esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Vulnerability Assessment of Historical Masonry Buildings in Croatian Coastal Area
## 5 Conclusions  
This paper presents a procedure for assessing the seismic vulnerability of the historical center of Kastel Kambelovac, one of the settlements of Kastela City located along the Adriatic coast. The settlement has a typical configuration and buildings that are representative of other small and medium historical centers in Dalmatia, not only along the coast, but also inland. A detailed investigation of archival documentation and visual inspections were carried out to detect the main architectural, structural, and material features important for seismic vulnerability assessments. The proposed approach combines the calculation of the vulnerability index through numerical investigations of the behavior of typical buildings with non-linear pushover analysis. This procedure provides vulnerability index maps of the area, critical peak ground accelerations for the calculation of early damage and collapse states, and new vulnerability curves, which relate vulnerability index, peak ground accelerations, and the damage index. The derived vulnerability curves allow us to estimate the damage to buildings in the event of a specific seismic action. The damage indexes were reported here in the form of vulnerability maps of the investigated area for two return periods according to EC8, 475 and 95 years. Damage indexes are fundamental to preventing and managing seismic risk in the relevant area.  
In the present study, we investigated for the first time the vulnerability of stone masonry buildings with flexible wooden ceilings, typical of the Dalmatian coast area. Furthermore, a methodology for the calculation of the damage index based on vulnerability indexes and seismic action in terms of peak ground acceleration was presented. Finally, the described methodology can be applied to and calibrated with other historic settlements.  
Conceptualization, Z.N. and E.B.; data curation, L.R. and N.O.S.; formal analysis, L.R. and N.O.S.; funding acquisition, Z.N. and E.B.; investigation, Z.N., L.R., N.O.S. and E.B.; methodology, Z.N., L.R., N.O.S. and E.B.; software, L.R.; supervision, Z.N. and E.B.; validation, L.R. and N.O.S.; visualization, L.R. and N.O.S.; writing--original draft, Z.N. and E.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by: (1) the EUROPEAN UNION, Programme Interreg Italy-Croatia (Project \"Preventing, managing and overcoming natural-hazards risks to mitigate economic and social impact\"--PMO-GATE); (2) the CROATIAN GOVERNMENT and the EUROPEAN UNION through the European Regional Development Fund--the Competitiveness and Cohesion Operational Programme (Project KK.01.1.1.02.0027).  
Not applicable.  
The authors declare no conflict of interest.
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 5 Proposed Network Deployment
### First Phase: Search
#### 5.1.2 Deployment of Static Networks of Different Sizes  
These derived parameters can be used to guide UAVs to create a static network of different sizes. For example, Figure 2(a) shows 10 randomly distributed UAVs, where each UAV starts at the locations indicated by the blue dots. The UAVs then move under the influence of attractive and repulsive potential fields from neighbors using the above parameters, and Figure 2(b) shows the resulting static network when the UAVs stop moving. This positioning is identical to Delauney triangulation [15], and this shows that potential-field-based staticnetwork deployment can subsume Delaney-triangulation-based network deployment. Furthermore, potential field parameters can also be derived to deploy networks similar to CPT [10]. Note that there exist multiple sets of values of the coefficients and exponents that will result in similar static network deployments.  
The same parameters can also be used to create static networks with different numbers of UAVs. For example, 156 UAVs are deployed in an AOI of \(2000\times 2000\) m\({}^{2}\), where UAVs start within an area of \(10\times 10\) m\({}^{2}\) near the center of the AOI. The search phase runs for 1500 time steps and Figure 4 shows the UAVs' positioning (green circles) at the end, where users (blue dots) are distributed in three clusters. Green circles show UAV coverage areas with the UAV in the center of each circle, and the red circle represents the command center. A black circle shows a UAV serving no users within \(U_{gr}\), thus acting as an inactive UAV. Similarly, the magenta circle shows a UAV serving multiple users and is an example of an active UAV. Increasing the number of UAVs that serve the same users leads to bandwidth sharing that decreases the active UAV energy consumption and thus increases the network longevity. The second phase of the network deployment seeks to increase the number of active UAVs by moving UAVs from areas with lower bandwidth requirements (fewer users) to areas with higher bandwidth requirements (more users) while maintaining network connectivity with the command center.  
Figure 4: Deployment of UAVs at the end of the first phase on the third training scenario. Blue dots represent users, green circles represent UAVs’ coverage areas, and the red circle represents the location of the command center. A black circle shows an inactive UAV and the magenta circle shows an inactive UAV.  
Figure 3: UAVs moving under the influence of potential fields with parameters (\(c_{a},c_{a},c_{r},e_{r}\)) as (\(1\), \(1\), \(3\times\sqrt{3}\times 100^{3}\), \(-2\)) to create a network with zero coverage gap.
# Model-Based Analysis and Improvement of Vehicle Radiation Emissions at Low Frequency
## 4 Model-Based Analysis and Improvement
### Importance Analysis  
To introduce the importance analysis of technical factors succinctly, it is assumed that the interferences related to all \"Interference sources\" and \"Sensitive equipment\" are described in the form of voltage. For each factor, its influence on the EMC problem is evaluated from two aspects, i.e., contribution degree and sensitivity. The former reflects the contribution on the interference voltage and is calculated by:  
\[\Delta_{i}=U-U_{r,i} \tag{7}\]  
where the subscript \(i\) is the index of the considered technical factor, \(\Delta_{i}\) is the contribution degree, \(U\) and \(U_{r,i}\) are the original interference voltage and that calculated by Equation (6) when the factor \(i\) is set to be zero, respectively. A larger \(\Delta_{i}\) implies that this factor contributes more on the radiated emission and it should be improved technically.  
The contribution degree is established with the assumption that each factor can be attenuated totally, which is impractical in engineering. So besides the contribution, another index called sensitivity is introduced to evaluate the improvement potential of the technical factor:  
\[S_{i}=\partial U/\partial F_{i} \tag{8}\]  
where the subscript \(i\) is the index of technical factor whose sensitivity is \(S_{i}\), and \(F_{i}\) denotes the considered factor including the interference voltage of sources and coupling coefficients in the network. Essentially, sensitivity indicates the effect by improving the factor with unit quantity at the current state.  
To comprehensively consider these two totally different indexes, EWM is adopted to measure the importance of each factor:  
\[\begin{array}{l}S_{c,i}=\omega_{1}x_{i1}+\omega_{2}x_{i2}\\ x_{i1}=\frac{\Delta_{i}-\min_{i}\Delta_{i}}{\frac{\Delta_{i}-\min_{i}\Delta_{ i}}{\max_{i}\Delta_{i}-\min_{i}\Delta_{i}}},\;\;x_{i2}=\frac{S_{i}-\min_{i}S_{i}}{ \max_{i}\Delta_{i}-\min_{i}S_{i}}\end{array} \tag{9}\]  
where \(S_{c,i}\) is the evaluation score of factor \(i\) which measures the importance, \(x_{i1}\) and \(x_{i2}\) are the Min-Max normalized values of the contribution degree and sensitivity, respectively. The weight coefficient, \(\omega_{j}\), is calculated as the following by EWM [17]:  
\[\omega_{j}=\frac{1+\frac{\sum_{i=1}^{n}\left(P_{ij}\cdot\log_{10}P_{ij}\right) }{\ln n}}{\sum_{j=1}^{2}\left[1+\sum_{i=1}^{n}\frac{\left(P_{ij}\cdot\log_{10 }P_{ij}\right)}{\ln n}\right]},P_{ij}=x_{ij}/\sum_{i=1}^{n}x_{ij} \tag{10}\]  
A larger evaluation score implies that technically the corresponding factor contributes more on the radiated emission.
## 4 Results and Discussion
### Comparing the Achieved Total Request Durations Across Different Network Scenarios  
A comparison of the total request duration for each network configuration is shown in Figure 15. From this comparison, we can see that the reliability of the network has a large effect on the spread of the reported latency. In the case of the mobile hotspot, outliers often introduce greater latency than the connection between Finland and the United States. This may be the result of connecting through a cellular network in a congested campus environment, but it shows a distinct disadvantage for use in populated areas without dedicated infrastructure.  
In contrast, the other network scenarios we evaluated provided high-reliability connections for both the operator and robot networks. As a result, these networks had far lower variability in their resulting latency. This did not, however, prevent the outliers from occurring. In both the wired and Wi-Fi tests, at least one sample took nearly 100 ms in the wired network and 130 ms in the Wi-Fi network. The main difference is that the latency is still at or only slightly above the control message interval of the control loop. Given these measurements, both network use cases are capable of low latency and reliable control.  
However, the overseas connection tells a slightly different story than the other networks. While the latency is much higher, the stability of the network still shows reliable latency, with an average of 240.769 ms. This may make control more challenging, as the robot will only move just over four times per second, but the reliability shows that tasks such as grasping an object are feasible. Table 5 shows the combined data of the mean, median, standard deviation, and confidence interval for each network test. These results tell a similar story to the presented box plot, as the standard deviation and confidence interval are far greater during hotspot testing than with networks with more reliable connections. An interesting note is that the standard deviation of the overseas connection more closely matches the wired local connection, suggesting that Wi-Fi connectivity may play a role in some latency variation.  
Figure 14: Box plot of camera feed latency across different network scenarios.
## 2 Materials and Methods
### Data Collection Process  
The questionnaires were first distributed to the selected participants in the waiting area of the dental clinics. After questionnaire distribution, these patients were numbered and randomly assigned into the three groups using Randomizer software ([[https://www.randomizer.org](https://www.randomizer.org)]([https://www.randomizer.org](https://www.randomizer.org)); accessed on 12 December 2019). One group did not undergo any intervention (control group), and the participants in the next two groups received either auditory distraction or brief relaxation therapy. The intervention was carried out when each patient was sitting on the dental chair before the tooth extraction procedure. Group 1 (the auditory distraction group) was asked to select their favorite music composition or religious recitation that they would like to hear before and during the tooth extraction procedure. Sanitized ear pods or headphones were provided, and the requested audio was played for at least 15 min using a mobile phone. For group 2 (the brief relaxation therapy group), a trained physiotherapist carried out the process of brief relaxation, as adapted from [PERSON] and colleagues [38; 40], which comprised the following instructed steps: (1) Let your lower jaw fall loosely and move it easily from right to left for three to five seconds. (2) Move the joints of your head and neck smoothly so that your head nods slightly from one shoulder to the other for three to five seconds. Let gravity do the work. Do you notice a change in awareness of your neck? (3) For three to five seconds, move your relaxed shoulders in a circular motion in their joints in such a manner that another person would barely see your movements. Let gravity work for you. (4) Pay attention to your awareness of your body. Do you notice any variation? (5) Beginning with your backbone, move your body loosely from side to side and from back to front for three to five seconds. Imagine that your chest is suspended from many flexible small joints. Let your ribs fall with gravity. Feel the flexibility of your chest. Notice the sensation inside your chest. (6) Pay attention to your body and be conscious of your bodily experiences. Notice your flexibility. (7) You do not need to worry about doing something wrong. The brief relaxation therapy process took approximately 15 min.  
The final group (control group) did not undergo any intervention and the dental practitioner carried out the extraction in a manner that is commonly practiced in most dental clinics. One oral and maxillofacial surgeon carried out the non-surgical extraction procedures for all the recruited participants at their scheduled appointments. The patient's level of anxiety was assessed again soon after the intervention, and before the procedure, using the same questionnaire. The age and sex of the study participants were also recorded using a self-administered questionnaire. To address any potential confounding related to inter-operator calibration, all extractions were carried out by one specialist oral surgeon following the standard clinical (procedural) and infection control protocols.
## 5 Discussions  
### Drought Sensitivity of SIF and VIs  
The time-lag between reflectance based vegetation and drought indices and the precipitation deficit is significant and has been widely discussed. Although the NDWI was previously found to be more sensitive to drought than the NDVI [13] (a similar conclusion could not be reached in this study), they both fail to indicate rapid changes in drought stress because they have no direct link to photosynthetic functioning beyond their sensitivity to changes in canopy structure and pigment concentration [17]. Alternatively, SIF is directly related to photosynthesis and water stress has been detected by SIF in both ground (e.g., [48; 49]) and aerial studies (e.g., [22; 31]). Thus the decline of photosynthesis due to drought stress is expected to be reflected by satellite-measured SIF.  
The significant correlations between SIF and multi-time-scale SPIs (Figures 3-5) indicate that GOME-2 SIF has good drought sensitivity. Table 3 shows the correlation coefficients between the NDVI and the multi-time-scale SPIs during the peak growing season. For most cases SIF gave higher correlations to SPI-1 and SPI-2 than the NDVI. Thus, regarding the result shown in Figure 6, SIF might be more sensitive to short-term precipitation but less sensitive to long-term precipitation than VIs, because the photosynthetic activity is mainly affected by the current water stress conditions while the green biomass or the canopy absorption of the spectrum reflects the growing status, which is influenced by water conditions all through the growing season. The more significant reduction of SIF (Figure 10) than NDVI (Figure 11) indicates that SIF is more drought-sensitive because SIF reduction results from the reduction of both FAPAR and SIF\({}_{\text{yield}}\)[30] whereas the NDVI only reflects the FAPAR  
Figure 11: The reduction of the normalized difference vegetation index (NDVI) from May to October in 2012.  
reduction. In addition, for some CDs (e.g., CDs 1406 & 1409 and CDs 2501 & 2502) during the peak growing season, the correlation between SIF and the PDSI was more significant than that between SIF and the SPI-1, indicating that SIF might be more sensitive to the combined stress from both high temperatures and scant precipitation than it is to water stress alone. This can be explained by the fact that both precipitation and temperature play critical roles in plant growth and heat-induced drought can lead to further declines in GPP and yield. The sensitivity of SIF to short-term drought stress provides an opportunity to employ SIF to monitor drought at an earlier stage. The similarity of the spatial pattern and temporal variation shown in Figures 9 and 10 also shows that SIF can track the evolutionary process of drought in a timely manner. Thus, satellite-measured SIF is a promising remotely-sensed index for drought monitoring that directly indicates drought-induced restrictions for photosynthesis.
## 2 Methodology
### Model Validation
#### 2.2.3 Numerical Details  
The computational domain has the same size as the actual experiment facility (1.0 m long \(\times\) 0.5 m wide \(\times\) 0.75 m high) and includes the hydrogen sensor. Boundary conditions such as the leakage flow rate and pressure conditions were applied in the same manner as the experimental conditions. After the geometric model was established, a tetrahedral mesh was generated with a total of 492,406 elements. For the hydrogen leakage and diffusion analysis, the internal flow was assumed to be a three-dimensional, unsteady, incompressible, and turbulent flow. Furthermore, the SIMPLEC algorithm was employed for calculating the pressure and velocity [19; 20]. The overall simulation time was 240 s.
# A Smart Parking Solution Architecture Based on LoRaWAN and Kubernetes
###### Abstract  
Finding a parking space in a city is one of the most common activities of a driver. This becomes more difficult when the city is unknown or has huge vehicular congestion. A solution to address this issue is called smart parking. Smart parking solutions rely on Internet of Things (IoT) and several technologies to achieve their purpose. This paper proposes an architecture for deploying a smart parking solution based on Long-Range Wide Area Network (LoRaWAN) sensors, LoRaWAN and a cluster of Kubernetes. This approach provides an open architecture able to share information with other parties through a REST API interface. Likewise, it contains a mobile and a web application for user interaction. This solution provides an administration interface for managing parking lots. The user interface lets a user to find, view information, display available spaces and rate a parking lot in real time. This solution could be used as an application as service parking system. The proposed architecture is fully portable and scalable due to the use of Kubernetes.  
IoT; smart parking; architecture; LoRaWAN; Kubernetes; MQTT; MongoDB
## 3 Empirical Results  
### Spatial Autocorrelation Analysis  
The global Moran's \(I\) of environmental regulation, financial support for agriculture and agricultural green development is shown in Figure 1.  
According to the results of the global Moran's \(I\) test, most of the Moran's \(I\) of environmental regulation, financial support for agriculture and agricultural green development from the years 2000 to 2020 have positive correlation and certain agglomeration effects, only showing a negative correlation in a few years, which may be due to the provincial heterogeneity of global Moran's \(I\)[40]. The global Moran's \(I\) of environmental regulation is distributed in the range of [0.04-0.276], which basically fluctuates around 0.16. Meanwhile, the global Moran's \(I\) of financial support for agriculture is distributed in the range of [\(-\)0.089-0.174], and the global Moran's \(I\) of agricultural green development is distributed in the range of [\(-\)0.067-0.232], both of which show obvious fluctuations while tend to be stable as a whole.  
According to the relevant principles of local Moran, the value of environmental regulation, financial support for agriculture and agricultural green development in the year 2020 is selected to draw the local Moran scatter plot based on the geographical adjacency matrix. The scatter plot could describe the local variations of these three variables more intuitively (Shown in Figure 2).  
The scatterplot shows that the scatter of the geographical adjacency matrix of agricultural green development has a balanced distribution in each quadrant, while environmental regulation scatters are mainly distributed in the second quadrant (low-high concentration) and the third quadrant (low-low concentration), and agricultural financial subsidies scatter are mainly distributed in the first quadrant (high-high concentration), the second quadrant (low-high concentration) and the fourth quadrant (high-low concentration). In general, there are significant spatial correlations among provinces in environmental regulation, financial support for agriculture and agricultural green development.  
Figure 1: Moran’s \(I\) index test results.
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 3 Results
### Datasets
#### 3.1.3 Spmcs  
We also utilized the SPMCS [17] dataset for evaluation purposes. This dataset encompasses 30 video sequences, each comprising 31 successive frames and sporting a resolution of 540 \(\times\) 960. It includes input images subsampled at x2, x3, and x4 factors, in addition to high-resolution raw images. The variety of subsampling rates and the higher-resolution frames make this an ideal dataset for evaluating our super-resolution model.
## 6 Computing in Vehicular Networks
### Onboard  
The primordial model for computing in earlier networks was the employment of onboard computing units. Those units, however, were limited in resources; they could not swiftly process large quantities of data collected from the sensors and make appropriate decisions such as those required for autonomous driving [108]. However, the improvement of V2V communication has enabled the sharing of resources among vehicles. This denotes the usage of the available resources of another vehicle by a different vehicle, thus forming a variation of an ad hoc cloud [13]. Even though this model is flexible and potentially allows for performance improvement by offering a larger set of resources for applications in vehicles, the high mobility of nodes and the resulting high variability of the topology of vehicular networks make it arduous to fully utilize shared resources [108]. Communication among vehicles can be disrupted at any time, thus resulting in a loss of access to shared resources. In addition, vehicles offering shared resources are often unknown, thus requiring authentication and security measures to ensure the safety of data.  
To alleviate these drawbacks, methods aimed at providing reliable resource sharing are being designed. There are approaches using the digital twin in the cloud for modeling the physical states of vehicles in the network with the real-time synchronization of data such as location, velocity, or available fuel [109]. The location of a digital twin in a remote cloud facilitates the usage of artificial intelligence and allows for the digital twin to act as a broker for intravehicular communication to secure resource sharing among vehicles. In this system, a vehicle communicates with its digital twin, which then can retrieve data from the digital twin of a different vehicle. Even though this approach is not limited by the proximity of vehicles, as the communication takes place via digital twins, vehicles have to transmit data to a cloud, which can introduce an unwanted latency.  
The other approaches introduce vehicular edge computing to share resources. In this case, the edge computing integration into vehicular networks is conducted via resources that are already available in the network. This means that rather than deploying dedicated edge computing servers, it involves utilizing the resources in vehicles to act as an edge node. Such an approach benefits from the geographical proximity of nodes, which may reduce latency but also may cause a disruption of service due to the limited timeframe in which they are able to interact via direct communication [110].