esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of International Standards on Curved Surface Isolators for Buildings
###### Abstract  
This study presents a comprehensive comparative analysis of a hospital located in Costa Rica, examining the performance of sliding pendulum isolators under different international seismic design standards. The standards considered in this research include the U.S. code ASCE/SEI 7-22 and various European standards, namely EN 15129, EN 1337, and EN 1998-1. The case study employs the Equivalent Linear Analysis method, as prescribed by Eurocode 8, alongside the Equivalent Lateral Force procedure from ASCE/SEI 7-22. The seismic action is defined using the acceleration response spectrum from the Costa Rican Seismic Code (CSCR-10, 2010). However, certain limitations must be acknowledged when applying the equivalent linear analysis approach. One key restriction is that the isolation system must be modeled with equivalent viscoelastic behavior, which is feasible for sliding pendulum isolators. Despite being a simplified method, this approach proves valuable in the initial selection and optimization of an isolation system, particularly for practitioners. It is recommended that this method be applied as a preliminary step before performing more advanced nonlinear analyses. After determining the optimized parameters for the friction pendulum system, the detailed design of the isolators will be conducted following the provisions of the selected international standards. This process includes verifying compliance with key performance requirements such as self-recentering capability, type testing procedures, deformation verification, and partial load verification on the concrete pedestal, where the isolators are assumed to be installed. These requirements ensure that the isolation system meets the necessary structural performance criteria, providing reliable seismic protection while adhering to international engineering best practices.  
Keywords:seismic response; buildings; seismic isolation; friction pendulum; international standards; equivalent method +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Methodology
#### 2.3.2 Random Forests (RF)  
The random forests (RF) algorithm, proposed by [PERSON] [54], was developed using classification and regression trees (CART) and the concept of \"bagging\". As a machine learning algorithm that can effectively solve high-dimensional regression problems, RF has been used extensively in research of regression and estimation, which uses subsets of data through bootstrap to process random binary trees. By repeatedly selecting random T (T < N) sample sets, a new training sample set is generated from the N original training samples. In the whole process of selecting samples, the same part of the samples may be collected repeatedly. A random subset of the training dataset needs to be randomly extracted from the original dataset for the development and training of the model (see the flowchart in Figure 2). Datasets that are not used in the model training are often called out-of-bag (OOB) data. These OOB datasets will not be used for model fitting, but in turn will be used for testing the estimation ability of the model [25].  
The RF algorithm is a feature selection based on the Gini coefficient. The criterion for selecting the Gini coefficient is that each child node needs to achieve the highest purity. The smaller the Gini coefficient, the higher the stability of the model and the higher the purity. CART is a binary tree, which means that each non-leaf node can only produce two branches. If multiple (taller than two) discrete variables are generated on a non-leaf node, the variable may be reused multiple times. Each feature selected from the RF tree is randomly produced from all the features, which reduces the risk of overfitting. In contrast to other decision trees, each RF tree is part of the selected feature [25]. Among the selected features in this part, the best feature is picked to partition the left and right subtrees of the decision tree, thereby providing more randomness and further improving the model's inductive power [25]. In short, the final estimation of the RF algorithm is the average of all factors. [PERSON]'s research has more details about the RF algorithm [54].  
Figure 2: The flow chart of the RF algorithm.
# Trends and Perspectives of mHealth in Obesity Control
## 3 Results
### Perspectives in mHealth for Obesity Control
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Items** & **Questions** & **Responses** \\ \hline \multirow{8}{*}{Healthcare provider perspectives:} & What are healthcare providers’ attitudes and beliefs regarding the use of mHealth technologies for obesity management? & Usage patterns of mHealth apps and devices for obesity control vary across demographics like age, gender, and socioeconomic status, impacting adoption rates and preferences. \\ \cline{2-3}  & & Healthcare providers perceive potential benefits in integrating mHealth into clinical practice for weight management, such as improved patient engagement, personalized care, and efficiency. However, they also recognize challenges including technical barriers, workflow integration, patient access, regulatory compliance, and evidence-based practice. Overcoming these challenges requires collaboration, training, and ongoing evaluation of mHealth interventions. \\ \hline \multirow{8}{*}{Patient perspectives and experiences:} & Individuals with obesity perceive mHealth tools as valuable for weight management, offering convenience, empowerment, personalization, motivation, education, support, and community. However, addressing barriers to adoption and ensuring credibility are crucial for maximizing their impact. \\ \cline{2-3}  & & Users’ experiences with mHealth applications or devices for tracking progress, setting goals, and receiving support vary based on factors like user-friendliness, customization, data accuracy, integration with healthcare providers, and the effectiveness of support features, all of which influence engagement and adherence to weight management interventions. \\ \cline{2-3}  & & Stakeholders perceive several barriers hindering the widespread adoption of mHealth interventions for obesity control, including technical challenges, data privacy concerns, regulatory uncertainty, limited evidence base, health inequities, healthcare system integration issues, provider training gaps, and user engagement challenges. Addressing these barriers requires collaborative efforts to overcome technical, regulatory, evidentiary, equality, organizational, and user-related challenges. \\ \cline{2-3}  & & Concerns related to accessibility, usability, privacy, and trust can impact acceptance and engagement with mHealth technologies. Addressing these concerns requires a user-centered approach, clear communication about privacy practices, and robust security measures to build trust and improve effectiveness. \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Items, questions, and responses assessing perspectives in mHealth for obesity control generated by ChatGPT.
# A Novel Complex-Valued Encoding Grey Wolf Optimization Algorithm
## 3 Complex-Valued Encoding Grey Wolf Optimization Algorithm (CGWO)
### The Complex-Valued Encoding Method
#### The Calculation Method of Fitness Value  
The complex domain is composed of real and imaginary parts, so the complex number must be changed into a real number when solving the fitness function value. The real value of an objective function is determined by its modulus, and the sign is determined by its phase angle. The concrete practices are as follows:  
\[\rho_{n}=\sqrt{X_{R_{n}}^{2}+X_{I_{n}}^{2}}\,,\,\,\,\,\,n=1,\,2,\,...\,,\,M \tag{15}\]  
\[RV_{n}=\rho_{n}\text{sgn}(\sin(\frac{X_{In}}{\rho_{n}}))+\frac{B_{L}+A_{L}}{2},\, \,\,\,\,n=1,\,2,\,...\,,\,M \tag{16}\]  
where \(\rho_{n}\) indicates the \(n\)th dimension module, \(X_{R_{n}}^{2}\), \(X_{I_{n}}^{2}\) indicate the real part and imaginary part of the \(n\)th dimension, respectively, and \(RV_{n}\) is converted into a real variable.
## 5 Conclusions  
In this study, we used BEPSHourly model to estimate the NPP of the study area in 2020, analyzed the factors affecting the NPP, and verified the results with MODIS NPP. At the same time, forest latent and sensible heat flux and canopy temperature were simulated. The results showed that within the scale of the flux tower, NPP reached a maximum of 0.33 g C m\({}^{-2}\) (0.5 h)\({}^{-1}\) in June. Sensitivity analysis showed that NPP was highly sensitive to the change of LAI. Latent heat flux and sensible heat flux showed different seasonal variations. Canopy temperature of the upper forest maintained the consistency of change with air temperature. At the regional scale, the total NPP in the study area in 2020 was 4.25 \(\times\) 10\({}^{11}\) g C a\({}^{-1}\), with an average of 564.71 g C m\({}^{-2}\) a\({}^{-1}\). The annual average NPP of silver birch was the largest, and the total NPP of northern Chinese larch was the largest. The spatial distribution of NPP was affected by topographic factors. This study contributes new aspects to our understanding of the application of the model, helping to improve the accuracy of NPP estimation in future research.  
Conceptualization, Z.Y. and W.L.; methodology, W.L.; software, Z.Y. and L.H.; validation, Z.Y. and Y.Q.; formal analysis, Z.Y.; investigation, Z.Y., H.L. and L.H.; resources, X.H.; data curation, Z.Y., Y.Q., H.L. and W.L.; writing--original draft preparation, Z.Y.; writing--review and editing, X.H. and W.L.; supervision, X.H. and W.L.; project administration, X.H. and W.L.; funding acquisition, X.H. and W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"National Natural Science Foundation of China (Grant number 31700561)\" and the National Key R&D Program of China (Grant number 2022 YFD200503).  
Not applicable.
## 3 Simulation Results
### Multiple-Target Environment
At this time, the detection performance of the detector will be affected. Therefore, the false alarm control capability in a multiple-target environment is also an important evaluation indicator for measuring the performance of detectors. The value \(\log\left(P_{fa}/P_{FA}\right)\) can be used to evaluate the false alarm control ability of each detector in the multiple-target environment. The closer the value \(\log\left(P_{fa}/P_{FA}\right)\) is to 0, the stronger the false alarm control ability. The false alarm control capability of each detector in a multiple-target environment is shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**\(\mathbf{P}\)\(\mathbf{d}\) = 0.5** & **CA** & **KLQ(6,3)** & **OS(28)** & **IBQ(6,3)** & **VIHCEMOS** & **KLQVI(6,3)** \\ \hline SNR/dB & – & 15.58 & 16.84 & 15.83 & 17.71 & 15.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of signal-to-noise ratios between various detectors. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & & & & & & \\
**of Interferences** & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline OS(28) & 0.8276 & 0.8135 & 0.7886 & 0.7534 & 0.7052 & 0.3203 \\ KLQ(6,3) & 0.8345 & 0.8237 & 0.8122 & 0.7997 & 0.7475 & 0.6443 \\ CA & 0.8475 & 0.5441 & 0.3503 & 0.2306 & 0.1611 & 0  
As shown in Figure 8, because the detection algorithm used in KLQVI-CFAR is more robust than all other comparative methods, it is able to control false alarms better when there are interferences in the reference window.
# OLGAVis: On-Line Graph Analysis and Visualization for Bibliographic Information Network
## 2 Background and Preliminaries
### Bibliographic Information Network Analysis  
Bibliographic data are used by numerous researchers for the purpose of retrieving information on related papers, such as authors, publishers, and research topics. As the volume of accumulated data of research results becomes vast, studies using bibliographic data for various purposes have continued in addition to simple search of information. Examples include a discovery of a pattern of collaboration between authors, an influential researcher in a group, or a relationship between universities or research institutes, as well as an analysis on an interaction of knowledge contained in a research product, research topics or trends, and a prediction of a new relationship or research topic [8; 12].  
The fields of research addressing the analysis of large-volume bibliographic data can be divided into four major branches. First, there is a graph theory that understands the form through modeling of the graph structure based on statistics, which is the most traditional research field. Another study in 1996 was also conducted in data mining, which was a general term for the process of discovering hidden information and meaningful structures from large-scale databases, to discover and predict links or trends between data by applying supervised and unsupervised learning algorithms for description and prediction. In 1997, with the emergence of the concept of a data warehouse, which refers  
Figure 1: (**a**) Homogeneous Information Network (**b**) Heterogeneous Information Network.  
to a large data storage, a data cube was built, and the OLAP technology was utilized to perform multidimensional data analysis.  
The OLAP, which is used for efficient analysis of structured data, is being expanded to explore the analysis and visualization of more complex structured data. In particular, an analysis has been conducted using the OLAP method for heterogeneous information networks that represent the complex connection relationship of various information objects that are closely interrelated. There are numerous studies that discover and extract inherent knowledge by analyzing the relationship between various information objects that are related to publications in addition to various statistical analyses on publications.  
When modeling heterogeneous information network data, a set of entities constituting nodes and edges has a set of attributes. The attribute that forms a node is called a node attribute, the attribute providing information about viewpoints is called an informational attribute, and the attribute that is given to a relationship is called an edge attribute. The node attribute can include author ID and paper ID, the information attribute can include publisher, publication year, affiliated institution, country, and the edge attribute can include the collaboration frequency and connection strength.
# A Nighttime and Daytime Single-Image Dehazing Method
## 3 Proposed Dehazing Method
### Mixed-Filter for Ambient Illumination Estimation
After obtaining the coefficients \(a_{k}\) and \(b_{k}\), the ambient illumination estimation \(A^{c}(x)\) can be obtained by using the mean value of the window containing the pixels as follows:  
\[A\ (x)=\frac{1}{|\omega|}\sum_{i\in\omega_{k}}a_{k}L_{sw}(x)+b_{k} \tag{10}\]  
Figure 2 shows the comparison of different ambient illumination estimation methods and dehazing effects. [PERSON] et al. 's [9] method chooses the brightest pixels in the image as the fixed value of the ambient illumination, which is affected by nighttime artificial light sources. If the brightness of the artificial light source is used as the ambient illumination, the light estimation will be higher. As a result, the color of the dehazing image in Figure 2b is too dark, which verifies that the ambient illumination estimation for nighttime dehazing should be a local variable rather than a fixed value. Because of the occlusion, the nighttime ambient illumination is directional and not scattered around. At the same time, the illumination intensity will decrease sharply due to the increase in distance and reflection. Therefore, the reflected light and the distant light source as a low light source should reduce the impact of illuminance estimation. It can be seen from Figure 2a,e that this image light is mainly two red light sources and presents umbrella-shaped illumination on the upper left. The ambient illumination estimation method of [PERSON] et al. [26] changes the color of the original light source, and because of its inability to preserve the edge, the light erodes to the backlight side. In comparison, we use the preprocessing image obtained by sub-window filtering as the guide image for ambient illumination estimation. The proposed method retains these two characteristics, and the color after dehazing is closer to the original color.
## 3 Results and Discussion  
### Organic Consumption in Warsaw--Overview  
This study's findings, based on the 449 respondents, indicate that Warsaw is still at an early stage of developing its organic food market. According to the HHS, 50% of respondents classified themselves in the low-organic consumption group (1-10%), while 7% reported consuming no organic products at all (0%). In contrast, 8% indicated high (>50%)-organic consumption (Figure 1). The low consumption of organic food in Poland has also been previously documented through the country's research [35].  
In the survey, about 20% of respondents indicated that they always or often choose organic options for vegetables, fruits, legumes, and eggs--consequently, these products are among the most frequently consumed organic items. In contrast, 78% and 53% of respondents reported that they never select organically produced plant-based drinks and milk, and 25-30% of respondents declared never opting for common staple foods, such as organic potatoes, meats, fish, or bread. Additionally, the less healthy food categories seemed to be more often consumed as conventional products, such as alcohol, sugary drinks, fast food, desserts, sweets, and salty snacks (36-55% indicated that they never opt for organic options) (see Figure 2). The preference for organic vegetables and fruits, as well as eggs, is also supported by the Farma Swietokrzyska report from 2021 [36] and further supported by findings from a study by [PERSON] and [PERSON] in 2021 [37], particularly in the case of organic vegetables. Milk alternatives represent a category of products that may be unfamiliar to Polish consumers, often accompanied by less favorable associations [38; 39]. Furthermore, there is a deficiency in communication and information regarding the various options available on the market [38; 39], and as a result, the majority of consumers may not know about organic plant-based drink selections. As for milk, in Poland, the limited number of organic milk processors hinders the growth of the organic dairy sector [40], translating into limited market offerings, which may explain the lack of preference for organic choices in this study. However, the situation is dynamically changing in this context, since big dairy market players have recently started developing organic production lines. The low consumption of organic fast foods, sweets, and alcohol may also be connected with the low availability of organic options of these food categories in Poland [41]. Previous Danish research shows that the majority of fast-food customers want healthier and more sustainable fast food menu options; however, taste and price remain the major drivers for actual purchase decisions within this food category [42].  
Figure 1: Answers to the household survey question: “What percentage, by volume, of the foods you eat is organic?” by respondents in Warsaw. N = 449.  
Figure 2: _Cont._
## 3 Engineering Background and Main Parameters Affecting Support Structure Design
### Initial Design of the Tunnel Structure Layout and Support  
The preliminary design of the support structure for the drill-and-blast construction method is shown in Figure 3. The design specifications for the initial support vary according to the surrounding rock grades, as described below:  
(1) Grade II/III Surrounding Rock  
The excavation cross-sectional diameter is 13.40 m. Within the 240\({}^{\circ}\) range of the top arch, \(\varphi\)25 diameter rock bolts of length 4.5 m and \(\varphi\)28 diameter rock bolts of length 6.0 m are alternately arranged in a staggered pattern with a spacing of 1.5 m \(\times\) 1.5 m. An 8-diameter steel mesh is suspended with a spacing of 0.20 m, and C25 concrete is sprayed to a thickness of 10 cm.  
(2) Grade IV Surrounding Rock  
The excavation cross-sectional diameter is 14.30 m. Within the 240\({}^{\circ}\) range of the top arch, \(\varphi\)28 diameter rock bolts of length 6.0 m and \(\varphi\)32 diameter rock bolts of length 9.0 m are alternately arranged in a staggered pattern with a spacing of 1.2 m \(\times\) 1.2 m. An 8-diameter steel mesh is suspended with a spacing of 0.20 m, and C25 concrete is sprayed to a thickness of 15 cm.  
(3) Grade V Surrounding Rock  
The excavation cross-sectional diameter is 14.70 m. Within the 240\({}^{\circ}\) range of the top arch, \(\varphi\)28 diameter rock bolts of length 6.0 m and \(\varphi\)32 diameter rock bolts of length 9.0 m are alternately arranged in a staggered pattern with a spacing of 1.0 m \(\times\) 1.0 m. An 8-diameter steel mesh is suspended with a spacing of 0.20 m, and C25 concrete is sprayed to a thickness of 20 cm.  
The secondary lining is proposed to be reinforced concrete throughout the full cross-section of the tunnel, with a thickness of 0.80 m for Grade II and III surrounding rock, 1.20 m for Grade IV surrounding rock, and 1.35 m for Grade V surrounding rock.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project Name** & **Burial Depth** & **External Water** & **Reduction Coefficient** \\  & **(m)** & **Pressure (MPa)** & **(\(\beta_{\text{e}}\))** \\ \hline Jinping II Hydroelectric Station Water & & & \\ Conveyance Tunnel & 2525 & 10.2 & 0.40 \\ Dianzhong Water Conveyance Songlin Tunnel & 606 & 2.0–2.5 & 0.33–0.41 \\ Dianzhong Water Conveyance Qinhao Tunnel & 468 & 2.0–3.0 & 0.43–0.64 \\ Tiensheng Bridge Stage II Hydroelectric Station & 700 & 3.8 & 0.54 \\ Gele Mountain Tunnel & 280 & 2.6 & 0.93 \\ Zhongtian Mountain Tunnel & 1700 & 6.3 & 0.37 \\ Yuanliang Mountain Tunnel & 780 & 3.0 & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical table of external water pressure and reduction coefficients of some projects.  
Figure 3: Preliminary design of support structure by drilling and blasting. (**a**) Grade II/III surrounding rock; (**b**) Grade IV surrounding rock; and (**c**) Grade V surrounding rock.
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
###### Abstract  
Positioning with low-cost GNSS (Global Navigation Satellite System) receivers is becoming increasingly popular in many engineering applications. In particular, dual-frequency receivers, which receive signals of all available satellite systems, offer great possibilities. The main objective of this research was to evaluate the accuracy of a position determination using low-cost receivers in different terrain conditions. The u-blox ZED-F9P receiver was used for testing, with the satellite signal supplied by both a dedicated u-blox ANN-MB-00 low-cost patch antenna and the Leica AS10 high-precision geodetic one. A professional Leica GS18T geodetic receiver was used to acquire reference satellite data. In addition, on the prepared test base, observations were made using the Leica MS50 precise total station, which provided higher accuracy and stability of measurement than satellite positioning. As a result, it was concluded that the ZED-F9P receiver equipped with a patch antenna is only suitable for precision measurements in conditions with high availability of open sky. However, the configuration of this receiver with a geodetic-grade antenna significantly improves the quality of results, beating even professional geodetic equipment. In most cases of the partially obscured horizon, a high precision positioning was obtained, making the ZED-F9P a valuable alternative to the high-end geodetic receivers in many applications.  
low-cost GNSS receiver; u-blox ZED-F9P; network RTK; comparison; positioning accuracy; total station reference; survey-grade antenna +
Footnote †: journal: Sensors