esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Program
### Estimation of Rotational Stiffness  
The moment-rotation angle relationship for the joint must be calculated to obtain the rotational stiffness of the joint. The rotation angle can be obtained using the displacements measured by the LVDTs installed on each specimen, as shown in Equation (1) (see Figure 2).  
\[\theta=\tan^{-1}(\frac{\Delta_{1}-\Delta_{2}}{\mathrm{d}_{1-2}})-\tan^{-1}( \frac{\Delta_{3}-\Delta_{4}}{\mathrm{d}_{3}+\mathrm{d}_{4}}) \tag{1}\]  
where \(\Delta_{1}\) is the displacement of LVDT1, \(\Delta_{2}\) is the displacement of LVDT2, \(\Delta_{3}\) is the displacement of LVDT3, and \(\Delta_{4}\) is the displacement of LVDT4. \(\mathrm{d}_{1-2}\) is the distance between LVDT1 and LVDT2, \(\mathrm{d}_{3}\) is the distance between LVDT3 and the joint, and \(\mathrm{d}_{4}\) is the distance between LVDT4 and the joint (see Figure 2).  
To obtain the joint stiffness from the moment-rotation angle relationship, the rotational stiffness was evaluated using a trilinear model to predict the actual rotational stiffness of the structure and its behavior under ultimate conditions (Figure 4).  
Figure 3: Test set-up and joint configuration: (**a**) Test set-up; (**b**) wedge joint in new specimens, one-year used specimens, and some four-year used specimens; (**c**) wedge joint in seven-year used specimens and some four-year used specimens.
# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
## 4 Demand Scenario Generation
### OSABS Base Demand and Beliefs  
#### 4.3.1 Assessment of OSABS as a Climate-Friendly Means of Transport and Intention to Use It  
In the survey described in Section 4.2.1, two questions were used to assess whether autonomous transport bikes would be considered a climate-friendly mode of transport: (1) \"In my opinion, the introduction of autonomous transport bikes brings many benefits for a climate-friendly mobility transition\" and (2) \"I believe that the introduction of autonomous transport bikes brings benefits for the population in the evolution towards climate-friendly transport\". Both items were answered on a five-point Likert scale with the options \"strongly disagree\", \"strongly disagree\", \"undecided\", \"strongly agree\", and \"strongly agree\". Alternatively, \"no answer\" could be selected.  
A total of 781 people answered item 1 (\"Advantages for a climate-friendly change in transport\"), of which 48.4% tended to agree and a further 25.9% fully agreed. Similarly, for item 2 (\"Benefits for the population in the development toward climate-friendly transport\"), of the 781 people who answered it, 50.1% tended to agree and another 24.3% fully agreed.  
With regard to the likelihood of use, participants could indicate whether they rated their own use of the autonomous cargo bike as \"not at all likely\", \"rather not likely\", \"undecided\", \"rather likely\", or \"very likely\" on a five-point Likert scale. Of the 782 people who answered this item, 31.8% thought use was rather likely, and a further 12.5% thought it was very likely. A total of 23.9% were undecided.  
In summary, these descriptive results shed positive light on a possible OSABS use case. More than half of the respondents perceived OSABS as a possible contribution to climate-friendly transport. And although the system did not even exist in prototype form at the time of the survey, 44.3% of respondents could imagine using it themselves. This provides a promising starting point for an application scenario.
# A Comparative Study of Signal Processing Methods for Contactless Geodetic Monitoring
## 1 Introduction
Therefore, before studying the dynamic behavior of a structure, the de-noising, smoothing, and filtering methods should be applied to eliminate the effects of noise [10]. Various authors [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] mainly dealt with the synchronization of the measurement signal obtained by the RTS method, which is essential for further dynamic response analyses. With the development of technology and additional programs, the lack of synchronization is eliminated. If the structure, working conditions, weather, and time allow, it makes sense to use several independent measurement methods and to compare the measured values. The method of comparison was described in the work of [PERSON] et al., 2005 [18]. Today, modern geodetic instruments and sensors such as GNSS, RTS, and Scanners can be used for static and dynamic bridge monitoring [1], which are the main part of a bridge's load test after its construction, reconstruction, or remediation. Nowadays, the RTS system is mainly used, which enables automatic prism monitoring up to a speed of 30 Hz, which is enough for dynamic monitoring of objects in the range of a few centimeters [8; 9; 10; 11; 12; 13; 14]. Increasing the number of complaints allows an additional program, which controls the RTS station via a laptop The software was developed at the Faculty of Geodesy in Zagreb (Zagreb, Croatia) and allows us to make two types of measurements: measurements of length, Hz (horizontal angles) and V (vertical angles) angle in milli-seconds and measurements of Hz and V angle only, whereby, due to the exclusion of the distance measurement, it is possible to make several more regular measurements per second [14; 15]. To better understand the behavior of the structure, it is reasonable to perform measurements with different independent non-contact methods and compare the results, as presented in [22]. Recently, many authors [23; 24; 25; 26; 27; 28; 29; 30] have been concerned with the use and accuracy of the results obtained with GNSS and terrestrial scanners in the determination of the structural response and in various types of object monitoring. In this paper, we give an example of a new way of performing measurements of the dynamic response of a bridging structure using two completely different measuring techniques. RTS was used to measure displacements on two separate structures in the vertical direction of the structure oscillation. In parallel with these measurements, we performedvibration velocity measurements with a seismograph and an associated geophone. Based on the measured physical values, the program was used to analyze the dynamic response of the bridging structure.
# PrimaVera: Synergising Predictive Maintenance
## 2 State of the Art
### Maintenance and Logistics Optimisation  
The easiest way to plan maintenance is to perform it upon failure, i.e., perform corrective maintenance. However, this leads to many failures and high downtime. For decades now, most organisations have used some form of preventive maintenance: periodic maintenance. Maintenance is then triggered by, for example, running time, calendar time or number of take-offs of an aeroplane. The first models were proposed over 60 years ago by [PERSON] and [PERSON] [50]. Nowadays, predictive maintenance is an emerging trend.  
For predictive maintenance, information is used that results from data acquisition, data processing and diagnostics, and prognostics, such as RUL estimates or failure probabilities. Typically, as asset gets older, the RUL estimate goes down and the failure probability goes up. If these estimates would be perfect, maintenance could be performed exactly before breakdown. However, estimates are imperfect and an economic trade-off needs to be made. Performing preventive maintenance too early leads to unnecessary down-time. Performing preventive maintenance too late leads to corrective maintenance, which is typically much more costly since the maintenance has to be performed under high time pressure, leading to high logistics costs to get a service engineer with the right parts and tools at the asset. Furthermore, downtime of a critical component causes the complete asset to be down, which implies high downtime costs for its owner. This means that there is an economically optimal moment to perform maintenance that incorporates these costs and the probability of failure or RUL estimate. This optimisation is further complicated because assets contain many (critical) components, and grouping maintenance leads to fewer disruptions for the customer and lower logistics costs.  
Because it is of key importance to perform maintenance at the right time, there has been a lot of research on making optimal predictive maintenance decisions(for recent reviews, see, e.g., [5; 51]). However, most of the research has been on single-item problems (i.e., one type of component). Exceptions, so papers focusing on multi-item problems, are those of [PERSON] [52] and [PERSON] [53]. There has been some research on integrating maintenance and the service logistics needed to have the right parts, people, and tools available at the moment maintenance is planned [54; 55] and on the usage of condition monitoring information to adapt operations [56]. Further integration of the topics of operations, maintenance, and service logistics is required. Another trending research area is making decisions with limited information. Since the prognostics and other information are often far from perfect, models and decision making need to take these imperfections into account. One way to do that is by modelling problems with partially observable Markov decision processes [57]. Such models are often hard to solve, but research on solving such models is ongoing [58].
## 3 Implementation and Experiments
### Evaluation  
We tested our greedy heuristic in two types of experiments. In the first type (Section 3.5), we compare our results with those of the well-known Mondrian [21] heuristic. We decided to compare with an existing implementation of Mondrian ([39]), since we could not find a more recent implementation of a \(k\)-Anonymity algorithm that is freely available. By specifying all possible pattern vectors, we \"misuse\" our greedy heuristic to solve the classical \(k\)-Anonymity problem. In the second type (Section 3.6), we solve \(k\)-Anonymity and Pattern-Guided \(k\)-Anonymity and analyze the distance of the results provided by our greedy heuristic from an optimal solution (with a minimum number of suppressed entries). Such an optimal solution is provided by the ILP implementation. We provide tables comparing two algorithms, where a cell is highlighted by gray background whenever the value is at least as good as the corresponding value for the other algorithm.
## 5 Conclusions
**Data Availability Statement:** The GPP, ET, and LAI data with a 0.05\({}^{\circ}\) spatial resolution for 1982-2018 can be obtained from the Global Land Surface Satellite (GLASS) products, ([[http://www.geodata.cn/thematicView/GLASS](http://www.geodata.cn/thematicView/GLASS)]([http://www.geodata.cn/thematicView/GLASS](http://www.geodata.cn/thematicView/GLASS)) (accessed on 1 July 2021)). Meteorological data can be obtained from the National Tibetan Plateau Data Center (2019) ([[http://data.tpdc.ac.cn/zh5-shans](http://data.tpdc.ac.cn/zh5-shans)]([http://data.tpdc.ac.cn/zh5-shans](http://data.tpdc.ac.cn/zh5-shans)) (accessed on 1 July 2021)). The VPD data can be derived from the Google Earth Engine (GEE) Terra Climate ([[https://code.eartheengine.google.com](https://code.eartheengine.google.com)]([https://code.eartheengine.google.com](https://code.eartheengine.google.com)) (accessed on 1 July 2021)). Soil water content (SWC) can be obtained from the GLDAS Noah Land Surface Model L4 monthly 0.25 \(\times\) 0.25\({}^{\circ}\) V2.0. ([[https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025](https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025)]([https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025](https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025))\(M\)2.0 (accessed on 1 July 2021)). **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 The Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition
### The Process of the Algorithm  
The Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition is shown as Algorithm 1.  
```
Input: the original traffic flow tensor \(\chi_{t,d,n}\)
1. calculate the date-hour trend of \(\chi_{t,d,n}\), get \(\overline{x}_{h}^{d,n}\)
2. get the intermediate tensor \(\chi^{\prime}{}_{t,d,n}=\chi_{t,d,n}-\overline{x}_{h}^{d,n}\)
3. calculate the hour-minute trend of \(\chi^{\prime}{}_{t,d,n}\), get \(\overline{x}_{m}^{d,n}\)
4. get the residual tensor \(E=\chi^{\prime}{}_{t,d,n}-\overline{x}_{m}^{d,n}\)
5. \(\hat{E}=CP\_ALS(E)\)
6. calculate \(\hat{E}+\overline{x}_{h}^{d,n}+\overline{x}_{m}^{d,n}\)
7. \(x_{t,d,n}=x_{t,d,n}*\omega+(\hat{E}+\overline{x}_{h}^{d,n}+\overline{x}_{m}^{ d,n})*(1-\omega)\) Return \(x_{t,d,n}\)
```  
**Algorithm 1** The Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition  
The input of the Algorithm 1 is original traffic flow tensor \(\chi_{t,d,n}\), which has the missing data. Firstly, we extract the \"day-hour trend\" features \(\overline{x}_{h}^{d,n}\) from the original traffic flow tensor \(\chi_{t,d,n}\), when predict the \"day-hour trend\" features that cannot be calculated by linear regression. Secondly, we get an intermediate tensor \(\chi^{\prime}{}_{t,d,n}\) by eliminating the \"day-hour trend\" features from the original traffic flow tensor. Thirdly, we extract the \"hour-minute trend\" features \(\overline{x}_{m}^{d,n}\) from intermediate tensor, when predict the \"hour-minute trend\" features that cannot be calculated by univariate spline. Fourthly, we get a residual value tensor \(E\) by eliminating the \"hour-minute trend\" features from intermediate tensor. Fifthly, the residual value tensor is decomposed by CP-ALS. The CP-ALS Algorithm is shown as Algorithm 2. Sixthly, we add \"day-hour trend\" features and the \"hour-minute trend\" features to the residual value tensor after reconstruction (\(\hat{E}\)). Seventhly, the non-missing data remain unchanged, and the missing data are set to \(\hat{E}+\overline{x}_{h}^{d,n}+\overline{x}_{m}^{d,n}\).  
```
procedure\(CP-ALS(E,R)\)  Initialize \(A,B,C\) repeat \(A\leftarrow\chi_{(1)}(C\odot B)(C^{T}C*B^{T}B)^{\top}\) \(B\leftarrow\chi_{(2)}(C\odot A)(C^{T}C*A^{T}A)^{\top}\) \(C\leftarrow\chi_{(3)}(B\odot A)(B^{T}B*A^{T}A)^{\top}\)  normalize columns until maximum iterations times or iterations convergence return \(\lambda\), \(A,B,C\)
```  
**Algorithm 2** CP-ALS Algorithm
# Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
## 3 Geoportal Implementation Framework
### Implementation of the Backend: Databases  
The backend PostgreSQL database management system was spatially enabled using the PostGIS extension. PostGIS, through its generic index structure (GIST), embeds the spatial functionality to enable PostgreSQL to efficiently handle spatial data, indexes, GIS algorithms, and geographical analysis functions. The integration of PostGIS was done through the data connection method aimed  
Figure 2: Example of a user query pipeline. Country selection examples from a user click query.  
at upgrading the PostgreSQL object-relational database into a backend spatial database with GIS capability. Moreover, PostGIS adds indexing, transaction, and concurrency functionality to PostgreSQL. PostgreSQL is a robust object-orientated relational database management system with a proven ability to work in high volume environments. It is ACID (atomicity, consistency, isolation, and durability) compliant. MapServer was integrated to support the display of dynamic geospatial maps over the internet. It supports the display and querying of raster and vector datasets from the database. Aside from querying capabilities, the integration of MapServer was aimed at enabling on the fly projections, spatial analysis and editing, high-quality rendering, cross-platform compatibility, and data interoperability. It has client and backend functionality, enabling the implementation of a hybrid architectural design that is known to improve system efficiency and online geospatial analysis.
## 3 Results
### Characterization of Technical Solutions
#### 3.1.2 Tandem Negative Positive Corona (TC)  
The experimentally determined charging efficiencies for the TC charger is shown with dots in Figure 3b. The dependence of \(P\eta\) product on mobility diameter follows a power law curve with an exponent of \(\sim\)1.3. Similar exponents have been reported for unipolar corona chargers and soot particles [21; 40]. The results illustrate that such a tandem configuration can substantially reduce the effect of pre-existing particle charge. Figure 3b also shows the Fuchs model computations for the TC configuration. The experimental data were found to be within the range of charging efficiencies calculated for \(N_{i}t\) products of 2 and 3 \(\times\) 10\({}^{12}\) m\({}^{-3}\)s. The slope of the calculated charging efficiency curves is less steep, exhibiting an exponent of \(\sim\)1.05, which is close to what similar calculations for unipolar chargers predict at such \(N_{i}t\) products [6]. The steeper curve in the experimental data is anticipated owing to the fractal-like structure of graphite particles [20]. Particle losses in the charger can also contribute to the observed differences at smaller sizes. The percentage change in the charging efficiency when the particle charge stage changes from neutral to +1 and +2 is summarized in Table 1 for both experimental data and numerical calculations and for the sizes that are currently considered in the RDE regulations (Regulation 2017/1154) [7]. The experimental results are closer to what the Fuchs model predicts for \(N_{i}t\) products of 2 \(\times\) 10\({}^{12}\) m\({}^{-3}\)s. The influence of pre-existing charge is, therefore, dropping from +18% at 23 nm to +5% at 50 nm for singly charged particles. Similarly, doubly charged particles result in +26% and 8% higher charging efficiencies over the same size range. The performance of this tandem corona configuration can be improved by operating the corona chargers at higher \(N_{i}t\) products (thus increasing the ion concentrations). A 50% increase in the \(N_{i}t\) product is calculated to lead to smaller than 5% differences in the acquired mean charge between neutral, singly and doubly charged particles of 23 nm.
###### Abstract  
A closed suctioning system (CSS) in patients with coronavirus disease 2019 (COVID-19) prevents spraying respiratory secretions into the environment during suction. However, it is not clear whether ventilation is maintained during the suction procedure, especially in patients with compromised pulmonary mechanics. This paper determines the effects of endotracheal tube (ETT) size, suction catheter size, and two lung mechanics (resistance and compliance) on ventilator-related parameters measured during suction. Suction was performed on an adult training lung, ventilated with either volume-controlled (VC-CMV) or pressure-controlled mandatory ventilation (PC-CMV), using ETT sizes of 6.5-8.0 mm paired with suction catheter sizes of 8-14 French (Fr). Peak inspiratory pressure (PIP) increased by 50% when the ETT's ventilation area was less than 25 mm\({}^{2}\) in size, especially in patients with high airway resistance ventilated with VC-CMV. Positive end-expiratory pressure (PEEP) levels significantly decreased when using 14 Fr SC during VC-CMV and fewer effects during PC-CMV. Change of expiratory minute volume increased with higher outer diameter of suction catheters and decreased with severe lung compliance during PC-CMV. The change in ventilator-related parameters were intently monitored in the patient whose pulmonary mechanic was compromised through the CSS endotracheal tube suctioning procedures in clinical airway management.  
COVID-19; closed suctioning system; ventilator-related parameters; pulmonary mechanic; endotracheal tube suctioning; mechanical ventilation +
Footnote †: journal: Applied Sciences
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 6 Research Challenges
### Concurrent Land Cover and Use Mapping  
Rapid changes in land cover, especially urban extents, have been overwhelmingly ignored in large-scale SUHI studies, since it remains a challenge to obtain regional or global land cover data at an annual or timely manner [236]. As a result, most previous studies used outdated urban area maps [4; 52; 111] or a limited number of urban maps [6; 53; 109; 234] to characterize the SUHI intensity over a long time period. For example, [PERSON] et al. [4] studied global SUHI distribution using LST data between 2003 and 2008, but they used the land cover in 2000. [PERSON] et al. [53] estimated the SUHI intensity from 2008 to 2012 in 32 major Chinese cities using the land cover data in 2010. [PERSON] et al. (2016) reported that the SUHI intensity might be underestimated by 50% by using outdated urban-extent maps in China, especially during the daytime. This calls for an accurate and concurrent mapping of land cover that can appropriately characterize the changes in land cover in estimating SUHI intensity and its change over time, in particular for the rapidly urbanizing areas like cities in China and India [18]. For example, efforts such as global urban dynamics mapping that contributes to SUHI studies, especially over large areas, are encouraged [237].
###### Abstract  
Nowadays, citrus greening or Huanglongbing is considered the most destructive disease in the citrus industry worldwide. In the Americas and Asia, the disease is caused by the putative pathogen, '_Candidatus_ Liberibacter asiatics' and transmitted by the psyllid vector, _Diaphorina citri_. It has been shown that volatile organic compounds (VOC) that are released from citrus leaves attract the psyllid vector. Herein, we tested whether the rootstock influenced the stored VOC profile in the scion leaves and if these influences were altered after infestation with _D. citri_. The VOC profiles of the hexane-extracted leaves of the mandarin hybrid 'Sugar Belle' that were grafted on three different rootstocks (C-35, sour orange (SO), and US-897) with and without infestation with _D. citri_ were studied. The GC-MS analysis showed that the scion VOC profiles of the non-infested control trees were similar to each other, and rootstock was not a strong influence. However, after one month of infestation with _D. citri_, clear differences in the scion VOC profiles appeared that were rootstock dependent. Although the total scion leaf VOC content did not differ between the three rootstocks, the infestation increased scion monoter genes significantly on US-897 and C-35 rootstock, increased terpene alcohols on US-897 and SO rootstock, and increased sesquiterpenes on SO. Infestation with _D. citri_ significantly reduced fatty acids and fatty acid esters across all of the rootstocks. Therefore, our results suggest that rootstock choice could influence scions with an inducible volatile defense by enhancing the amounts of VOCs that are available for repelling vectors or for signaling to their natural enemies or parasitoids. According to this study, US-897 may be the best choice among the three that were studied herein, due to its diverse and robust VOC defense response to infestation with _D. citri_.  
volatile organic compounds; rootstock; scion; citrus; infestation; _Diaphorina citri_