esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Estimation of \(Rsei\)  
The \(RSEI\) has been widely used in regional eco-environmental quality assessment since it was proposed by [PERSON] et al. The \(RSEI\) consists of the greenness index (\(NDVI\)), the humidity index (\(WET\)), the heat index (\(LST\)), and the dryness index (\(NDBSI\)). The selected indicators were completely based on remote-sensing information, which was easy to obtain. The calculation process does not need manual intervention, and the results are objective and reliable [31; 32]. The spatial resolution of the \(RSEI\) in this study was 30 m, and the temporal unit was annual. The \(RESI\) can be expressed as:  
\[RSEI=\frac{PC1-PC1_{\min}}{PC1_{\max}-PC1_{\min}} \tag{6}\]  
\[PC1=PCA(NDVI,NDBSI,LST,WET) \tag{7}\]  
\[NDVI=(\rho_{NIR}-\rho_{\text{Ref}})/(\rho_{NIR}+\rho_{\text{Ref}}) \tag{8}\]\[NDBSI=(IBI+SI)/2 \tag{9}\] \[SI= \begin{bmatrix}(\rho_{SWIR1}+\rho_{\text{Red}})-\left(\rho_{Blue}+ \rho_{NIR}\right)\end{bmatrix}\] (10) \[\begin{bmatrix}(\rho_{SWIR1}+\rho_{\text{Red}})-\left(\rho_{Blue}+ \rho_{NIR}\right)\end{bmatrix}\] \[IBI= \begin{array}{ll}\{2\rho_{SWIR1}/(\rho_{SWIR1}+\rho_{NIR})- \left[\rho_{NIR}/(\rho_{NIR}+\rho_{\text{Red}})\right.\\ \left.+\rho_{Green}/(\rho_{Green}+\rho_{SWIR1})\right]\}/\{2\rho_{SWIR1}/(\rho_{ SWIR1}\\ \left.+\rho_{NIR}\right)-\left[\rho_{NIR}/(\rho_{NIR}+\rho_{\text{Red}})+ \rho_{Green}/(\rho_{Green}\\ \left.+\rho_{SWIR1}\right)\right]\}\end{array}\] (11) \[WET_{TM}= \begin{array}{ll}0.0315\rho_{Blue}+0.2021\rho_{Green}+0.3102\rho _{\text{Red}}+\\ 0.1594\rho_{NIR}-0.6806\rho_{SWIR1}-0.6109\rho_{SWIR2}\end{array}\] (12) \[WET_{OLI}= \begin{array}{ll}0.1511\rho_{Blue}+0.1972\rho_{Green}+0.3283\rho _{\text{Red}}+\\ 0.3407\rho_{NIR}-0.7117\rho_{SWIR1}-0.4559\rho_{SWIR2}\end{array} \tag{13}\]  
where \(PC1\) is the first principal component, \(NDVI\) is the normalized difference vegetation index, \(NDBSI\) is the dryness index, \(SI\) is the soil index, \(IBI\) is the improved building index, \(WETOLI\) is the humidity index calculated by the OLI sensor, and \(WETTM\) is the humidity index calculated by the TM sensor.  
It should be noted that the land surface temperature (\(LST\)) index was calculated using the open-source code (the SMW algorithm) [33], provided by [PERSON] et al. in the Google Earth Engine (GEE).
# Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions
## 2 Classical FCM and Modifications on Its Objective Function
### A Sparsity-Inducing Term  
Contrary to the previous approach, it is possible to modify the objective function by adding a sparsity-inducing term, which will manifest a tendency towards crisper and more interpretable clusters:  
\[J(X,\ U,\ V;\ m,\ C,\ \lambda)=\sum_{i=1}^{n}\sum_{j=1}^{C}\mu_{ij}^{m}d^{2} \big{(}x_{i},\ c_{j}\big{)}-\lambda\sum_{i=1}^{n}\sum_{j=1}^{C}\mu_{ij} \tag{4}\]  
This penalization term would cause the data instances to belong to fewer clusters but with higher membership values. This is of considerable usefulness for feature selection in high-dimensional datasets or interpretability in scenarios like image analysis, customer segmentation, etc. [19]. Several variants of this idea have been presented before, primarily aiming to rectify the robustness in the presence of noise and outliers and the clustering of high-dimensional data.
## 4 Discussion
### Ecological Significance of the EOS and Its Turning Points  
Phenological changes have great effects on the structure and function of ecosystems. At the community level, various species have different phenological responses to climate change, whereas the EOS can lead to a change in the competition for light and water conditions [17; 56]. Moreover, plant species changes in the community introduced by the EOS can lead to phenological mismatches; for example, the period of high consumer demand for a resource does not match with the period of resource abundance [57]. At the ecosystem level, phenological grassland changes can modify certain land surface parameters (e.g., albedo, sensible heat flux, evaporation, boundary layer conductivity),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Provines**} & **Climate** & **Human Activities** & **Climate-Human Activities** \\  & **Independent (\%)** & **Independent (\%)** & **Intersections (\%)** \\ \hline Qinghai & 40.22 & 10.45 & 28.19 \\ Tibet & 66.17 & 6.80 & 9.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Independent contributions of the turning points of climate, human activities, and their intersections to the annual turning points variations of EOS at the province level.  
which affects the regional carbon and water cycles [58]. For example, a later EOS may promote GPP and cause plants to close their stomates and increase water use efficiency if a soil deficit exists [59]. Moreover, the delayed EOS may also increase transpiration and partly offset the GPP, therefore leading to closer relationships between the net ecosystem productivities and EOS changes [60].  
The existence of turning points indicates that the EOS trend over long-time periods does not remain unchanged, and the rates of EOS changes differ before and after these points. This observation has several advantages in ecosystem-related studies. First, climatic controls on the EOS in the Qinghai-Tibetan Plateau intersect with each other and follow non-linear relationships with the EOS. An analysis of the EOS before and after the turning points therefore helpful to evaluate the climatic driving mechanisms of the EOS. Second, the detection of spatial heterogeneity of the turning points is helpful for evaluating the large-scale implementation effects of ecological conservation projects. Third, an analysis of the turning points of the EOS relationships with ecosystem functions and services provide important guidelines for fine ecology planning and the development of protection policies.
## 2 Main Result
### Estimated State Error  
Internal state \((x)\) information is often blended into a linear combination or omitted from the external state \((y)\). Therefore, an estimator is required to capture the full state vector \((x)\). However, the estimator complexity increases when the true system undergoes a health change and when bounded stochasticities exist in the system dynamics. Given the initial plant \((A_{m})\) and using the true system's external state, an estimator-observer can be created:  
\[\text{Estimator}\begin{cases}\dot{x}=A_{m}\dot{x}+B(u+Ly)\\ \dot{y}=C\hat{x}.\end{cases} \tag{6}\]  
Note that the estimator and true systems are actuated using the same input \((u)\).  
To minimize the error between both the estimator and the true systems, consider the following error states:  
\[\begin{split} e_{x}&=\hat{x}-x\\ \hat{e}_{y}&=C\hat{e}_{x}.\end{split} \tag{7}\]  
If both internal and external error states converge to zero \(\
i\left\{e_{x},\hat{e}_{y}\right\}\xrightarrow[t\to\infty]{}0\), the true plant states have been numerically captured. To guarantee \(\left\{e_{x},\hat{e}_{y}\right\}\xrightarrow[t\to\infty]{}0\), error state dynamics must be investigated:  
\[\begin{split}\dot{e}_{x}=\dot{x}-\hat{x}&=(A_{m} \hat{x}+B(u+Ly))-(Ax+Bu+v_{x})\\ &=A_{m}\hat{x}+B(\underbrace{L_{*}+\Delta L}_{=L})y-\underbrace{ (A_{m}+BL_{*}C)}_{=A}x-v_{x}\\ &=A_{m}e_{x}+B\underbrace{\Delta Ly}_{=v_{x}}+\underbrace{v}_{=-v _{x}}.\end{split} \tag{8}\]  
Therefore, the state error equations can be written as follows:  
\[\text{Error Equations}\begin{cases}\dot{e}_{x}=A_{m}e_{x}+Bw_{x}+v\\ \dot{e}_{y}=C\hat{e}_{x}\end{cases}, \tag{9}\]  
where \(w_{x}=\Delta Ly\) and \(v=-v_{x}\). In addition, the estimator can be modified to use fixed gain \((K)\):\[\text{Estimator w}/\text{ Fixed Gain}\begin{cases}\dot{x}=A_{m}\dot{x}+B(u+Ly)+K(y- \hat{y})\\ \hat{y}=C\dot{x}.\end{cases} \tag{10}\]  
Resulting in the following error equations:  
\[\text{Error Equations w}/\text{ Fixed Gains}\begin{cases}\dot{e}_{x}=(A_{m}-KC)e_{x}+Bw_{x}+v\\ \dot{e}_{y}=Ce_{x}.\end{cases} \tag{11}\]  
In order to use the fixed gain \((K)\), \((A_{m},C)\) must be observable and selected fixed gain \((K)\) must satisfy: \(\text{Re}(\sigma\{(A_{m}-KC)\})<0\).  
Independent of the desired estimator, \(\{e_{x},\dot{e}_{y}\}\) cannot guarantee to converge to zero because of the two residual terms \(\{Bw_{x},v\}\) in the error equations. An additional argument will be needed to remove and bound residual terms.
## 4 Re-Tracking Algorithm Based on Echo Model Fitting
These mis-pointing angles are recorded by the IMU during airborne experiments, and the deviation in the input mis-pointing angles depends on the precision of the IMU devices. This enhances the fitness between the theoretical echo model and the actual airborne waveform, thereby reducing the possibility of tracking failure caused by the low similarity between the theoretical model and actual waveform [12; 37]. Figure 8: Flowchart of re-tracking algorithm implementation step.
## 3 Results
### Climatic Effects on GPP and WUE
Pr was positively correlated with GPP at a higher level in relatively arid regions (Figure 13d). In Figure 14, except for shrubland and wetland, forest, grassland and cropland WUE showed positive correlations to Rn and Ta, with negative correlations to RH and Pr. This phenomenon was more obvious in the humid  
Figure 11: Average correlation coefficients (0–1, unitless) of GPP and WUE with climatic factors in each arid-humid zone. * indicates the negative correlation. Figure 10: Spatial distribution of correlation coefficients (\(-\)1–1, unitless) between WUE and climatic factors from 2001 to 2021. (**a**) Relative Humidity (RH); (**b**) Net radiation (Rn); (**c**) Average temperature (Ta); (**d**) Precipitation (Pr); (**e**) Wind Speed (WS). and semi-humid zones, while in the arid and semi-arid zones, the response of WUE to climate factors showed spatial heterogeneity among vegetation types. It is noteworthy that in the correlation analysis between WUE and Rn, all vegetation types in each arid-humid zone exhibited positive correlations (Figure 14b). Wetland WUE showed higher negative correlation with Pr and RH (Figure 14a,d), which coincided with the higher average correlation coefficient of WUE with Pr and RH for wetland in Figure 12. In summary, the GPP and WUE of most vegetation types in all arid-humid zones across China were sensitive to Ta. GPP and WUE also showed high sensitivity to Pr and Rn, respectively. The responses of some vegetation GPP and WUE to a climatic factor varied in different regions, which might be related to local topography, vegetation density and the growth cycle. Figure 12: Average correlation coefficients (0–1, unitless) of GPP and WUE with climatic factors for each vegetation type. * indicates the negative correlation. Figure 13: Average correlation coefficients between GPP and climatic factors for each vegetation type in four arid-humid zones from 2001 to 2021. (**a**) Relative Humidity (RH); (**b**) Net radiation (Rn); (**c**) Average temperature (Ta); (**d**) Precipitation (Pr); (**e**) Wind Speed (WS). F: forest; S: shrubland; G: grassland; W: wetland; C: cropland.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 3 Motivation/Desired Outcome of Transaction-Based Energy
### Improved System Efficiency  
A sustained growth in adoption of energy sharing/trading in a transactive manner can effectively improve network efficiency [11], because energy is used up at or close to the point of production, which drastically reduces distance-related transmission losses. The rise in the use of DERs and ESSs enables diverse distributed generation of energy, which reduces dependency on the main grid; thus enabling the utility companies to provide other ancillary services, thereby improving the power grid efficiency. Moreover, with the integration of TE, consumers' demand will be met locally, and congestions on transmission lines will be reduced drastically, and in parallel, leading to a corresponding reduction in energy losses. TE has potential to optimize the use of DER [34] and reduce system operation cost [10]. For instance, utility companies dispatch more generators to meet the growing demand of consumers in peak periods; however, with TE, the demand ratio will reduce during peak periods, thus relieving the utilities of the additional cost and effort to meet peak demand.
# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 4 Results
### Experiment Preparation
#### 4.1.4 Implementation and Training Details  
For training of the transmission removal network in general scenarios, our training dataset contains 4521 image triplets from [13; 41], the validation dataset contains 1012 image triples from [13], and we also added 50 real-world image triplets for testing from [13]. For training of network with portraits, the training dataset contains 3000 triplet images, and the validation dataset contains 1000 triplets.  
We implemented our model using Pytorch. In our generator network, the input port was a combination of image \(\mathbf{I}\) and one or two related information of the transmission layer. To distinguish between the scheme using edge information prediction (Case 1 in Figure 2) and the scheme using flash/ambient pair light (Case 2 in Figure 2), we denote the experimental results for both by Ours(a) and Ours(b), respectively. Note that for Ours(a), we chose the same input for transmission removal except for [13], and for Ours(b), we chose the same input as [13]. All source and target sizes were clipped randomly to 256. The feature extractor was based on the Resnet34, and its input channel number was set as 4. We used the extracted underlying information to represent the content so that the generated image was highlighted in the content, and the difference in the gradient was used to distinguish the content information. Gram matrix changes were made to the features to further constrain the style from source to target. We used a PatchGAN discriminator to constrain the style of the generated image and the size of the output matrix was set as 30. The sizes of the convolution kernels in the middle of the SR module were 9, 1, and 5.  
We trained for 50 epochs with batch size 16. The entire model is learned from random initialization using the RMSProp optimizer [36] with a learning rate of \(5\times 10^{-5}\) and clamp of the discriminator is set to the range of \([-0.01,0.01]\). The whole training converged in approximately 7 h using a single GPU NVIDIA GeForce GTX 3090 Ti for the 4721 image pairs from the training data. We implemented random cropping for images with more than 640,000 pixels. A cyclic calculation with batch size 1 was used in the test network to calculate the mean value.
## 2 Materials and Methods
### WCAG Overview  
Accessibility analysis is a process that allows for determining whether the interface of a given program or application is available to the widest possible audience, and also people with different backgrounds or limitations such as visual impairments [40]. One such standard, called the Web Content Accessibility Guidelines (WCAG), was developed by the World Wide Web Consortium (W3C). Its first version, known as WCAG 1.0, was published in 1999. The next iteration, WCAG 2.0, was made in 2008, and W3C updated its guidelines with 61 success criteria, continuing to take into account users with disabilities [41].  
WCAG 2.2, which is the current version, gives guidelines for making websites more accessible for all users in general, but also focuses on people with disabilities, including accommodations for users with blindness and low vision, deafness and hearing loss, limited movement, speech disabilities, photosensitivity, learning disabilities, and cognitive limitations. These guidelines address web content on desktops, laptops, tablets, and mobile devices in the following four areas [42]:  
1. Perceivable--related to providing information and interface components in ways users can perceive. Examples: Providing text alternatives for non-text content that can be changed to speech or braille; and providing adaptable content that can be presented in different ways without losing information or structure.
2. Operable--related to providing interface components and navigation that is operable for users. Examples: Making all functionality available from a keyboard; providing users with enough time to read and use interface content; avoiding designs known to cause seizures or physical reactions; facilitating users in navigation, searching for content, and determining their current location; and facilitating usage of input devices other than a mouse and a keyboard.
3. Understandable--related to providing information and ways of interface operation that are understandable to users. Examples: Making text context readable and understandable; making interface elements occur and operate in a predictable way; and helping users to avoid mistakes and aiding mistake correction.
4. Robust--related to making interface content robust enough to be interpreted by a variety of user agents, including assistive technologies. Example: Taking care of interface compatibility with commonly used user agents and assistive technologies.  
The compatibility of web content with the WCAG guidelines might be automatically assessed, which is the already established approach [40; 43; 44]. A specialized software takes the link as input and produces a compliance report. Advances in this domain concern even automated webpage refactoring methods that improve web accessibility [45; 46].
## 4 Experimental Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Modulo (\(2^{n}-1\))**} & \multicolumn{2}{c}{**Modulo (\(2^{n}+1\))**} & \multicolumn{2}{c}{**Merged Circuits**} & \multicolumn{2}{c}{**Modulo (\(2^{n}+1\))**} & \multicolumn{2}{c}{**Proposed Work**} \\ \cline{2-8}  & \multicolumn{2}{c}{[1]} & \multicolumn{2}{c}{[2]} & \multicolumn{2}{c}{[1,2]} & \multicolumn{2}{c}{[8]} & \multicolumn{2}{c}{**Proposed Work**} \\ \hline \multirow{2}{*}{\(n\)} & **Area** & **Area** & **Area** & **Area** & **Area** & **Area** & **Area** & **Area** \\  & **(LUT)** & **(LUT)** & **Saving** & **(LUT)** & **Saving** & **(LUT)** & **Saving** & **(LUT)** & **Saving** \\ \hline \multirow{2}{*}{\(8\)} & \(84\) & \(103\) & \(187\) & \(0\) & \(113\) & \(+39.57\%\) & \(107\) & \(+42.78\%\) \\ \hline \multirow{2}{*}{\(16\)} & \(355\) & \(358\) & \(713\) & \(0\) & \(382\) & \(+46.42\%\) & \(338\) & \(+52.59\%\) \\ \hline \multirow{2}{*}{\(32\)} & \(1406\) & \(1281\) & \(2687\) & \(0\) & \(1498\) & \(+44.25\%\) & \(1284\) & \(+52.21\%\) \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the hardware areas of the proposed method and other methods.
## 5 Experimental Results and Discussion
### Verification of the Health Factor
#### 5.2.5 Comparison Analysis the Method Based on OCV and the Method of This Study  
In all above 18 estimations, the one with SOC interval 95%-30% and 11 order polynomial OCV-SOC curve has the smallest estimated error, which is showed in Table 6.  
Compared the Table 6 with Table 5, first of all, just like SOH prediction method proposed, in the early and middle cycles, the relative error of the method based on OCV is smaller than it in the later cycles. And, the relative error of the method proposed is smaller than that of method based on OCV. The OCV-SOC curve is affected by the aging of the battery. In other words, the real OCV-SOC relationship is related to the working condition and the degradation of the battery. Therefore, only using the OCV-SOC curve obtained only according to the first OCV test is lack of precision. The influence of hysteresis effect is becoming increasingly with the degradation of the battery; therefore, in the later cycles of the battery, the estimating errors are bigger than those of in the early or middle cycles.
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 3 Results and Discussions
### Training Time for AI Models
For the LUCIA building HVAC (nZEB), the training times are as follows: for ANN, approximately 5.5 s; for Random Forest, approximately 2 s; for XGBoost, around 8 s; for RBFN, about 19 s; for Autoencoder, approximately 11 s; for Decision Tree, around 7 s. In the FUHEM building (heating) scenario, the training times are as follows: for ANN, around 1.3 s; for Random Forest, approximately 1.4 s; for XGBoost, about 0.7 s; for RBFN, around 2.1 s; for Autoencoder, about 1.1 s; for Decision Tree, approximately 0.2 s. For the EII building (cooling), the training times observed are as follows: for ANN, approximately 1.8 s; for Random Forest, about 1.7 s; for XGBoost, around 0.8 s; for RBFN, about 3.2 s; for Autoencoder, approximately 2 s; for Decision Tree, about 0.1 s. Finally, for the EII building (heating), the training times are as follows: for ANN, approximately 2.1 s; for Random Forest, around 1.3 s; for XGBoost, about 0.9 s; for RBFN, about 2.2 s; for Autoencoder, approximately 5.8 s; for Decision Tree, around 0.3 s. Across all scenarios, the RBFN model consistently exhibits the longest training time, while the Decision Tree model consistently demonstrates the shortest training time. The variability in training times across different models and scenarios highlights the need to consider computational efficiency alongside performance when selecting an AI model for specific building applications. Figure 6: Evaluation matrix for 3 different buildings: (**a**) LUCIA building HVAC (nZEB), (**b**) FUHEM building (heating), (c) EII building (cooling) and (**d**) EII building (heating).