esa-sceva/satcom-chunk-collection huggingface dataset:
# Replacement of Natural Sand with Expanded Vermiculite in Fly Ash-Based Geopolymer Mortars
## 3 Results and Discussion
### Compressive Strength  
Figure 9 illustrates the 28-day compressive strength of the geopolymer mixes. According to the figure, the mixes with a 10 molar NaOH solution developed lower compressive strengths compared with those having a 15 molar NaOH solution. The higher compressive strength of geopolymers having a higher activator concentration agrees with previous research on bottom ash-based geopolymer mortar [36], FA-based geopolymer concrete [37; 38; 60], geopolymer mortar containing rice husk ash [70] and geopolymers containing zeolitic tuff and marble waste [51], and this is because of the larger porosity of the mixes having lower activator concentrations [51]. The 10 molar NaOH mixes with 50% and 70% FA exhibited 13-19% and 18-22% lower compressive strengths in comparison with the 15 molar NaOH mixes, respectively. As can be observed from Figure 9, for a given vermiculite content and NaOH concentration, an increase in the FA content caused a decrease in strength in the geopolymer mixes. The lower compressive strengths of the mixes with higher FA contents agree with previous studies on mortar [40; 41; 42; 43; 44] and concrete [45; 46; 47; 52], and this is due to the non-hydraulic behavior of FA at ambient temperature. Therefore, the formation of geopolymer gel takes longer for a mix with a higher FA content [47; 71]. For the 10 molar NaOH solution, 70% FA mixes containing 0%, 10% and 30% vermiculite experienced 10%, 13% and 20% lower compressive strengths, respectively, and for the 15 molar NaOH solution, they showed 4%, 9% and 19% lower compressive strengths compared with the 50% FA mixes, respectively.  
Based on Figure 9, incorporating expanded vermiculite led to a decrease in the compressive strengths of the mixes for a given FA content and NaOH concentration. As is also shown, an increase in the vermiculite content led to decreased strengths of the mixes. These behaviors agree with those reported in previous research on conventional mortar [12; 25; 26], conventional concrete [63; 64], metakaolin-based geopolymer concrete [57] and clay-based bricks [58; 59] and they happened because a higher vermiculite content in the mix required a larger water content for completing the hydration process, which in turn resulted in the strength of the mix [63]. At 50% FA, the mixes containing 15% and 30% vermiculite had approximately 5% and 9% lower strengths, respectively, and at 70% FA, they had approximately 9% and 21% lower strengths compared with the companion mixes without vermiculite, respectively. As can be observed in Figure 9, the compressive strengths of all the mixes met the minimum compressive strength of building panels by the EN 13279 standard (i.e., 2 MPa) [72].  
Figure 9: Compressive strengths of the geopolymer mortar at 28 days.
## 4 Results
### Deformation Evolution Process of Landslide  
The SBAS-InSAR method was used to obtain the average line of sight (LOS) shape change rate map before sliding in the study area, as shown in Figure 8. It can be seen in Figure 8 that before the occurrence of the landslide, the upper part of the landslide deforms violently, and the maximum line of sight deformation speed was as high as 25 mm/a. There was an obvious uplift accumulation area on the right side of the landslide, and the maximum annual deformation rate was 20 mm/a. This may be caused by the compressibility of the soil in the upper and right areas. The disturbance results show that the Hanyuan landslide is in a state of continuous movement from January 2019 to August 2020. Based on these results, it is clear that InSAR technology can be used to identify unstable slopes in the early stages of monitoring and early warning so as to analyze post-disaster stability and reduce losses caused by secondary disasters.  
Figure 7: Elevation difference before and after landslide.
###### Abstract  
Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites' climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs' training (2011-2015) and testing (2016-2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R\({}^{2}\) > 0.90) estimate ETo for all sites except Harrington. Testing period (2016-2017) root mean square errors were recorded in range of 0.38-0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ETo and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed.  
recurrent neural networks; deep learning; irrigation scheduling; Penman-Monteith; physical hydrology components; water cycle budgeting Article
## 4 Results
### Evaluation Results  
We trained and compared six ML models, namely RF, MLP, LGBM, CEM, NODE, and TabNet. The evaluation was performed in terms of standard machine learning classification metric such as: accuracy (_Acc_), precision (\(P\)), recall (\(R\)), and \(F1\)-score (\(F1\)). Model performance was measured in a macro-averaged setting to ensure equal care for all classes.  
Based on the results presented in Table 1, several important observations can be highlighted.  
Figure 2: Tweets processing procedure.  
The first observation suggests that classifiers regularly achieve better results on network features than on text features in terms of all considered performance measures (_Acc_, \(P\), \(R\) and \(F\)1).  
Another observation concerns combined features (the union of text and network features), which provide classifiers with even more fruitful ground for inducing classification models. With respect to the standard measure of accuracy (_Acc_), the classifiers induced from the combined features show a meaningful improvement over those induced from the text features, ranging from 3.9 to up to 7.7%, whereas with respect to the \(F\)1 score, this progress ranges from 3.7 to up to 8.2%. Considering the features from the network, we also find that the performance improvement, which favors combined features over network features, is at most 1.4% for _Acc_ and at most 1.7% for \(F\)1 score. There are also exceptions: for the LGBM classifier, performance remains the same whether features from the network or a combination of features are used, and the exception is the RF classifier, where combined features do not improve performance. In short, the observation based on the results suggests that the features from the network complement the text features well, and in such a combined set achieve better classification performance.  
Considering only the most fruitful results are obtained with a set of combined features, in terms of \(F\)1 score, CEM is the most successful classification model with 67.9%. The lowest performance is achieved for the TabNet model with 66.6%. The MLP and NODE models perform well compared to the CEM model, as their performance is only one percentage point lower.  
Based on these results, we decided to integrate the CEM model as the part of the Multi-Cro-CoV-cseBERT model for retweet prediction based on multilayer network and text features. We will further use this model for information-spreading analysis in the domain related to COVID-19 pandemic.
## 1 Introduction  
Cultural heritage is an irreversible wealth of human civilization and is critical to our understanding of human evolution and cultural diversity. Heritage sites and their surroundings are prone to be affected by natural factors, such as disasters, climate change and hydro-geological variation, as well as by human activities, like uncontrolled tourism, resource over-exploitation and land encroachment. These influences frequently lead to structural and ground deformation of these sites. In general, there are two types of deformation threats for cultural heritage: (1) slow deformation caused by long-term environmental or human-induced influences, such as surface subsidence and crust movements; and (2) rapid deformation, for example dislocation and collapse caused by natural disasters (e.g., earthquakes, landslides, floods) and human activities (e.g., wars and improper restoration). Hence, high-precision technologies are urgently needed to achieve near real-time monitoring and preventive diagnosis of deformation threats, enabling the effective conservation and sustainability of cultural heritage and their surrounding areas.  
Traditionally, deformation monitoring in cultural heritage sites is carried out by installing electrical sensors in selected structures with automatic systems for data acquisition and recording or by using portable instruments with manual reading of data taken at fixed time intervals [1; 2; 3; 4; 5]. The former can provide continuous data in real time, while the latter gives periodic measurements. Both approaches can be used to monitor the structural behavior of important parts of the heritage site, supporting the preventive diagnosis of deformation threats. At present, these traditional methods are being used for structural deformation monitoring of some ancient buildings in Italy and other countries [6]. These methods have certain advantages, such as high the accuracy and reliability of measurements and flexibility in the design of the monitoring system. However, they can only acquire data of the monitored structure within the cultural heritage sites, not the entire area of the site and its surrounding landscape. Furthermore, where it is not feasible to install any instruments or guarantee protection for the sensors, such as in war and conflict zones, these methods will not be applicable.  
In recent years, advances in differential radar interferometry have created new approaches for cultural heritage monitoring and protection. As one of the important Earth observation technologies, differential radar interferometry, particularly the Differential Synthetic Aperture Radar Interferometry (DInSAR), has been successfully used in surface deformation monitoring by measuring phase differences in the line of sight (LOS) direction [7; 8; 9; 10]. Differential radar interferometry overcomes the main limitations of traditional methods for heritage monitoring due to its extensive spatial coverage and not requiring the installation of instruments on heritage structures. Thus, in recent years, its performance in cultural heritage sites has been systematically exploited [11; 12; 13; 14].
# High-Expansion Foam: A Risk Control Option to Increase Passenger Ship Safety during Flooding
## 3 Solutions for Damage Stability Protection Using Foam: AREST System
### Foam Characteristics
#### 3.3.2 Adhesion and Cohesion  
From the HAZID process the following adhesion and cohesion requirements were identified for the AREST risk control options:  
* _AREST P1_: installations must remain in place during their service life and following a flooding incident, where they must withstand buoyant forces. Foam must possess sufficient cohesion to ensure the installation does not break apart when acted upon by buoyant forces.
* _AREST A2_: foam must have sufficient adhesion in order to create a watertight seal within the shutter arrangement. No critical cohesion requirements were identified.  
Given the above, we determined the adhesive and cohesive properties of the foam by conducting a \"pull-off\" test. This works by measuring the force required in order to separate the foam from a given bonded surface (adhesion) or alternatively the force necessary to separate the foam body itself (cohesion).  
Adhesion is a property that varies depending on the nature of the bonding surface and increases when moving from smoother to coarser surfaces. However, in this case steel was considered appropriate given that the targeted spaces for such installations (voids) have predominantly steel surfaces. The pull-off test, also referred to as a stud-pull test, works by firstly applying a layer of foam to a steel plate and allowing it to cure. Following this process, an adhesive connection is made between a metal stud and the foam surface, using an epoxy resin of a stronger bond than that expected by foam adhesion. An incision is then made in the foam around the perimeter of the stud, such that foam adhesion to the plate is the only force resisting separation. An apparatus is then used in order to pull the stud/foam assembly from the steel plate and the force required to do so is recorded, thus providing a measure of foam adhesion. Figure 8 reports the pull test diagram and the different phases of the test.  
Figure 7: Effect of specimen size on permeability determination.  
The foam cohesive properties were also measured in a similar way (same process, without the steel plate). From the test result, the foam has an adhesion to steel of 0.13 MPa and a cohesion at the foam surface of 0.19 MPa. As a result, and given the contact area of the foam installation to the surrounding ship structure, it is highly unlikely that the foam body would break free from its bonds even when fully immersed.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work
### Standards for Indoor Positioning, Mapping and Navigation
#### 2.3.6 Other Standards and Formats  
Besides these standards for indoor positioning, mapping, and navigation, several other standards and formats are also relevant to indoor applications, including the OGC Land and Infrastructure standard (LandInfra) aimed at land and civil engineering infrastructure facilities representation [98]; the green building data model (gbXML) ([[https://www.gbxml.org](https://www.gbxml.org)]([https://www.gbxml.org](https://www.gbxml.org)), accessed on 3 March 2022) aimed at the representation of buildings for energy analysis; the Indoor Map Data Format (IMDF), published by Apple and OGC, that provides the definitions of selected indoor feature types and certain venue types such as airports, malls, and train station based on GeoJSON [99]; the OSM for indoor environments [100], and so on.  
Hopefully, a group of ISO TC211 Geographic Information standards, which specify methods, tools, and services for geographic data management, acquisition, processing, analysis, access, and presenting and transferring geographic information between different users, systems and locations, may be extended and utilized in indoor environments, for example:  
* ISO 19116:2019 Positioning services, which specifies the data structures and contents of an interface between position-providing device(s) and position-using devices(s) so that position information can be interpreted unambiguously.
* ISO 19133:2005 LBS--Tracking and navigation, which describes the data types, and operations associated with those types, for the implementation of tracking and navigation services.
* ISO 19134:2007 LBS--Multimodal routing and navigation, which specifies the data types and their associated operations for the implementation of multimodal location-based services for routing and navigation.
* ISO 19147:2015 Transfer Nodes, which specifies the data types and code lists associated with those types for the implementation of transfer nodes and their services in transport modelling and location-based services.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 4 Isara
### Hardware Characteristics  
The ISARA transmitter (Figure 10) is at 26 GHz. The tone generator includes a phase locked loop (PLL) frequency multiplier to get to 26 GHz from an on board temperature compensated crystal oscillator (TCXO). The output of the PLL connects to a state power amplifier (SSPA) for a total output power of 27 dBm [13]. The output of the SSPA feeds into a bandpass filter which is connected to an (MMIC) switch which toggles between the high gain and the reference gain antenna as part of the ISARA experiment. Prior to reaching the antenna, a coupler diverts part of the RF power and feeds it into a Schottky detector circuit that provides a telemetry voltage to the spacecraft computer through a buffer. A gold plated aluminum chassis was designed to house the hybrid RF electronics. The transmitter weighs no more than 200 g and consumes no more than 10 W of power [13].  
Figure 9: ISARA Block diagram.  
The ISARA reference gain antenna or standard gain antenna (SGA) is a \(4\times 4\) element microstrip patch array that produces 18.9 dB gain and has a broad antenna beam pattern. The SGA was designed to be insensitive to thermal environment so that it provides a stable and reliable signal comparison to the high gain antenna [12].  
The high gain antenna (Figure 11, Table 3) is a reflectarray including three \(33.9\times 8.26\) cm panels. The ISARA antenna incorporates an array of 24 solar cells (designed and fabricated by Pumpkin Inc., [12]) on the opposite side of the reflectarray, and provides about 24 W of spacecraft power. The feed is mounted on the bus in an offset configuration with a focal length of 27.6 cm and a feed offset of 14.67 cm [14]. The feed design uses a \(4\times 4\) element microstrip patch array printed on \(\varepsilon\tau=2.0\) substrate and fed by a separate stripline layer. Each patch generates the required circular polarization [14]. The reflectarray performance was characterized at JPL prior to flight by testing in the anechoic chamber. The test showed a very good agreement between predicted and measured performance and the peak gain was measured at 33.5 dBi. Elevation beamwidth was measured at 2.39 degree and azimuth beamwidth at 2.47 degree. The total antenna mass (feed and reflector) is less than 500 g [14].  
Figure 11: ISARA antenna [14]. The antenna is mounted on a mock-up structure of the CubeSat, in the deployed configuration.  
Figure 10: ISARA transmitter module [13].
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 2 Rockburst Definition and Classification  
### Rockburst Description  
The first recorded rockburst event happened in the gold mines of Witwatersrand, South Africa in the early 1900s [6]. Rockburst occurs when rocks fail suddenly and violently, and there is a clear connection between rockburst activity and mining depth, while stress concentration is also a factor that can lead to failure on the walls of shafts and boreholes after drilling [21]. As mining progresses deeper into the subsurface, the in-situ stress increases compared to rock strength, increasing the likelihood of rockburst. Rockbursts are related to hard rocks, geological structures like faults and dykes, and high extraction ratios and mining techniques that produce stress. Most practical rock engineering tasks predict stress versus strength relationships at subsurface apertures and failure causes [22]. Depending on the rock mass and loading circumstances, failure might happen quickly or slowly. Violent failure with fast ejection characterizes brittle failure or rockburst. Spalling (non-violent brittle failure) or ductile behavior (ability of the rock material to bear extensive, inelastic deformation post-failure without significant loss of strength, e.g., compressing ground) may characterize slow or non-violent failure. [PERSON] defined rockbursts as seismic events causing significant damage to tunnels or mine excavations [23]. Rockbursts are typically associated with excavation damage or support failure. The term does not refer to seismic events that do not cause damage. Table 1 provides a review of historical definitions based on keywords such as violent failure, highly stressed, brittle rock, and strain energy. These definitions range from small rock pieces falling off to large seismic events displacing rock and being felt on the surface, such as a local earthquake.
## 3 Results and Discussion
### Univariate Analysis
It can be observed that both of these parameters exert weak influences on adsorption selectivity. Figure 1: The relationships of (**a**) \(S_{\text{ads}}\) (CO2/N2+O2) and (**b**) \(S_{\text{diff}}\) (CO2/N2+O2) with (**c**) largest cavity diameter (LCD) and (**d**) pore-limiting diameter (PLD). Figure 2: The relationships between (**a**) \(S_{\text{ads}}\) (CO2/N2+O2) and \(Q_{\text{st}}\), (**b**) \(S_{\text{diff}}\) (CO2/N2+O2) and volumetric surface area (VSA). In addition to adsorption and diffusion selectivity, the Henry coefficient of CO\({}_{2}\) reflects the adsorption performance of CO\({}_{2}\) in the infinite dilution state, helping to explain the capture performance of MOFs for air with very low CO\({}_{2}\) concentration. Figure 3a clearly shows the tendency of \(K_{\text{N2}}\) to change with enthalpy. When the porosity \(\phi\) is small, the MOF has no space due to the limited pore volume, and only a small amount of N\({}_{2}\) can be adsorbed; therefore, \(K_{\text{N2}}\) is small. When \(\phi\) is in the range of 0-0.29, \(K_{\text{N2}}\) increases significantly with increasing \(\phi\). When \(\phi\) > 0.29, \(K_{\text{N2}}\) slows down and gradually stabilizes with increasing \(\phi\). Figure 3b compares the Henry coefficients of the 3 gases. It can be seen that the trends of the Henry coefficients of N\({}_{2}\) and O\({}_{2}\) are almost the same; however, CO\({}_{2}\) is different. First, in most MOFs, the Henry coefficient values of CO\({}_{2}\) are basically larger than the Henry coefficient values of N\({}_{2}\) and O\({}_{2}\). Second, when LCD >20 A, the \(K_{\text{CO2}}\) value tends to be level, and eventually stabilizes. The Henry coefficient of CO\({}_{2}\) is still higher than the coefficients of N\({}_{2}\) and O\({}_{2}\), which also leads to MOF selectivity >1 when the LCD is infinite, as seen in Figure 1a Finally, it can be observed that only a few MOFs can be identified for which the CO\({}_{2}\) Henry coefficient can be >10\({}^{-1}\) mmol/g/Pa. Observing these MOF structures reveals that most have smaller or open metal sites. The above univariate analysis can only determine the relationship between individual parameters and performance. \(Q_{\text{st}}\), PLD and LCD are considered to have dramatic impacts on adsorption selectivity and diffusion selectivity, but their variable influences cannot be analyzed quantitatively. We will further utilize 4 types of ML algorithms to obtain additional information about structure-performance.