esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimal Device Design Consideration and Simulations
### COM Modeling Analysis for Optimal Device Design Consideration (MATLAB)  
COM modeling was conducted to identify the optimal design parameters for the sensor, with various structural parameters adjusted to achieve the best S\({}_{21}\) performance. The manipulated parameters included the IDT pair, aperture, cavity, metal thickness, and the size and thickness of the sensing material. Figure 2a presents the schematic of the optimal device design, highlighting the cavity area where HfO\({}_{2}\) is deposited. Figure 2b displays the COM modeling results of the S\({}_{21}\) characteristics based on the determined device parameters, with the parameter values listed in Table 1. The COM modeling results indicate low loss, high sidelobes, and low leakage conduction at high frequencies based on the determined parameters. The sensors were then manufactured based on these parameters. The COM modeling process is described in detail in our previous studies [(20; 21)].  
Figure 1: (**a**) Overall view of the developed SAW UV-C sensor with reference SAW device and (**b**) schematic illustration of operating mechanism of the developed SAW UV-C sensor system.  
Figure 2: (**a**) Schematic of the two-port SAW delay line structure with HfO\({}_{2}\) sensing material and (**b**) S\({}_{21}\) response, based on the parameters determined through COM modeling.
###### Abstract  
Multiple damage detection using structural responses only is a problem unresolved that is in the field of structural health monitoring. To address this problem, a novel feature of mechanics-driven statistical moments of wavelet transform-processed dynamic responses is proposed for multi-damage identification in beam-type structures. This feature is referred to as a continuous wavelet transform (CWT)-second-order strain statistical moment (SSSM), with CWT-SSSM in the abbreviation. The mechanical connotation of CWT-SSSM lies in that the SSSM of each order principal vibration contains strain mode shapes, inducing greater sensitivity to local damage. With this method, the CWT is used to extract and amplify the singularities caused by damage in each order SSSM curve, following which the data fusion technology and three-sigma rule in statistics are adopted to construct the damage index. The presence of damage is characterized by the abrupt change in the damage index. The soundness and characteristics of the CWT-SSSM feature are verified by identifying multiple damages in a cantilever beam bearing two breathing cracks. The results show that the proposed feature can accurately designate multiple cracks free of baseline information on the intact counterpart; moreover, it has robustness against noise and applicability under excitations of approximately uniform spectra.  
damage detection; CWT; SSSM; dynamic response; strain mode shapes; baseline-free damage feature; noise robustness; beam-type structure +
Footnote †: journal: Applied Sciences
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
## 2 Data and Methodology
### AERONET Data and Methodology  
AERONET is a ground-based instrument network that supplies a lengthy, continuous, and easily accessible public-realm database of aerosol radiation, optics, and microphysical properties [37]. The AERONET inversion code supplies aerosol optical properties in the entire atmospheric column obtained from direct and scattered radiation measured by the Cimel solar/sky radiometer [38; 39].  
We utilized AERONET aerosol inversions data of AAOD at 440 nm with Level 1.5 and Level 2.0 quality assurance [40]. Cloud clearing and manual checks were performed in the measurements, and pre and post field calibrations were also carried out [41]. The total uncertainty of the on-site instrument AERONET AOD ranges from \(\pm 0.1\) to \(\pm 0.2\), which is related to the higher error (\(\pm 0.2\)) within the ultraviolet spectrum scope [42]. To compare with OMI\({}_{500}\), herein, AERONET AAOD\({}_{440}\) was converted to AAOD\({}_{500}\) according to the following equation [25]:  
\[\text{AAOD}_{500}=\text{AAOD}_{440}\times\left(\frac{500}{440}\right)^{-\text{ AAE}_{440}\cdot 870} \tag{2}\]  
where the term 'AAE\({}_{440-870}\)' refers to the AAE for wavelengths 440 to 870 nm.
# Investigating Load-Bearing Capabilities and Failure Mechanisms of Inflatable Air Ribs
## 1 Introduction
[20] analyzed the impact of wrinkles on the vibration characteristics of inflatable arches, noting that the initial wrinkling position is closely related to the inflation pressure. [PERSON] et al. [21] studied the influence of the pressure, the number of ribs, and the wind ropes on the load-bearing performance of tents and obtained an optimized structure using the response surface methodology. [PERSON] et al. [22] conducted an experimental analysis of the performance of inflatable frames, examining their structural response under both horizontal and vertical loads. The study proves that the presence of creasing can lead to catastrophic structural failure. Additionally, the load-bearing mechanisms of air ribs have been studied. [PERSON] et al. [23] tested the bending performance of air ribs under different inflation pressures. It was concluded that the air-ribbed tents could be analyzed by the traditional beam theory before local buckling occurred. [PERSON] et al. [24] proposed that the buckling behavior of inflatable arches can be simplified to a pseudo-bending beam model. The load distribution, material properties, inflation pressure, and rise-span ratio all significantly affect the buckling characteristics of inflatable arches. [PERSON] [25] and [PERSON] [26] et al. studied the bending behavior of inflatable arches by controlling the load and boundary conditions of the structure. [PERSON] et al. [27] studied the deformation characteristics of inflatable arches and obtained the location of wrinkles by experiments and numerical calculation. It was found that the diameter, depth, and span of the inflatable arch have a significant impact on the load-bearing capacity of the structure. This paper aims to analyze the influence of structures on the load-bearing performance of air ribs. Ten air ribs of different rise-span ratios were designed to calculate the ultimate bearing capacity. The segmentation failure competition mechanism is proposed to explain the buckling behavior of ribs under vertical load. Based on the performance of single ribs, stents with different ribs were built. The stress and deformation of tents under wind loads were studied by the CFD-FEM method. The method can be used to improve the wind resistance of tents, and the best structure is proposed.
## 4 Measurement Formation Process
### Construction of the Measurement-Formation Path (Tree)
elements; nodes' maximum communication range \(R_{comm}\); Output: Set of measurement formation a trees \(T=\{T_{1},T_{2},\ldots,T_{M}\}\), \(T_{i}=\{In_{i},S_{i},Y^{i}\}\)
1 For each measurement \(i=1,\ 2,\ldots,\ M\)
2 Calculate the set of interested nodes \(In_{i}=\{all\ nodes\ s.t.\ \Phi_{ij}\
eq 0,\ j=1,\ 2,\ldots,n\}\). 3 Calculate the set of neighbor nodes \(Ne=\{each\ node\ v\ s.t.\ dist\ (v,t)\leq R_{comm},\ v\in V,\ t\in In_{i}\}\). 4 Do
5 Calculate the number of edges incident from \(Ne\) to \(In_{i}\) (denoted as \(De\left\{|Ne|\right\}\)). 6 If\(Max\left(De\left\{|Ne|\right\}\right)\geq 2\)
7 Remove all adjacent nodes of node \(v\) from \(In_{i}\) where \(v\in In_{i}\) and \(De\left\{v\right\}=Max\left(De\left\{|Ne|\right\}\right)\)
8 Add node \(v\) into \(In_{i}\)
9 Renew \(Ne\) and \(In_{i}\)
10 Ifend
11 While (\(\left|In_{i}^{new}\right|<\left|In_{i}^{old}\right|\)). 12 Calculate the convex hull (denoted as \(Cl\)) of set \(Ne\cup\{sink\}\) by using Graham scan method
13 Construct the MST which takes the sink as a root and connects all \(Cl\) nodes
14\(T_{i}=\{In_{i},Cl,Y^{i}\}\) where \(Y^{i}\) recorded the edge information of such MST
15 Forend
16 Return\(T=\{T_{1},\ldots,T_{M}\}\)
```  
**Algorithm 2**Construction of the Measurement-Formation Path
## 3 Results
### Accuracy  
The LULC map of the Mato Grosso state showed an overall accuracy of 92.4% for 2015, with user accuracy and producer accuracy above 78% (Table 3). The highest confusion was among dry savanna, humid savanna, and pastureland (-86-95% user accuracy), as such classes present a mixture of woods, shrubs, and grasslands [31].  
Water bodies presented 100% confidence, although we acknowledge that the spatial resolution of 100 m limits the classification of small rivers and lakes. For instance, only a few parts of the Xingu River in the Parque do Xingu indigenous land, corresponding to its largest riverbeds, could be mapped in this study (Figure 8). Croplands, forestlands, and water bodies obtained high user accuracy and producer accuracy \(>\)90%. Pasturelands, on the other hand, showed high user accuracy (95%) but low producer accuracy (78%).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Reference/ Classified** & **Cropland (67/84)** & **Forest-Land (72/56)** & **Water Body (29/14)** & **Humid Savanna (64/36)** & **Pasture-Land (63/37)** & **Dry Savanna (64/36)** & **Total (263/359)** & **UA** \\ \hline Cropland & 76 & 0 & 0 & 0 & 8 & 0 & 84 & 90 \\ Forestland & 0 & 55 & 0 & 0 & 1 & 0 & 56 & 98 \\ Water Body & 0 & 0 & 14 & 0 & 0 & 0 & 14 & 100 \\ Humid & 0 & 0 & 0 & 31 & 0 & 5 & 36 & 86 \\ Savanna & 0 & 0 & 0 & 0 & 35 & 2 & 37 & 95 \\ Pastureland & 0 & 0 & 0 & 3 & 1 & 32 & 36 & 89 \\ Dry Savanna & 0 & 0 & 0 & 3 & 1 & 32 & 36 & 89 \\ Total & 76 & 55 & 14 & 34 & 45 & 39 & 263 & \\ PA & 100 & 100 & 100 & 91 & 78 & 82 & & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix among the major land use and land cover classes from Mato Grosso state using the validation dataset. The values in parentheses indicate the number of training and validation samples, respectively. UA, user accuracy; PA, producer accuracy. Overall accuracy = sum of diagonal/total number of samples = 243/263 = 92.4%.
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 3 Study Area and Datasets
### Data Processing  
Due to the coherent nature of the imaging process, SAR images are contaminated with speckle noise. To reduce this noise, a non-overlapping 4 by 4 averaging filter is applied to each image. Each pixel in the resulting image represents the backscatter of a 200 m by 200 m region. This pre-process increases the computational efficiency as well by reducing the data volume. To identify ice and water pixels, the CNN requires image patches. Patch size and stride counts were selected based upon an approach similar to that of [PERSON] et al. [23] to opt the suitable size for such application regarding the original spatial resolution. Patches of 20 by 20 pixels (4 km \(\times\) 4 km) were extracted with a stride of 3 pixels between the center of each patch. Image analysis charts were interpolated to SAR grids using nearest neighbour interpolation and then same patches were recorded.
# Design and Testing of a LUT Airfoil for Straight-Baded Vertical Axis Wind Turbines
## 3 Experimental Equipment and Procedures
### Pressure Measurement and PIV (Particle Image Velocimetry) Devices  
The pressure measurement devices include an electronic scanning measurement system (DSY104), pressure scanner (PSI9816), and angle of attack control mechanism. The accuracy of the pressure measurement and the PSI9816 pressure scanning are less than \(\pm\)0.2% FS (Full Scale) and \(\pm\)0.05% FS, respectively. The accuracy of the angle of attack control is \(\pm\)2\({}^{\prime}\). The Particle Image Velocimetry (PIV) systems include a double pulse laser with a maximum operating frequency of 15 Hz, a camera resolution of 2048 \(\times\) 2048, a field range of 312 mm \(\times\) 312 mm, and an image resolution of 152.66 \(\upmu\)m/pixel. The tracer particles were produced by large smoke generators (TM-1200).  
The pressure on the LUT airfoil surface is given by the pressure coefficient \(C_{p}=(P_{i}-P_{\infty})/\left(0.5\rho V_{\infty}^{2}\right)\), where \(P_{i}\) is the pressure of the pressure tap \(i\), \(P_{\infty}\) is the freestream static pressure, \(V_{\infty}\) is the freestream speed, and \(\rho\) is the air density. In this work, the drag coefficient was calculated by the wake investigation method. The wake rake had a height of 300 mm and was installed 0.9 c downstream from the test airfoil trailing edge. The calculation formula of the drag coefficient is:  
\[c_{d}=\int\limits_{wake\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,  
In the wind tunnel experiment, it was necessary to eliminate errors of measurement. Therefore, the pressure values of the pressure taps and the wake rake were referenced when the freestream velocity was 0 m/s and the angle of attack was 0\({}^{\circ}\). Uncertainty analysis of the coefficients of both the lift and the drag was evaluated with the accuracy of the PSI9816 scanner.
## 4 The Arrowhead-EP of a Real Use Case
### The Use Case Value Chain and Engineering Process Map  
Figure 9 illustrates the value chain and engineering process map (VCEP map) of the SBS use case, a new approach to provide a complete overview of the value chain that supports the use case. As discussed in Section 3, the VCEP-map is a simple and effective two-dimensional schema, describing the ecosystem of the stakeholders, technologies, design methods, and tools involved in the engineering process of the smart boiler and providing information about the EPs that are complementary to the Arrowhead-EP model. For each component of the SBS, the VCEP map specifies (i) which tool chain has been adopted for the engineering of the component and in the phases, (ii) which stakeholders have been involved and (iii) where phases of the smart boiler's life cycle the components are used and by whom.  
For example, the _\"Value and Supply-Chain\"_ section of the map specifies that the SBS vendor (StkH1--light blue) is active in all the phases of the Arrowhead-EP and adopts two tool-chains (TC-1 and TC-2, see the \"Tool-chains\" section). These two tool chains are used in phases 1, 2, 3, and 5, 6 to design, develop, and operate the electromechanical parts of the SBS, integrate the SBS ECU in the boiler, and operate and manage the fleet of SBSs with the IoT framework and the IoT integration platform. Regarding the previous two components, the map also highlights the cooperation of StkH1 and StkH2 (the IoT platform vendor). In the _\"System Components\"_ section, the map specifies that the _IoT framework (SC3)_ is a component designed and developed by StkH2 but operated by StkH1, while the _SB User App (SC6)_ is designed, developed, deployed, and maintained by the app developer (StkH4) and is used during operations by the maintenance operator (StkH5) and by the final user (StkH6). The map also clearly highlights that the evolution phase is not automated by any tool, although the related activities are addressed by some of the stakeholders.  
The SBS use case is a relevant SoS example that is developed, designed, and operated using an Industry 4.0 EP. This demonstrates that the combination of the Arrowhead-EP model and the VCEP-map provides a rich, detailed, easy-to-understand, and complete description of the value chain and life cycle of the final product.  
Figure 9: VCEP map describing the smart boiler system value chain for each Arrowhead-EP phase (AH-EPP).
## 4 Discussion
This would drastically reduce middle to long distance mobility, as well as the use of motorised vehicles. It is important to mention that certain destinations, such as specialised hospitals, will still have a metropolitan radius of influence. For these kinds of opportunities, sustainable and diverse transport alternatives have to be guaranteed;
8. **Use of technology. ** One of the major lessons that the COVID-19 pandemic has left is the massive potential that technology has to reshape education, commerce, and employment. This can generate important changes in the urban realm. By encouraging the use of technology in schools, commerce, and employment centres (e-learning, e-commerce, and home offices), mobility can drastically reduce the need to go to a specific location every day. Lowering the number of travelling people reduces the overloading of public transport, as well as the traffic levels, bringing important social, economic and environmental benefits. Additionally, spaces that are no longer needed, such as some office buildings, can be reconverted (taking advantage of their usual central location) to other uses such as commerce, housing, or schools. Finally, it must be said that using technology brings considerable economic benefits to employers, the government, and society, as mobility is expensive for all. As part of the further research that can be performed to complement the current investigation, it will be important to incorporate more socio-economic and demographic factors that help provide a more accurate understanding of the qualitative differences that also affect the degree of accessibility in a given city. By doing so, the relationship between marginalisation levels and other social variables such as income, age, or gender with the level of accessibility will become clearer, providing useful data for developing public policies and urban interventions. Considering new accessibility measures to other destinations as green areas, universities and minor employment centres and more local commerce such as pharmacies or small shops can provide very useful data of the local level configuration of the city in different areas. These additional measures can also help to develop specific public policies based on the topic being analysed; for example, accessibility to green areas. Even though the concept of the 15 minutes city focuses on transport modes such as walking and cycling, it would be relevant to run the accessibility measures using other transport modes, especially public transport and private vehicles. For this to be done, it is important to generate the necessary data, such as GTFS for public transport and a database that provides the average speed for private vehicles per road per time of the day. The previous paragraph encloses a compilation of ideas that could complement and expand the current scope of work. It is important to consider that multiple variables play a dynamic role in the analysis of accessibility in a city, and by understanding this, it becomes clear that the possibilities to analyse accessibility from different perspectives are vast.
## 3 Materials and Methods
### Data Analysis Results  
#### Results Based on Automated Content Analysis  
The _Leximancer_ software tool (www.leximancer.com) analysis for agriculture social networks identified 11 themes and 54 concepts depicted in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Themes** & **Hits** & **Concepts** \\ \hline Social & 5363 & interaction, networking, social media, spectrum of engagement, internet platforms, internet forums, social networks \\ \hline Network & 4379 & link, group, social support, influence, centrality, structural characteristics, participatory programs, relationships, interactions, presence, actions \\ \hline Agricultural & 4274 & research institutes, agribusiness, government, agencies, industry, extension systems, social networks, technology \\ \hline Knowledge & 3979 & information, bridges, interventions, design, management \\ \hline Farmers & 3494 & decision-makers, farmers, investors, traders \\ \hline Systems & 2039 & services, collaboration, production, artificial intelligence \\ \hline Groups & 1715 & clusters, households, farmers, networks \\ \hline Data & 928 & big data, analytics, visualization \\ \hline Model & 807 & infrastructure, machine learning \\ \hline Food & 711 & supply chain, food ecosystem \\ \hline Nodes & 489 & contacts, links, graphs, entities \\ \hline \hline \end{tabular}
\end{table}
Table 5: Themes and concepts of agricultural social networks (Source: _Leximancer_ categorization).  
The _Leximancer_ visualization of ASN concepts in Figure 2 highlighted 11 themes. Table 5 expands further on the themes and also presents the concepts that are associated with each theme. Hits in Table 5 refer to the number of occurrences of a particular concept found within an associated theme. The _social_ theme refers to the social aspect of networks, interactions, and the spectrum of engagement. _Network, nodes, and groups_ highlight the structural characteristics of a group based on the relationships within the group and include clusters of farmers as part of the network. The _agricultural_ and _farmers_ themes confirm the context of the study as the research focused on the agricultural sector (agribusiness) in general and social networks within the agricultural sector specifically. The _knowledge_ theme points to the management of information that may support the design of interventions based on lived experience. The _systems_ and _data_ themes incorporate services and the application of artificial intelligence (AI) in the context of big data and analytics, while _model_ refers to the machine learning (ML) models that may be applied for analytics. The _food_ theme includes the food ecosystem and incorporates the food supply chain associated with ASNs.
## 4 Results and Discussion  
### FIS Execution with Test Data  
Data from a real milling job was used to test and verify the proposed FIS. The set is comprised of data previously treated by removing any outliers and data which could interfere with the system. From this data, a total of 4 datasets were selected to test the FIS. Each of them was manually analyzed and values \(WHP_{sp}\), \(W_{sp}\) and \(CP_{sp}\) were taken from this data. These values were input to the FIS algorithm for each of these datasets. In a real application of this system in a real-time scenario, this manual input can be replaced by and automatic calculation of these parameters. A small dataset was created named Pre-Test Case to be used to initially test the system. Since it was a smaller dataset (20 min of acquisition data), it was faster to run on the Python algorithm. After the first adjustments  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rule** & & **Inputs** & & & **Outputs** \\ \hline
**\#** & **Delta_CP** & **CP\_Deviation** & **CTW\_Deviation** & **PDM Status** & **Flow Restriction** \\ \hline  
L & VL & EL & N \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._and debugging done with the Pre-Test Case dataset, other 3 datasets numbered from 1 to 3 with more data (from 2 h to 4 h of acquisition) were used in the system to verify its functionality and accuracy.  
All 4 datasets were run in the FIS algorithm on Python 3.10 and the output values recorded in an Excel sheet (.xls format). This data was later plotted.