esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Region, Methods and Datasets
### Assessment Framework and Indicators  
In this study, Hainan Island is taken as the basic geographical unit, while the ecosystem structure and service assessment are paid more attention to, based on the ecological parameters and the most basic characteristics of the ecosystem that can be obtained by remote-sensing technology. By analyzing the temporal and spatial characteristics of ecosystems in different ecological protection areas with different control measures, the differences in the protection effectiveness in these areas are explained. The assessment framework is shown in Figure 2.  
In the framework, the ecosystem pattern, ecosystem quality and ecosystem services are the core assessment contents, reflecting the ecosystem basic features. Regarding the ecosystem pattern, based on the analysis of the type composition and distribution of the ecosystem, and taking the natural ecological space retention rate as the index, this paper compares the impact and effectiveness of the protection of natural ecological space in different ecological protection areas with varying measures of control. Parameters such as the vegetation structure, vegetation coverage, leaf area index, and gross primary productivity are mainly considered in terms of the ecosystem quality. The last three parameters are used to construct the vegetation ecological quality index, and the effects of different levels of ecological protection management and control measures on the quality protection of the vegetation ecosystem are analyzed. Regarding the ecosystem services,  
Figure 1: Spatial distribution of various important nature reserves on Hainan Island.  
according to the dominant ecosystem services in Hainan Island, two types of water and soil conservation services are mainly considered.  
To assess the ecosystem protection effectiveness and its differences, multiple assessment indexes are calculated, including the contribution degree of ecological security, ecosystem stability and change trends. Based on the expansion of the spatial and temporal dimensions, the ecological protection effect in the protection area can be identified; then, the difference in the protection effect can be estimated by comparing the different types of protection areas. The contribution degree of ecological security is mainly evaluated from the spatial dimension. Due to that, the protection area is placed in the natural geographical region and expands the spatial scale, while the role of the protection area in protecting regional ecological security is evaluated by the ecological space, ecological quality and ecological services. A high contribution can represent great effectiveness. The stability and change trends of the ecosystem are mainly evaluated from the time dimension and within a certain period by analyzing the ecological space, ecological quality, and ecological service inside and outside the protected area. Compared with outside areas, the rising stability in the protection area may indicate an improvement trend, causing a better protection effect.
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 2 Material Selection and Pre-Processing  
Generally, fabric-based antenna research focuses mainly on the ergonomic and electrical properties of fabric materials. An essential aspect of antenna development is the selection and/or fabrication of the materials that constitute the antenna. The electrical and mechanical properties of these materials are of utmost importance as they determine to a large extent, the radiation performance of the antenna. Such materials include the fabric type to be used for the radiators and ground layers, substrates or spacers, conductive yarns and inks, adhesive films, conductive glue or epoxy, and cabling.  
For the purpose of this research, the target fabric refers to the textile-based material into which the final antenna will be integrated. This will act as the dielectric substrate on which a conductive ink layer will be dispensed or printed on its top and bottom layers to create a microstrip structure. The base fabric thus refers to a thin fabric material on which conductive ink is soaking in, dispensed, or printed to be used as a conductive sheet that can be cut into any planar shape.
## 4 Experiments and Analysis
### Experimental Setup  
The inputs to the proposed model are embedding initialization by combining TF-IDF weighting and Glove with 200 dimensions with other parameters during the network training steps. At the input layer, we used Gaussian noise with the value of 0.5 and Gaussian dropout with the value of 0.3 at the connection network with the one-dimensional convolutional layer to avoid overfitting. For CNN's layers, we apply three channels with 600, 300, and 150 filters with kernel window sizes of k (3, 4, and 5) and Relu as an activation function to each convolutional layer. The output of the convolutional layer then fits into max-pooling with pool size 2. After receiving the feature from the concatenation layer, we applied the Attention mechanism before feeding the output to the BiLSTM layer. The BiLSTM consists of 256 batch size, a dense size of 128, Relu, and kernel regularization rate of 0.0001. After the dense layer, a sigmoid function is used for the binary classifier. Finally, we engaged binary cross-entropy to train the model and Adam optimizer for the model's learning rate. We run the experiment on Windows 10 on an Intel core i5 processor with 3.00 GHz and 16 GB of RAM.
## 2 Materials and Methods
### Validation  
The fully configured PSM and LBM with assigned material properties and applied loading and boundary conditions were exported from the pre-processor software HyperWorks to Abaqus Standard (v2021, Dassault Systemes, Velizy-Villacoublay, France) to solve the finite element simulations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Material** & **Element Type** & **Constitutive Law** & **Material Properties** \\ \hline \hline \multirow{7}{*}{PSM} & \multirow{2}{*}{Bone} & \multirow{2}{*}{C3D4} & \multirow{2}{*}{Linear elastic} & \(\mathrm{E}=1200\), \(\mathrm{\
u}=0.29\)[16] \\ \cline{3-5}  & & & & Relationship between \(\mathrm{HU}\) and \(\mathrm{E}\) was determined using \\ \cline{3-5}  & & & & Equations (1)–(5) \\ \cline{3-5}  & & & & \(\mathrm{\
u}=0.3\)[37; 38; 39] \\ \hline \multirow{7}{*}{LBM \& PSM} & \multirow{2}{*}{Cartilaginous} & \multirow{2}{*}{C3D4, C3D5} & \multirow{2}{*}{Linear elastic} & \(\mathrm{E}=23.8\), \(\mathrm{\
u}=0.42\)[27] \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \multicolumn{5}{c}{LBM \& PSM} \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline \multicolumn{5}{c}{LBM: literature-based model, PSM: patient-specific model, E: elastic modulus, \(\mathrm{\
u}\): Poisson’s ratio, AF: annulus fibrosus, HU: Hounsfield Unit.} \\ \end{tabular}
\end{table}
Table 1: Cont.  
Both the literature-based and patient-specific FE models were validated. Outcomes were compared with in vivo [53; 54; 55; 56] and in vitro experimental measurements taken from the literature [57; 58; 59; 60]. [PERSON] et al. have shown that comparison with diverse finite element results can increase the quality and reliability of the FE model's prediction [2]. Therefore, the results of the current study were compared to multiple previously published well-accepted in silico data [2; 29].  
In the case of pure bending, the ROM, the intervertebral rotations (IVR), and the facet joint forces (FJF) were compared with in vitro [57; 58; 60] and in silico data [2; 29] at each spinal level. In addition, the load-deflection curves of L1-L5 were plotted together with an in vitro measurement, and a range from numerical results. The ROM and IVR were calculated with an in-house developed MATLAB (MathWorks, Natick, Massachusetts, United States) script (Appendix B). Against pure compressive follower load, the intradiscal pressure (IDP) in the L4-L5 nucleus pulposous of both models was averaged over all elements and then compared with an in vitro [59] and a range of in silico results [2]. A combined compressive, and bending load was applied to the FE models to measure the IVR, FJF, and IDP values at each spinal level and compared with in silico [2] and in vivo measurements [53; 54; 55; 56], as the combined loads are assumed to be the most representative for in vivo experiments [34].
## 4 Results and Discussion
### C/N0 of L1C Observables  
This section presents the noise characteristics of L1C observables based on the observed carrier-to-noise-density ratio (C/N0). The C/N0 was carried out using signal collected in Sanya, China. Figure 11 depicts the observed C/N0 of L1C signal transmitted on Vespucci which includes both L1 CD and L1 CP. The trends of C/N0 of L1 CD and L1 CP transmitted by the same satellite are identical. On L1C signal, the pilot occupies 75% of the signal power. So, the C/N0 of L1 CP is more than L1 CD approximately 5 dB. Hence, the L1 CP has better tracking performance than L1 CD, and the tracking results of L1 CP are more accurate than L1 CD considering the influence of noise. Moreover, the C/N0 of the enhanced signal transmitted by QZSS is greater than that of GPS III.  
Figure 11: Carrier-to-noise-density ratio (C/N0) of L1C transmitting on Vespucci tracked by GNSS Receiver.  
Figure 10: Skyplot of satellites visible during GPS III + QZSS signal collection (PRN number is shown).
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
###### Abstract  
To address the limitations of traditional methods in modeling complex nonlinear relationships in horizontal in situ stress prediction for shale reservoirs, this study proposes an integrated framework that combines well logging interpretation with machine learning to accurately predict horizontal in situ stress in shale reservoirs. Based on the logging data from five wells in the Luzhou Block of the Sichuan Basin (16,000 samples), Recursive Feature Elimination (RF-RFE) was used to identify nine key factors, including Stoneley wave slowness and caliper, from 30 feature parameters. Bayesian optimization was employed to fine-tune the hyperparameters of the XGBoost model globally. Results indicate that the XGBoost model performs optimally in predicting maximum horizontal principal stress (SHmax) and minimum horizontal principal stress (SHmin). It achieves R\({}^{2}\) values of 0.978 and 0.959, respectively, on the test set. The error metrics (MAE, MSE, RMSE) of the XGBoost model are significantly lower than those of SVM and Random Forest, demonstrating its precise capture of the nonlinear relationships between logging parameters and in situ stress. This framework enhances the model's adaptability to complex geological conditions through multi-well data training and eliminating redundant features, providing a reliable tool for hydraulic fracturing design and wellbore stability assessment in shale gas development.  
horizontal in situ stress prediction; XGBoost; well logging interpretation; machine learning; complex nonlinearity; Bayesian optimization; shale reservoirs +
Footnote †: journal: applied sciences  
0000-0001-5002-4899-2237][PERSON]  
1 Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China; [EMAIL_ADDRESS] (W.Y.); pxbguoowei@96 petrochina.com (W.C.); [EMAIL_ADDRESS] (X.P.); [EMAIL_ADDRESS] (W.H.); [EMAIL_ADDRESS] (L.W.); [EMAIL_ADDRESS] (Y.L.)
## 2 Principles and Methods
### Attention Block U-Net Deep Learning Network
#### 2.3.1 AB-U-Net Architecture  
The proposed AB-U-Net architecture is shown in Figure 1. It consists of two parts: the feature encoder module and the feature decoder module.  
The feature encoder module is composed of a series of convolution layers and max pooling operations, which aims to gradually reduce the spatial dimension of feature maps and capture more high-level semantic features. For each convolution layer, it contains 3 \(\times\) 3 convolution, batch norm operation, and a ReLU activation function. In U-Net, convolution operation is processed in the  
Figure 1: Illustration of proposed attention block U-net (AB-U-Net). time reversal (TR)-reconstructed images are fed into the encoder module where the attention blocks embedded. The reconstruction images are outputted through the decoder part.  
DoubleConv block, which includes one max-pooling layer and two convolution layers. Being different from the standard model, the last three DoubleConv blocks are modified and named as modified blocks, where extra attention blocks are added into existing two convolution layers for feature maps refinement. The structure of the attention block will be illustrated in Section 2.3.2. When a 512 \(\times\) 512 pixels TR-generated PA image is fed into the network, two convolution layers are applied to generate various features. Then, a max-pooling operation with the 2 \(\times\) 2 pooling kernel is employed to spatially reduce the size of feature maps, which increases the robustness to some small disturbances of the input image. After four downsampling steps, the low-quality reconstruction PA image is converted into 1064 feature maps, with the size of 32 \(\times\) 322 pixels, including high-level features extracted from the input.  
The feature decoder module is to recover abstract features and corresponding spatial dimensions. It is composed of convolution layers and upsampling operations. In AB-U-Net, the 2 \(\times\) 2 deconvolution is employed to enlarge the image. Skip connection is utilized to concatenate with original features of the same size from the decoder, so that low-level information such as the location of texture can be passed to the decoder. At the output of the decoder, a convolution layer with a 1 \(\times\) 1 kernel is applied to transform 64 feature maps into a 512 \(\times\) 512 pixels single-channel image, and that is the final PA reconstruction image of the network.
## 2 Materials and Methods
### Datasets
#### 2.2.1 GLASS LAI  
The GLASS LAI was calculated from the MOD09A1 using a general regression neural network (GRNN) method at a global scale with spatial and temporal resolutions of 8-day and 500 m, respectively [10]. The \"effective\" CYCLOPES LAI was firstly converted to the actual value using the clumping index (\(\Omega\)), using the following equation:  
\[\text{LAI}_{act}=\text{LAI}_{eff}/\Omega \tag{1}\]  
Then, the MODIS and CYCLOPES LAI were fused in a weighted linear combination to obtain the best LAI estimate. The MOD09A1 data were reprocessed to clear cloud barrier, and the missing gaps were filled to obtain spatial-temporal continuous and smooth data using the algorithm proposed by [PERSON] et al. [35]. A GRNN was trained using the fused LAI for each biome type and the MOD09A1 data over the Benchmark Land Multisite Analysis  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Product**} & \multirow{2}{*}{**Sensor**} & **Spatial** & **Temporal** & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Span**} & \multirow{2}{*}{**Reference**} \\  & & **Resolution** & & & & \\ \hline LAI & MODIS & 500 m & 8-day & GRNN & 2000–2018 & [PERSON] et al. [10, 11] \\ FVC & MODIS & 500 m & 8-day & MARS & 2000–2018 & [PERSON] et al. [13, 14] \\ GPP & MODIS & 500 m & 8-day & Revised & 2000–2018 & [PERSON] et al. [15, 33] \\ \hline \hline \end{tabular}
\end{table}
Table 1: GLASS products were investigated in the study.  
Figure 2: Study area. The land-cover maps in the right panel were published by [PERSON] et al. [34].  
and Intercomparison of Products (BELMANIP) sites. Ultimately, the annual LAI profiles were retrieved using the neural network trained from the MOD09A1 reflectance data [10].
## 3 Results
### Model Usefulness
#### 3.3.2 Mapping Zones with Better and Worse Energy Performance at Building Level  
In Figure 7, a zoomed-in view of the nUBEM model in the Barajas area of Madrid is shown, contrasting newly constructed residential buildings with the historic center of Barajas. The buildings are ranked by their NRPEC, and their corresponding energy performance class is displayed. It is noteworthy how the historic center of Barajas (in red, on the right) shows much lower energy efficiency compared to the new larger blocks (in green, on the left), where the most efficient buildings were constructed in 2017 and the least efficient in 2008. The green buildings on the left side of the image were constructed in 2023, in contrast to the historic center buildings, which date from 1950-1980. This case is particularly interesting as Madrid is not among the regions of Spain that provide open access to their EPCs, making it a valuable study in how the model can fill information gaps. The model has learned the relationships between variables on a national scale and applies them appropriately across all regions.  
Figure 6: (**a**) Actual residential buildings in Spain with an EPC available in open databases; (**b**) residential buildings in Spain predicted using the nUBEM model.  
Figure 7: Closer view of the nUBEM at Madrid. In green to red: residential buildings ranged by NRPEC, energy classes values displayed for multi-family buildings in D3 climate zone. In grey: non-residential buildings. NRPEC: non-renewable primary energy consumption.  
This model provides acceptably accurate NRPEC and GHG emissions values for evaluating districts, neighborhoods, and larger scales, helping to identify energy-inefficient areas. The value obtained for each building is a statistical estimate based on its characteristics, derived through the ML algorithm. While statistically accurate when grouping buildings, it is important to note that the individual NRPEC value predicted for each building cannot be considered accurate for every precise building, as a building may have unique characteristics that fall outside the usual parameters and having unpredictable NRPEC for the algorithm. Therefore, individual validation of a building is necessary to accurately determine its energy performance. This single building exact energy performance prediction of individual buildings exceeds the model's capabilities, as this is not the model's objective and surpasses the capacities of the currently available data.
## 4 Discussion
as the reason for the increase in BMI suggested hypermetabolism related to reduction of HCV associated inflammation, but this finding requires further studies. To the best of our knowledge, this is the first study to evaluate the correlation between HCV core antigen and changes in liver stiffness during anti-viral treatment. The main advantage of HCVcAg is its excellent correlation with HCV RNA accompanied by much lower costs [50; 51; 52]. In our previous study baseline HCVcAg concentration correlated with HCV RNA level, its on-treatment decline was faster, and it predicted a virologic response [7]. In our study, all patients achieved an SSR, i.e., HCVcAg was undetectable at the end of treatment and this effect was maintained during a six-month follow-up. We also found a statistically significant negative correlation between changes in LS between baseline and FU96 and baseline HCVcAg. Considering that the level of LS changes depends on the severity of inflammation (ALT), a lower concentration of HCVcAg may allow an increased inflammatory response and therefore changes in LS are bigger. However, the importance of this correlation requires further research on a larger group of patients. The advantage of our study is its prospective nature, its innovation in the assessment of changes in liver stiffness and the applied treatment based on DAA regimens. Additionally, for the first time, we analyzed the impact of HCVcAg changes on liver stiffness during antiviral treatment. One of the most important limitations of our work is the relatively small group of patients and the lack of comparative results of liver biopsies through which a more detailed analysis could be carried out, but it could be currently questionable from the ethical point of view due to the availability of non-invasive methods of liver evaluation. Moreover, due to excellent antiviral efficacy of DAA therapy, we were not able to compare LS dynamics in responders and non-responders.
# Distinct Bacterial Consortia Established in ETBE-Degrading Enrichments from a Polluted Aquifer
## 1 Introduction
Indeed, several antibacterial and betaproteobacterial strains capable for partial or full ETBE degradation were isolated from these enrichment cultures [12]. The recent study of [PERSON] et al. [22] has shown that algae (_Scenedesmus, Chlorella_) may be a part of natural microbial consortia from contaminated groundwater and, under light conditions, supply oxygen needed for bacterial ETBE degradation. They identified Comamonadaceae and Candidatus Moranbacteria as the most abundant bacterial groups in algal-bacterial culture that degraded ETBE under fed-batch conditions. The present study was carried out in the context of the bioremediation of a gasoline-polluted aquifer in France, described by [PERSON] et al. [4]. Although BTEX have been already largely consumed in the plume at the time of sampling, ETBE remained as the prevailing pollutant, together with MTBE and TBA. Due to oxygen consumption in the course of BTEX biodegradation, the conditions in the plume became anoxic and therefore unfavorable for oxygenate biodegradation. Oxygenation of the aquifer is an option for ETBE bioremediation in such cases [4]. The main objective of this study was to establish aerobic ETBE-enrichment cultures from the studied aquifer that could later serve for bioaugmentation and assess their ETBE-degradation capacity and taxonomical composition. To this end, the ETBE-enrichment cultures were established from water samples from the anoxic plume of the studied aquifer and from the upstream zone where the conditions were aerobic. Taxonomical composition of the original water samples and of the enrichment cultures was assessed with a 16S rRNA gene-based microarray, and the major taxa were confirmed by cloning and sequencing of the 16S rRNA genes. In addition, we assessed the presence and diversity of the _ethB_ gene in original water samples and ETBE-enrichment cultures by cloning and sequencing.