esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Selection of Assessment Indices and Methods
This study assessed the heat reduction capacity of the area using evapotranspiration analysis by utilizing the EEFlux (Earth Engine Evapotranspiration Flux) model based on the Google Earth Engine system and Landsat 8 satellite image. Table 1 shows the definitions and analysis methods for the four aforementioned assessment indices. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Assessment** & **Definitions** & \multicolumn{2}{c}{**Analytic Methods**} & \multicolumn{1}{c}{**References**} \\ \hline  & & & NDVI = (NIR \(-\) & \\ vegetation & distribution and vitality & RED)/(NIR + RED) & calculation by using & [PERSON] et al. [42] \\ vitality & of vegetation & Landsat image & landset 8 TM & Weier and Herring [43] \\  & & RED = visible red & band 4 (NIR), band 5 (RED) & [PERSON] et al. [44] \\  & & Landsat image & & \\ \hline flood reduction & empirical parameter & curve number & Korean curve number & Korea Ministry of \\ capacity & used in hydrology for predicting direct runoff & by land cover & by land cover & Environment [53] \\ \hline  & & & vegetation & CF: carbon fraction 0.5 & \\  & vegetation & carbon storage using & vegetation & Wi: biomass of trees & \\ carbon & storage & biomass of urban trees & CF \(\times\) Xi \(\times\) Wi & DBH: diameter at breast height & [PERSON] et al. [56] \\ carbon & storage & biomass of urban trees & Wi = (DBH)b & a. b: biomass allometric & Park and Kang [64] \\  & & Xi = vegetation area & equations (broad-leaved tree & \\ storage & & & \(\times\) number of trees & a: 0.1403, b: 2.4595/conifer & \\  & & & & a: 0.1915, b: 2.1436) & \\ \hline soil & carbon storage & carbon storage & soil carbon storage = & forest: 69.7 \(\mathrm{\SIUnitSymbolC}/\mathrm{ha}\) & \\  & storage & in urban soil & area \(\times\) soil carbon/ha & paddy: 60.6 \(\mathrm{\SIUnitSymbolC}/\mathrm{ha}\) & \\  & & & area \(\times\) soil carbon/ha & crop: 45.9 \(\mathrm{\SIUnitSymbolC}/\mathrm{ha}\) & Korea Forest Service [65] \\  & & & & etc: 11.5 \(\mathrm{\SIUnitSymbolC}/\mathrm{ha}\) & \\  & & & & (urbanized areas) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition and analysis method of the ERS assessment indices.
## 3 Results and Discussion
### Surface Area via Brunanuer-Emmett-Teller (BET) Apparatus  
Since the calcination and carbonation involves solid-gas reactions, they are primarily surface based rather than bulk reactions. The surface area of the solid plays a vital role in locking and/or liberating the CO\({}_{2}\) species from the dolomites at appropriate temperature- (calcination) and pressure-driven (carbonation) processes. Moreover, the amount of adsorbed gaseous nitrogen with monatomic layer is proportional to the surface area of the material. Figure 5 represents the 11-point BET (under liquid N\({}_{2}\)) after outgassing the materials at 300 \({}^{\circ}\)C for two hours.  
Based on the BET analysis, we have determined the surface area which is approximately 35.45 m\({}^{2}\)/g for the handpicked dolomite, [HP-Dolo]. From the literature, pure calcium carbonate calcined at 745 \({}^{\circ}\)C had a reported surface area of 15.2 m\({}^{2}\)/g [50] and the surface area of dolomite, from Carl Pool Co. 200+ mesh, is around 0.97 m\({}^{2}\)/g [51]. We have carried out the BET of the Alfa Aesar commercial dolomite sample, [CO-Dolo] and the surface area is found to be 0.7 m\({}^{2}\)/g. Therefore, the higher surface area of HP-Dolo obtained from the BET measurements support the smaller crystallite size (increased FWHM) as evidenced from the XRD results in the Section 3.3.
## 3 Results and Discussion
### VOC Emissions  
Within the frame of this work, to better understand the potential impact on indoor environment quality, short-cut rapeseed, short-cut woodchips and mineral wool insulation VOCs were analysed and compared. Fourteen key volatile organic compounds including aldehydes, alkanes, and terpen were collected especially from bio-waste insulations using HS-SPME GC-MS. Target compounds for bio-based materials (Table 7) were selected based on a list published in ISO 16000-6 (Annex A) focused on building products' VOC emissions in indoor air [35] as the mineral wool proved to behave like an inert material with almost no VOC emissions. In general, short-cut woodchips' VOC detection proved to be the most abundant, resulting in a higher number of VOCs as well as higher detector responses observed in short-cut woodchips compared to short-cut rapeseed comparing the bio-based insulations--especially in the case of hexanal and alpha-pinene. An aldehyde pentanal together with even more important aldehyde-hexanal (described as \"grassy\" [36]) that is known as a product of unsaturated fatty acids oxidation [37] were emitted from a short-cut woodchips sample. Both compounds have been identified as causing unpleasant, irritating odors [38]. Compared to rapeseed and to mineral wool, a wide variety of terpen (such as alpha-pinene, beta-pinene, camphene, 3-carene) were also observed emitting from woodchips' samples.  
The chemical composition of raw material used for building insulation materials production represents only one of the factors affecting the quality of indoor air. In addition, the performance of building materials, and therefore VOCs' release, depends on prevailing thermal and moisture conditions, air pressure difference over the structure, and the quality of construction work [39] (these factors were not considered in presented study). However, it also depends on a structural design with a special focus on wall panel composition of all used materials. Therefore, it is necessary to identify the VOC emissions from insulation materials inside the sandwich panel, determine their environmental impact and select the appropriate enveloping material to prevent their emissions into the interior. It was also addressed in the studies of [PERSON] et al. [40], [PERSON] et al. [41], and [PERSON] et al. [42].
## Appendix A Framework for Physital Cultural Heritage Tourism Application Development
### Applying the Framework to the \"Memories of Kellie\" Case Study
Training and Education:
* Create guides for educators and site staff to help users navigate the application and connect it to the physical site. Stage 5: Solution Development  
Technical Specifications:  
* Use blockchain standards (e.g., ERC-721, ERC-1155, etc.) to mint NFTs for digital collectibles. * Develop AR functionality with accurate overlays of Kellie Castle's architectural evolution. Iterative Development:
* Test each feature with target users, including educators, tourists, and heritage enthusiasts, to refine usability and engagement. Marketing Strategy:
* Promote the application through partnerships with tourism boards and educational platforms, emphasizing its immersive storytelling and cultural preservation goals. Stage 6: Testing and Refinement  
Functional Testing:  
* Validate the performance of AR features and NFT integrations. * Test the application's interactivity and narrative flow across different user demographics. User Feedback:
* Conduct surveys and focus groups to evaluate the app's usability, storytelling effectiveness, and emotional impact. * Address concerns, such as pacing and information delivery, identified during earlier user testing. Compliance Check:  
Ensure alignment with heritage preservation standards and accessibility guidelines. Stage 7: Deployment and Support  
Launch Strategy:  
* Roll out the updated application with AR and Web3 features, ensuring smooth integration with existing systems. * Train site staff to guide visitors in using the app's on-site AR features. NFT Placement and Accessibility:
* Offer digital collectibles through an integrated marketplace within the app. * Use QR codes in physical locations to link visitors to relevant NFTs or interactive features. Compatibility and Maintenance:
* Regularly update the app to include new stories, features, and technical improvements. Stage 8: Evaluation and IterationPerformance Metrics:
* Monitor user engagement through metrics such as NFT redemptions, time spent in the app, and feedback on storytelling. * Assess the app's impact on-site visitation rates and user satisfaction. Long-Term Improvements:
* Expand the narrative to include additional historical periods or architectural changes. * Explore partnerships with other historical sites to scale the application for broader use. Applying the framework to \"Memories of Kellie\" enhances its potential as an interactive DCH tool, bridging digital and physical heritage experiences. By integrating NFTs, AR features, and gamified storytelling, the application can create lasting connections between users and Kellie Castle's history. These additions not only enrich the user experience but also support cultural preservation and sustainable tourism, ensuring the project's long-term relevance and impact.
## 3 Results and Discussion
### Prediction Results of the CNN-FSPA Model  
As the last section stated, the CNN models showed better prediction ability for the site amplification factors than the traditional BPNN models. In the following two sections, the CNN-FSPA and CNN-PSPA models are discussed in detail with regard to parameter optimization and prediction results. Based on the optimized parameters, the comparatively better prediction model was trained for predicting the site amplification factors of the Lower Hutt Valley.
# Analytical Investigation of Viscoelastic Stagnation-Point Flows with Regard to Their Singularity
## 5 Conclusions  
In this paper, we obtained the analytical solutions of the stress distributions of a wall-free steady stagnation-point flow with the proposed velocity profile \(\mathbf{u}=(ax,-ay)\) for the Oldroyd 8-constant model under different material relations. All solutions here are compatible with the momentum conservation equation, and their singularities are discussed in detail. The results show that all models have singularities near the stagnation point. Most of these can be effectively avoided by appropriately choosing the model parameters and free functions. However, the singularity in Oldroyd-B and Maxwell-B models at the Weissenberg number \(Wi=\frac{1}{2}\) is non-removable. The results in this investigation can be directly used to analyze the stress contributions and their singularities of a wide spectrum of viscoelastic models derived from the Oldroyd 8-constant model. Furthermore, for the Oldroyd-B model, we obtained the analytical solutions of the stress tensor in a near-wall stagnation-point flow with the proposed velocity profile \(\mathbf{u}=\pm(axy^{\alpha-1},-y^{\alpha})\) with \(\alpha>1\) and show that the solutions can satisfy the momentum conservation along the streamline passing the stagnation point. To the best of our knowledge, such an analysis of the compatibility of model solutions with momentum conservation in a near-wall stagnation-point flow is absent in the literature. To further investigate near-wall stagnation-point flows, a more general velocity profile has to be proposed.  
Furthermore, nowadays, there is a tendency to make comparisons, at least qualitative, between complete theoretical models that are not computer-implementable and experimental models in order to understand which terms of the theoretical models are not covered by the experimental models, and moreover, to understand the correspondences between terms of the theoretical and experimental models, see, e.g., [18]. For the next step, it will be an interesting and valuable work to further compare the models discussed in this article under different material parameter relations with the experimental models.  
Conceptualization, M.O. and Y.W.; validation, J.L.; formal analysis, J.L., M.O. and Y.W.; investigation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, M.O. and Y.W.; visualization, J.L.; supervisions, M.O. and Y.W.; funding acquisition, M.O. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deutsche Forschungsgemeinschaft (DFG--German Research Foundation). The OAP was funded by Technical University of Darmstadt.  
Not applicable.  
We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG--German Research Foundation) and the Open Access Publishing Fund of Technical University of Darmstadt, and further partial funding by the DFG as part of the OB 96/46-1 and OB 96/52-1 projects.  
The authors declare no conflict of interest.
## 6 Conclusions and Future Work
### Conclusions  
This study introduced a configurable test environment utilizing the OMF to facilitate the empirical evaluation of network measurement tools. By addressing limitations of traditional testbeds, such as fixed topologies and lack of flexibility, the proposed environment provides a modular and reconfigurable platform for conducting a wide range of network experiments under realistic conditions.  
A key contribution of this work is the seamless integration of widely used measurement tools, including pathload, spruce, wbest, pathchip, iperf, netperf, and many more, into the OMF framework. This extensive toolset enhances the versatility of the test environment, catering to a wide range of experimental needs. Predefined templates automate the setup of common scenarios, enabling researchers to execute robust and repeatable experiments with minimal effort. This integration significantly reduces setup time while ensuring consistency, adaptability, and the ability to address diverse use cases effectively.  
The framework's capabilities were validated through four case studies. The first demonstrated its ability to simulate real-world network dynamics by evaluating the accuracy of RTT measurement tools under varying queuing delays. The second highlighted its suitability for bandwidth estimation by comparing active tool outputs with MRTG-derived ground truth under fluctuating cross-traffic conditions. The third showcased its effectiveness in measuring achievable throughput for TCP and UDP in wireless networks, illustrating its utility in exploring protocol performance under varying link layer rates. The fourth case study focused on evaluating the performance of VoIP applications under diverse network conditions, such as varying levels of packet loss and jitter, using key metrics like MOS and PDR to assess communication quality and reliability.  
The modular architecture of the environment ensures scalability, supporting small-scale tests and large-scale multi-node experiments. Automation features, including centralized data collection integrated with the OMF measurement library, enable efficient real-time data analysis, simplifying the interpretation of experimental results.  
The OMF-enabled environment represents a significant advancement in network research, offering unparalleled flexibility, configurability, and automation. Its ability to replicate realistic network conditions and integrate diverse tools makes it a valuable resource for researchers, facilitating precise, repeatable, and meaningful experiments that address complex research questions.
# Temperature Effects on the Natural Frequencies of Composite Girders
## 4 Numerical Simulations for Modal Testing  
The effects of temperature on the natural frequencies of the specimen with were evaluated through FE simulations. To account for thermal effects on materials, the temperature dependencies of the elastic modulus and thermal coefficients were incorporated into the FE model. To simulate realistic heat distribution and thermal expansion effects in the specimen, heat transfer and thermal expansion analyses were conducted consecutively prior to modal analysis. The thermal displacements obtained from the heat expansion analysis were incorporated into the modal analysis as initial displacement fields. Additional details on our numerical simulations are provided in the following subsections.
# Special Issue Advances in High-Performance of Eco-Efficient Concrete
## 4 Development and Evaluation of Nano-Silica Sustainable Concrete
This is because the freeze-thaw damages cause the internal structure of RRAC to become porous and prone to cracking, thus providing convenient channels for CO2 to react with the alkali in the cementitious materials. With the growth of the replacement rate or recycling number, RRAC reveals serious freeze-thaw damage and inferior carbonation resistance, which is due to the continuous deterioration of repeatedly recycled concrete aggregate (RRCA) quality. However, when the replacement rate was 25%, the carbonation depth for the third generation of RAC was comparable to the second generation of RAC at a 75% replacement rate, and even the first generation of 100% RAC. To ensure better carbonation resistance durability of RRAC, the low replacement rate of RRCA should be considered. For the third generation of RAC with the 100% replacement rate, its highest carbonation depth after freeze-thaw cycles was 9.16 mm, which still met the design requirements for structural use in a microfrozen region. This indicates that it is feasible for three generations of RRAC to be used in the microfrozen environment and that RRAC has great engineering application potential and promotional value. Investigation of Mechanical Properties, Durability and Microstructure of Low-Clinker High-Performance Concretes Incorporating Ground Granulated Blast Furnace Slag, Siliceous Fly Ash and Silica Fume  
[PERSON], [PERSON] and [PERSON] [6] show that the main assumption of eco-efficient High-Performance Concrete (HPC) design is the reduction in Portland cement clinker content without negatively affecting the composite's mechanical and durability properties. In this paper, three low-clinker HPC mixtures incorporating slag cement (CEM III/B as per EN 197-1) and Supplementary Cementitious Materials (SCMs)--Ground Granulated Blast-Furnace Slag (GGBFS), Siliceous Fly Ash (SFA) and Silica Fume (SF)--were designed. The maximum amount of Portland cement clinker from CEM III/B varied from 64 to 116 kg in 1 m\({}^{3}\) of concrete mix. The compressive strength of HPC at 2, 7, 14, 28, 56, 90 days, and 2 years after casting, as well as the modulus of elasticity on 2-year-old specimens, was tested. The depth of water penetration under pressure and internal frost resistance in freeze-thaw tests were evaluated after 56 days of curing. Additionally, the concrete pH value tests were performed. The microstructure of 2-year-old HPC specimens was analyzed using scanning electron microscopy (SEM). The research proved that it is possible to obtain low-clinker HPCs that reach a compressive strength of 76-92 MPa after 28 days of curing and show high values of modulus of elasticity (49-52 GPa), as well as increased resistance to frost and water penetration under pressure.
###### Abstract  
Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.  
forest degradation; indicators; MODIS; multi-scale; validation Article  
Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images  
To appear in the proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015, pp. 1-10.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
## 2 Primary Theory  
### Wavelet Packet Decomposition  
The sampled signal in WT will be passed through the low-pass and high-pass filters to obtain the corresponding results. Each result is considered as a sub-space; however, only the low-pass filter results will be regarded as the input of the next step and then get another layer of low-pass and high-pass filtered results [23]. Different from the WT, WPD will transform the results of low-pass and high-pass filters further; therefore, the results of wavelet packet transform can be regarded as a complete binary tree whose root node is the original signal, and the sub-nodes of the next layer are the results of WT [20;24]. A three-level sub-band tree is shown in Figure 1, where \(s\)(0,0) represents the initial space of the dataset, \(s\)(\(j\),\(n\)) denotes the decomposed WPS, \(j\) represents the number of decomposed levels and \(n\) represents the index of the subspace at the \(j\)-th level. The \(k\)-th WPD coefficient of the \(n\)-th wavelet packet at the \(j\)-th level can be expressed as:  
\[d_{j}^{m}(k)=\left\{\begin{array}{l}\frac{\sum\limits_{n}h_{0}(m-2k)d_{j-1}^ {n/2}(m)\text{ if $n$ is even}\\ \frac{\sum\limits_{n}h_{1}(m-2k)d_{j-1}^{(n-1)/2}(m)\text{ if $n$ is odd}\end{array}\right. \tag{1}\]  
where \(h_{0}(k)\) and \(h_{1}(k)\) are a pair of quadrature mirror filters that are irrelevant to the levels, and the relationship between them is as follows:  
\[h_{1}(k)=(-1)^{1-k}h_{0}(1-k) \tag{2}\]  
After that, the decomposition coefficient of the \(j\)-th layer can be obtained by iteratively calculating the coefficients of the (\(j-1\))-th layer.  
Figure 1: Tree structure for wavelet packet decomposition.  
The frequency range corresponding to the subspace \(s(j,n)\) is \([\frac{nf_{\text{s}}}{2^{j+1}},(n+1)f_{\text{s}}/2^{j+1}]\), where \(f_{\text{s}}\) represents the sampling frequency.  
The energy of a signal is decomposed into the time-frequency domain by WPD, and it can be reflected by the unit value of WPD coefficients. Because of the perfect performance of WPD, it is superior to other methods in dealing with the typical non-stationary signals, like MI-EEG [25].