilon_{x}<\varepsilon_{xa}\). The stresses in the \(x\), \(y\), and \(z\) directions in the soil are the minor, intermediate and major principal stresses, respectively. A failure plane has developed in the soil behind the wall and active failure has occurred.
2. Active earth pressure zone: \(\varepsilon_{x}\in(\varepsilon_{xa},\varepsilon_{0})\). The stresses in the \(x\), \(y\), and \(z\) directions in the soil are the minor, intermediate and major principal stresses, respectively. The major principal stress is the self-weight stress \(\sigma_{z}\), while the earth pressure on the retaining wall in the \(x\) direction represents the minor principal stress.
3. Passive earth pressure zone before the major principal stress reversal: \(\varepsilon_{x}\in(\varepsilon_{0},\varepsilon_{x1r})\). The stresses in the \(x\), \(y\), and \(z\) directions in the soil are the intermediate, minor and major principal stresses, respectively. Compared to the active earth pressure zone, the direction of the minor principal stress has changed.
4. Passive earth pressure zone between the major and minor principal stress inflection points: \(\varepsilon_{x}\in(\varepsilon_{x1r},\varepsilon_{x3r})\). The stresses in the \(x\), \(y\), and \(z\) directions in the soil are the major, minor and intermediate principal stresses, respectively. In this zone, the self-weight stress \(\sigma_{z}\) is the intermediate principal stress, and the earth pressure on the retaining wall is the major principal stress.
5. Passive earth pressure zone after the second reversal of the minor principal stress: \(\varepsilon_{x}\in(\varepsilon_{x3r},\varepsilon_{xp})\). The stresses in the \(x\), \(y\), and \(z\) directions in the soil are the major, intermediate and minor principal stresses, respectively. The major principal stress is the earth pressure \(\sigma_{x}\) on the retaining wall, while the self-weight stress \(\sigma_{z}\) in the soil is the minor principal stress.
6. Passive failure zone: \(\varepsilon_{x}>\varepsilon_{xp}\). The direction of the principal stresses in this zone is consistent with the previous zone. The soil behind the wall undergoes passive failure along the failure plane.  
However, it is not always possible to divide the earth pressure zones into six regions. In some cases, the earth pressure on the retaining wall when the minor principal stress second reversal occurs is greater than the passive limit earth pressure, which means that the minor principal stress second inflection point does not exist. This indicates that the earth pressure behind the wall is divided into five zones as shown in Figure 4.