esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
###### Abstract  
The miniaturization of electronics, sensors, and actuators has enabled the growing use of nanosatellites for earth observation, astrophysics, and even interplanetary missions. This rise of nanosatellites has led to the development of an inventory of modular, interchangeable commercially-off-the-shelf (COTS) components by a multitude of commercial vendors. As a result, the capability of combining subsystems in a compact platform has considerably advanced in the last decade. However, to ascertain these spacecraft's maximum capabilities in terms of mass, volume, and power, there is an important need to optimize their design. Current spacecraft design methods need engineering experience and judgements made by of a team of experts, which can be labor intensive and might lead to a sub-optimal design. In this work we present a compelling alternative approach using machine learning to identify near-optimal solutions to extend the capabilities of a design team. The approach enables automated design of a spacecraft that requires developing a virtual warehouse of components and specifying quantitative goals to produce a candidate design. The near-optimal solutions found through this approach would be a credible starting point for the design team that will need further study to determine their implementation feasibility.  
C 2020; 7 142; doi:10.3390/aerospace7100142
## 3 Results of the Survey
### Management of Adult Patients with Asthma in the ED at the Time of COVID-19 Pandemic
#### 3.2.2 Asthma and COVID-19: What We Know Today
Experiences during the COVID-19 pandemic suggest that MDI was comparable to nebulizers (in terms of efficacy and patient acceptance) in treating mild to moderate asthma exacerbations in the ED [58]. However, there is no concrete evidence of a difference in aerosol and droplet generation between these two routes of inhaled drug delivery (MDI vs. nebulizers) [23]. Given this lack of evidence, we recommend using the available devices to minimize the delay in drug delivery, preferring the MDI if possible [59; 60]. In the ED, patients with severe asthma should be treated with inhaled \(\beta\)2-agonists (e.g., salbutamol) and inhaled anticholinergics (e.g., ipratropium bromide) with continuous or repeated nebulization. Concomitant oxygen supplementation is recommended when saturation is <90%. There are no significant differences regarding this cut-off in the case of concomitant COVID-19 [54; 55; 56]. Administration of systemic corticosteroids should occur early, possibly within 1 h of admission to the ED [61; 62; 63]. As aforementioned, again, nothing changes if the patient has concomitant SARS-CoV-2 infection, since the potential better clinical outcome of severe COVID-19 with corticosteroids administration. Intravenous (IV) magnesium sulfate [64], \(\beta\)2-agonists (e.g., salbutamol) and methylxanthines (e.g., theophylline) can be added if clinical improvement is incomplete or absent, up to subcutaneous (SC) administration of epinephrine [17; 65; 66]. The criteria for intubation of asthma patients remain unchanged. Obviously, asthmatics with concomitant SARS-CoV-2 infection complicated by acute respiratory failure and criteria for ARDS also require aggressive respiratory support with intubation, invasive ventilation and admission to ICU [2; 65; 66]. There is limited evidence that antibiotic therapy may improve symptoms with respect to standard care or placebo, in asthma exacerbation. Literature evidence is even insufficient to reach firm conclusions about some important outcomes, such as hospital admission and mortality. For these reasons, we recommend not administering antibiotics in patients with asthma exacerbations unless there is clear evidence of bacterial infection/superinfection [67]. Table 1 shows a comparison of some major issues related to asthma management before and during the COVID-19 pandemic.
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 2 Materials and Methods
### Image Processing of UAV Images  
#### 2.3.1 Implementation of Bilateral Filter  
The first step in image processing was the enhancement of all UAV images, using a bilateral filter. Bilateral filter, a type of time-domain non-linear filter, reduces the noise with edge preservation by restoring the intensity of every pixel with weighted intensity values of surrounding pixels [33]. A bilateral filter uses the Gaussian distribution for calculating the weighted intensity values with an additional component, which is a function of pixel intensity difference [34]. This additional component ensures it used only weighted intensity values in computing blurred intensity values. The bilateral filter is defined as:  
\[I_{filtered}=\frac{1}{W_{P}}\sum_{x_{i}\in\Omega}I(x_{i})f_{r}(\|I(x_{i})-I(x)\| )g_{s}(\|x_{i}-x\|) \tag{1}\]  
\(W_{P}\) is the normalization term defined by:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **DJI Mavic Pro Camera** \\ \hline Sensor Type & \(1/2.3^{\prime\prime}\) CMOS Sensor \\ Million Effective Pixels & 12.34 \\ Image Size & 4000 \(\times\) 3000 \\ Lens & 35 mm \\ ISO range & 100–1600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the DJI Mavic Pro camera.  
Figure 1: Localization of Kerian River and its tributaries in Malaysia.  
\[W_{P}=\sum_{x_{i}\in\Omega}f_{r}(\|I(x_{i})-I(x)\|)g_{s}(\|x_{i}-x\|) \tag{2}\]  
In Equations (1) and (2), \(I_{filtered}\) is the filtered image, \(I\) is the input image, \(x\) are the coordinates of the current pixel, \(\Omega\) is the window centered in \(x\), \(x_{i}\)\(\epsilon\)\(\Omega\) is another pixel, \(f_{r}\) is the range kernel and \(g_{s}\) is the spatial kernel. Allocation of the weight \(W_{P}\) uses \(f_{r}\) and \(g_{s}\)[34].  
If a pixel located at \((i,j)\) is to be denoised in image using surrounding pixels and we consider one of its neighbors at \((k,l)\), then the weight assigned to a pixel at \((k,l)\) to denoise a pixel at \((i,j)\) is given by:  
\[w(i,\ j,\ k,\ l)=\ \exp(-\frac{(i-k)^{2}+(j-l)^{2}}{2\sigma_{d}^{2}}-\frac{\|I( i,j)-I(k,l)\|^{2}}{2\sigma_{r}^{2}}) \tag{3}\]  
In Equation (3), \(\sigma_{d}\) and \(\sigma_{r}\) are smoothing parameters, whereas \(I(i,j)\) and \(I(k,l)\) are pixel intensities at \((i,j)\) and \((k,l)\), respectively [34]. We calculate the denoised intensity of pixel, \(I_{D}\) at \((i,j)\) after evaluating the weights and normalizing them using the equation:  
\[I_{D}(i,j)=\frac{\sum_{k,l}I(k,l)w(i,j,k,l)}{\sum_{k,l}W(i,j,k,l)} \tag{4}\]  
Bilateral blur can fragment the image into different scales [35]. Along with edge preservation, bilateral filter finds application in denoising, tone mapping, tone management, data fusion, retinex, and texture and illumination separation [36]. The present work required denoising, which the bilateral blur serves, and hence we select it for this study. We did not detect the staircase effect and gradient reversal in the filtered image in this work, which are the limitations of the bilateral blur.
# Eyelid Dynamics Characterization with 120 GHz mmW Radar
## 5 Eyelid Results
### Slanted Observations  
Another way to reduce the background contribution of the face is to observe the eye at a slanted angle. In our case, the radar was relocated below the face level, observing the eye with a slanted antenna beam pointing upwards with an angle of \(55^{\circ}\) with respect to the horizontal direction. In this slanted direction of observation, the parts of the face orthogonal to the radar observation are reduced up to the point that the face's contribution to the measured echo is no longer dominant. Under this condition, the phase rotation of the eyelid can be observed without requiring background cancellation. Figure 10 shows a complete recording of 24 s, where a blink can be observed at \(t=1\) s, the eye is voluntarily closed in the interval of 3 s to 6 s, and this is followed by six normal blinks until the recording ends, for \(t=24\) s. The phase term was converted into a relative range offset in mm. The measurements indicate a typical closure range offset of nearly 5 mm, which is likely influenced by the movement of the skin around the eye. In Figure 11, it is possible to observe one blink in more detail. Based on the details observed in this measurement, we can determine the average blinking period using Figure 10 and examine the dynamics of a single blink using Figure 11. For instance, the average blinking period and standard deviation were calculated using the last six blinks measured in the subject, and additional dynamics parameters are presented in Table 2. Furthermore, a fast-speed video was recorded in synchrony with the radar acquisition. Slow-motion video  
Figure 8: Phase (blue line) and magnitude (red line) of the eye spot backscattering. The radar is located at a 1 m distance in front of the subject’s face, observing the eye area with a horizontal beam. Several blinks can be observed, producing fast magnitude and phase oscillatory transients.  
reproduction allowed us to relate the video-recorded eyelid closure/aperture timing with the timings of the dynamics parameters obtained from the radar observations in order to validate the proposed technique.
# Design and Analysis of a Planar Six-Bar Crank-Driven Leg Mechanism for Walking Robots
## 2 Description of the Proposed Mechanism  
Figure 1 shows the proposed planar single-DOF six-bar mechanism, which includes an integrated skew pantograph. This mechanism represents an advanced kinematic chain that has found extensive application within various engineering domains requiring the precise transmission and scaling of motion. Diverging from the conventional pantograph, this intricate variant incorporates additional linkages that endow it with the capability to trace more complex motion paths, extending its functionality into three-dimensional operations. A notable characteristic of the six-bar skew pantograph is its duality in motion manipulation, whereby it can amplify or reduce input movements through adjustments in the lengths of the bars and their respective motions, a feature crucial in applications requiring varied motion outputs [14]. The precision with which these pantographs operate is imperative, especially in precision engineering contexts where exact replication of motions is demanded; this is achievable through the meticulous design and manufacturing of the mechanism [15; 16; 17]. Moreover, the versatility of this six-bar linkage is evidenced by its numerous configurations, each tailored to fulfill a wide array of motion profiles, which substantiates its broad applicability across different technological fields [18].  
Figure 1: Scheme of the proposed mechanism.  
The main idea behind selecting this design is its capability of generating amplified four-bar coupler curves at its output point. This is particularly useful in applications such as walking machine leg mechanisms, for which a compact mechanism with a relatively large output coupler curve is needed [19]. Additionally, this leg is designed to mimic the trajectory pattern of an amphibious leg, which is characterized by a linear supporting phase.
## 2 Methodology
### Microzonation  
In this study, microzonation, which involves reducing a large area into smaller areas, was accomplished using Geographic Information System (GIS) software. The area of each zone was calculated, and any large zones with an area greater than 1 km\({}^{2}\) were further subdivided into smaller zones. Once the areas of the zones met the requirements of this study, their centroids were located using a latitude and longitude format.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Regulation** & **Date** & **CO** & **NOx** & **HC a** & **HC+NOx** & **PM** & **PN** & **Cycle** \\ \hline
**Gasoline \& LPG b** & & & & & & & \\ \hline ULEV & January 2013 & 1.31 & 0.044 & 0.034 & & 0.004 c & FTP-75 d  \\ \hline
**ULEV** & January 2016 & 1.31 & & & 0.078 & 0.004 & FTP-75 \\  & & & & & & & US06 e, SC03 f  \\ \hline
**Diesel 8** & & & & & & & & \\ \hline EURO 3 & July 2002 & 1.1 & 0.95 & 0.22 & & 0.11 & FTP-75 \\ \hline EURO 4 & January 2006 & 0.74 & 0.39 & & 0.46 & 0.06 & NEDC h  \\ \hline EURO 5 & September 2009 & 0.74 & 0.28 & & 0.35 & 0.005 & NEDC  \\ \hline EURO 6 & January 2014 & 0.5 & 0.08 & & 0.17 & 0.0045 & \(6\times 10^{11}\) km\({}^{-1}\) & NEDC \\ \hline \hline \end{tabular}  
* Hydrocarbons (HC) content is measured at the tailpipe. HCs are expressed as non-methane organic gas (NMOG) (non-methane hydrocarbons(NMHC) multiplied by 1.04) for gasoline and liquefied petroleum gas (LPG), whereas HCs for diesel are expressed as NMHC. b Lightweight vehicles, small and midsized passenger cars, and trucks. Additionally, emission tests performed on used vehicles that are more than 5 years old or have run more than 80,000 km. c For vehicles equipped with gasoline direct injection (GDI) engines certified after January 2014. d Federal Test Procedure (FTP-75) is defined by the US Environmental Protection Agency (EPA). e US06 is a high acceleration aggressive driving cycle. f SC03 is the air conditioning cycle under hot ambient condition. g Small and midsized passenger cars and trucks. h New European Driving Cycle (NEDC). e Remark for gasoline- and LPG-powered vehicles. Large-sized passenger cars and trucks as well as extra-large passenger cars and trucks: NH\({}_{3}\leq\)10 ppm after January 2013. Large-sized passenger cars and trucks as well as extra-large passenger cars and trucks: CH\({}_{4}\leq\)0.5 g/kWh after January 2013. e Remark for diesel-powered vehicles. Large-sized passenger cars and trucks as well as extra-large passenger cars and trucks as well as extra-large passenger cars and trucks as well as extra-large passenger cars and trucks as well as extra-large passenger cars and trucks: NH\({}_{3}\leq\)10 ppm as per EURO 6 certification.  
\end{table} 
Table 1: Emission standards in Korea(unit: g km\({}^{-1}\)). emissions must, therefore, be more comprehensive instead of focusing exclusively on pollutant emissions. To address this concern, the proposed study measures and analyzes emission trends concerning a wide range of regulated gaseous pollutants, greenhouse gases, and particle-precursor gases released from vehicles fueled with gasoline, LPG, and diesel. Further, gasoline vehicles have been classified based on the engine technology (GDI or multi-point injection (MPI)) incorporated therein, whereas diesel vehicles have been classified based on European emission-regulation standards--namely EURO 3, EURO 5, and EURO 6.
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 6 Discussion
### The Influence of the 24 h Period  
From Figure 2, we can find there exists a 24 h period in the IPv6 traffic change, which is verified by the good performance of Naive-24h. Therefore, the 24 h traffic difference, which means the difference between the traffic volume and the traffic volume 24 h ago could be an alternative prediction choice for improving the model performance.  
In this subsection, we predict the 24 h traffic differences rather than the exact volume of future traffic to study the influence of the 24 h period. We take the predicted 24 h traffic difference plus the traffic volume 24 h ago as the predicted traffic volume, and then calculate the model performance in the MAPE metric. As shown in Table 6, the MAPE (24 h) is the model performance of predicting the 24 h traffic differences while the MAPE (Directly) is the model performance of predicting the traffic volume directly. The baseline machine learning models' performance is significantly improved by predicting the 24 h traffic differences as it can eliminate the period influence.  
However, the LS6 model does not perform better while focusing on predicting the 24 h traffic differences. Still, the LS6 model predicting the exact volume of the IPv6 traffic performs best among all models in the two training approaches. We believe it is because the SARIMA in the LS6 can make good use of the period information. Eliminating the 24 h period influence might not be beneficial for our LS6 model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MAPE** \\ \hline LS6 (w/o SARIMA\_v6) & 0.4882 \\ LS6 (w/o SARIMA\_v4) & 0.4108 \\ LS6 (w/o LSTM\_v6) & 1.2277 \\ LS6 (w/o LSTM\_v4) & 0.4045 \\ LS6 (w/o IPv4) & 0.3569 \\ LS6 (w/o SARIMA) & 0.3776 \\ LS6 (w/o LSTM) & 0.7950 \\ LS6 & **0.3146** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction performance of LS6 and variants (ECNU).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Model** & **MAPE** \\ \hline DHU & LS6 & 0.2410 \\  & LS6 (combine) & **0.2317** \\ \hline ECNU & LS6 & 0.3146 \\  & LS6 (combine) & **0.2953** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction performance of LS6 trained with a single training data and combined training data.
## 2 Related Work
ApplicationsOur considering UAV network is beneficial in many applications due to its scalability in terms of mobile deployment. First of all, the use of UAV networks for 5G and beyond 5G network applications is actively discussed nowadays in industry and academia [11; 23; 25]. In addition, the use of UAV networks for surveillance, security, and rescue tasks, is also to actively under discussion, as presented in [53; 54; 55; 56; 57]. Moreover, distributed deep learning and federated learning applications of UAV networks is introduced in [58]. Furthermore, software-related research results in UAV networks and systems are well summarized in [59; 60]. Lastly, a mobile charger study in [61] can be related to UAV charging algorithms, including the proposed algorithm in this paper. However, the mobile charger research in sensor networks has several differences from our proposed UAV charging algorithm: (i) mobile chargers are moving in order to charge power-hungry sensor network devices whereas our proposed algorithm lets the UAVs move to be charged by ground-mounted charging towers; and, (ii) mobile chargers are energy-limited, whereas charging towers in this paper are AC-powered(that is more realistic, because there is no specific description on how the mobile chargers can be charged in [61]).
# A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks
## Proposed Method  
### Motivation  
Until now, the Bluetooth generating architectures can generally be classified as having line, tree, ring or mesh network topologies. While the ring architecture is simple and suitable for proactive routing, it produces longer path lengths for packet transmission, especially when the size of the ring network increases. The routing protocol on a larger ring subnet needs to consider the routing path length design issue to mitigate the packet delay since most previous studies consider one-way routing in the ring subnet [25; 26].  
On the other hand, the mesh architecture reduces path length in terms of packet delay performance, but introduces larger scatternet formation complexity than other topologies. Another important issue in a Bluetooth scatternet is routing with a formed scatteret. Current routing solutions for mesh networks, including proactive, the reactive, and hybrid algorithms, discuss protocol design without considering the ease of routing in such a scatternet. As a result, the routing protocol with formation design [27] is challenging, due to existing performance tradeoffs between formation complexity and packet delay.  
In contrast to prior works on Bluetooth scatternet formation and routing algorithms, a RMR algorithm is designed to jointly address the problems of achieving the simple routing and determining the optimal placement of mesh links for a multi-hop scatternet. With local topology,a ring subnet is generated first and each master in the ring subnet forms its piconet individually. A peak-search method is then introduced in the formation phase to determine the optimal placement of available mesh links by interconnecting masters in the ring subnet, thus achieving better network capacity. Figure 1 illustrates an example of the RMR configuration that satisfies the design objectives to generate networks into desired mesh-ring subnets.
## 4 Discussion
### Targeted ROI Processing Approach
Since K-means clustering requires many sampling points, the random sampling points in the targeted ROI approach could pick up small rice fields. This approach had an advantage in the classification of fragmented rice fields. However, accurate rice field regions need to be known beforehand in order to implement ROI-targeted processing. Figure 8: Visual comparison between this study’s rice (left sides) and the NESEA-Rice10 maps (right side); (**a,b**) show a comparison of the separability of rice and non-rice areas; (**c,d**) show a comparison of salt and pepper effects. The red colour is regions classified as rice field areas; comparison location is in Selangor state, Malaysia (3\({}^{\circ}\)27\({}^{\circ}\)5.83\({}^{\prime\prime}\)N, 101\({}^{\circ}\)7\({}^{\prime}\)41.18\({}^{\prime\prime}\)E).
# An Interoperable UMLS Terminology Service Using FHIR
## 5 UMLS FHIR Terminology Service: Design and Implementation
### Architecture and Impenetation: UMLS FHIR Terminology Service  
The FHIR specification integrates REST with the standard to manage resources and other related entities. The HAPI library has a set of predefined REST servers [36], and this terminology service architecture uses the HAPI plain REST server. As an implementation of the FHIR standard, the HAPI REST servers honor the FHIR Restful specifications [10]. The architecture, shown in Figure 8, is composed of three core entities:  
* A user-facing client application written in HTML and JavaScript;
* UMLS FHIR API developed using HAPI REST Server, running on the Apache Tomcat web server;
* A MySQL database for UMLS.  
The terminology service can be accessed in two ways: a web application with a simple user interface, available at [[http://umls.it.ilstu.edu/umlsfhir/](http://umls.it.ilstu.edu/umlsfhir/)]([http://umls.it.ilstu.edu/umlsfhir/](http://umls.it.ilstu.edu/umlsfhir/)), or via an API, available at [[http://umls.it.ilstu.edu/umlsfhir/fhir](http://umls.it.ilstu.edu/umlsfhir/fhir)]([http://umls.it.ilstu.edu/umlsfhir/fhir](http://umls.it.ilstu.edu/umlsfhir/fhir)). The web application uses the API. The web interface allows users to search for a concept using its name, where an autocomplete algorithm presents the search results based on the  
Figure 8: Architecture of UMLS FHIR terminology service.  
Figure 7: Profiled FHIR ConceptMap as a UML class diagram with designed FHIR extensions (Figure 6) as attributes (highlighted).  
typed string. This search result is structured using the FHIR CodeSystem resource and returned to the user as an FHIR Bundle resource. From Section 3.2.3, CodeSystem is used to declare the existence of a code system, such as UMLS, and optionally define a part of its content. As the user search results are a subset of UMLS, the CodeSystem resource is used for an interoperable representation of the search results. When the user selects a term from the results, the selected term's knowledge is returned as ConceptMap. Using the HAPI documentation, the REST operations on resources are performed through providers [37]. The server defines two providers: ConceptMapProvider, which allows read operation of profiled ConceptMap (Figure 7), and CodeSystemProvider allows the search operation on the CodeSystem. The Database module, a plug-in module, queries the locally installed MySQL UMLS database.