esa-sceva/satcom-chunk-collection huggingface dataset:
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 2 Materials and Method
### Local Tide and Storm Surge Data  
Tidal data for comparison with the GNSS buoy results were obtained from the UK Environment Agency (EA) tide gauges at Liverpool (WGS: 53 deg26'58.9'' N, 03 deg01'5.2'' W, British National Grid: SJ 3248 9525) 50 km to the south, and Heysham (WGS: 54 deg01'54.5'' N, 02 deg55'13.4'' W, British National Grid: SD 3982 5993) 15 km to the north-east, downloaded from British Oceanographic Data Centre (BODC) website www.bodc.ac.uk (accessed on 4 June 2021). The EA tidal data were recorded as 15 min averages; the EA gauges are part of the UK National Tide Gauge Network [24]. Both of these tide gauges are full-tide bubbler tide gauge systems [25]. The datum used throughout is local chart datum (CD), which at Rossall is 4.90 m below Ordnance Daum Newlyn (ODN).  
Tidal predictions of high water (HW) and low water (LW) [26] are shown in Table 1. At Blackpool, high water was predicted to be 8.87 m at 21:23 on 28 August 2019 and 9.01 m at 10:42 on 29 August. The highest predicted tide of the spring cycle was 10.27 m on 2 September at 12:64; hence, the tides during the experiment were close to the peak springs. Tide gauge data at Liverpool and Heysham show that leading up to these high waters, the storm surge heights were relatively small: \(\sim\)0.1 m for the first tidal cycle and \(\sim\)0.3 m for the second tidal cycle [27].
## 3 Multi-Hop State-Aware Routing Strategy Based on Traffic Flow Forecasting  
Based on the system model proposed above, two problems are studied in this paper. The first problem is how to establish a traffic prediction model between two nodes and make routing decisions based on the results of the traffic prediction model. The second problem is how to make the enhanced nodes respond intelligently to the changing of network topology.  
To the first problem, the traffic flow prediction strategy based on the RNN network combined with the Deep Deterministic Policy Gradient (DDPG) method is proposed to predict the network traffic distribution. To the second problem, the node state of the WSNs is analyzed, and a Double-Deep Q Network is trained to achieve the optimal next hop under the condition of topological changes.
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 4 Case Study
### Effect of Chronological Characteristic on the Power System Reliability  
#### 4.2.1 Load Uncertainty  
Generally, the forecasted demand has an error. Therefore, several scholars believe that system demand has a probability distribution. By expanding (10), the generalized load model can be expressed as follows:  
\[L_{t}(x)=\sum_{i=1}^{U}P_{i}\,\delta(x-D_{i,t}), \tag{19}\]  
where \(U\) refers to the number of cases of demand, and \(P_{i}\) is the probability that the system demand is \(D_{i,t}\). This section assumes that the load uncertainty follows a discrete normal distribution with seven intervals, with a standard deviation of 5% for the predicted demand [10]. Table 3 lists the reliability indices of RTS 2020 according to the load uncertainty of 0-15%.  
Reliability indices increase as uncertainty increases. Although overpredicted and underpredicted demands have the same probability; overpredicted demand affects the system reliability more significantly than underpredicted demand. Therefore, a loss of load occurs more often in a more unpredictable system.
# 5G-MEC Testbeds for V2X Applications
## 5 Summary of Available Tools
### MEO and MANO  
The MEO is responsible for various functionalities in the MEC system such as tracking the overall view of the system depending on the MEH deployment of computing, networking, and storage resources and MEC services. The MEO needs to ensure appropriate MEH selection and MEC application relocation based on the user requirements. It also handles the onboarding of applications, packages, and SDKs. The ETSI specifies the deployment model of the MEC system in an NFV environment in which the MEC application and the NFV VNFs use the same VI. To fulfill the management and orchestration functionalities of the MEC system, the ETSI defined the NFV MANO architecture. Most of the surveyed testbeds deploy MANO functionalities using open source tools and platforms.  
**OSM**[31] is an open source tool developed by the ETSI community. It delivers the MANO stack for an NFV and is capable of handling the VNFs independent of the VIM. However, OSM is compatible with open source VIM such as OpenStack. OSM has minimum hardware resource requirement specifications. and can deploy MEC applications as VNFs in the NFV environment. In a paper by [PERSON] et al., OSM was used to deploy the MEC orchestrator functionalities [98].  
**JOX**[99] is a Juju-based service orchestrator that can interact with CN functionalities using the orchestrator plugins. JOX supports the orchestration of the virtualized network to deploy the network slices. JOX is compatible with the ETSI MANO NFV environment and allocates network slices based on the VNF and resource configuration specifications. JOX runs on juju VNFM and provides a plugin to the LL-MEP and FlexRAN SD-RAN controller.  
**OpenBaton**[100] is an open source platform providing the ETSI MANO functionalities. OpenBaton VNF packages are adaptable with Juju VNFM or generic VNFM. Generic VIMs can be integrated with the system via Juju VNFM. OpenBaton provides an NFV orchestration framework and manages MEC applications as VNFs. OpenBaton is widely used for the deployment of network slice applications.  
**cMEAO**[52] is a centralized MEC application orchestrator in the smart highway V2X testbed. cMEAO is compatible with the K8s VIM and its API. cMEAO allows the resources at the network's edge to be allocated by applying a smart mechanism. The smart mechanism that carries out the application placement is influenced by factors such as the MEH availability, RSUs, latency requirements, and the user's geolocation. In addition, cMEAO supports the exchange of messages between the vehicles and the edge.  
**LightMANO**[101] is a lightweight open source orchestrator platform that is compatible with the ETSI MANO architecture. LightMANO is built using Docker and runs within the K8s environment. The LightMANO framework orchestrates the NFV services in a distributed system. LightMEP [102] adapts LightMANO as the orchestrator for its MEC system.
## 2 Study Area and Datasets
### Multibeam Bathymetry Data  
The groundrutthing water depth (WD) data used in this study was acquired during June-September in 2009 as part of INFOMAR, the Irish national seabed mapping program (www.infomar.ie). The system employed onboard the R.V. Kears was the multibeam Kongsberg-Simrad MBES (EM3002) operating at circa 300 kHz [43]. Data was collected using differential GPS and tidal corrections were applied in the post-processing. Depths were reduced to LAT (Lowest Astronomical Tide) and deemed suitable for hydrographic charts. The complete system was capable of measuring water depths between 0 and 15 m with a vertical accuracy of less than 10 cm RMS and a horizontal positioning accuracy of less than 1 m. These data are used as the response variable in our study models.
## 3 Analysis of Original Research Articles
### Descriptive Analysis of the Selected Studies  
A total of 50 studies were selected based on the inclusion criteria.  
Figure 4 presents the distribution of the papers by country. China holds the research lead with 17 articles published, followed by the United States and India with 8 and 8 articles, respectively.  
Figure 5 displays the numbers of articles published each year. Starting in 2020, research interest has grown rapidly in the past four years.  
Figure 6 shows the percentage of papers published by subject area. Computer Science holds the lead with 27 published papers (22.4%), followed by Mathematics with 15 (12.9%), Economics/Econometrics/Finance with 13 (11.2%), and Agricultural/Biological Sciences with 11 (9.5%).
# BeiDou Geostationary Satellite Code Bias Modeling Using Fengyun-3C Onboard Measurements
## 2 Fengyun-3C Onboard Data Quality Analysis
### Measurement Availability  
In the Asia-Pacific regional service of BDS in 2015, there were only 14 operational satellites, including 5 GEOs, 5 IGSOs and 4 MEOs. In addition, the MEO satellite C13 was unavailable in 2015. When orbiting the earth, the Fengyun-3C can receive 4 to 6 BDS signals in most of the Asia-Pacific area because the GNOS instrument is capable of tracking up to 6 BDS satellites from the positioning antenna [3]. However, in other flight regions the tracking number is less than the minimum of 4 satellites required for a kinematic solution. Unfortunately, the GNOS receiver sometimes even fails to track BDS signals. Figure 1 shows the numbers of BDS and GPS satellites that the onboard GNOS receiver tracked per epoch in DOY 062. It is evident that the number of BDS satellites tracked was very unstable due to the invisibility of GEO and IGSO satellites outside the Asia-Pacific region. Compared with the whole GPS constellation, the available BDS data quantity was obviously less. For further comparison, Figure 2 shows the percentage of the observed GPS and BDS satellites for the whole data set. The Fengyun-3C receiver tracked 4 or more BDS satellites in only 33.4% of all epochs, whereas the number of GPS satellites tracked dropped below 4 in just 1.6% of the epochs. The low number of visible BDS satellites is an inevitable problem for the precision orbit determination of the Fengyun-3C satellite, which typically contributes to poor positioning accuracy [10].
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 6 Simulation Results and Analysis
### Simulation Models
#### 6.1.2 Energy Consumption Measurement Model  
Given that energy consumption in the dormant state is significantly lower than that in other transceiver states, the energy consumption of an EN, denoted as \(E_{c}\), can be simplified as the sum of transmit and receive energies [2]:  
\[E_{c}=V_{\text{DD}}\cdot(I_{tx}\cdot T_{tx}+I_{rx}\cdot T_{rx}), \tag{29}\]  
where \(V_{\text{DD}}\) is the nominal voltage, \(I_{tx}\) and \(I_{rx}\) denote the transmitting and receiving currents, respectively. \(T_{tx}\) and \(T_{rx}\) indicate the duration of transmitting and receiving, satisfying \(T_{tx}=T_{rx}=T_{0}A\). According to [33], these parameters are configured as specified in Table 5. Additionally, \(P_{T}\) denotes the transmit power at the transmission power level \(l_{tx}\).
# Utilization of Merge-Sorting Method to Improve Stitching Efficiency in Multi-Scene Image Stitching
## 5 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Number of Experiments** & **SIFT** & **SURF** & **A-KAZE** \\ \hline  & 1 & 510 & 1141 & 247 \\  & 2 & 551 & 1122 & 260 \\ (a) and (b) & 3 & 517 & 1224 & 252 \\ \(800\times 600\) & 4 & 529 & 1118 & 275 \\  & 5 & 501 & 1132 & 254 \\ \hline Average & 6 & 521 & 1147 & **257** \\ \hline  & 1 & 7129 & 6335 & 2840 \\  & 2 & 7170 & 6282 & 2689 \\ (c) and (d) & 3 & 7209 & 6326 & 2713 \\ \(2592\times 1944\) & 4 & 7231 & 6329 & 2699 \\  & 5 & 7238 & 6304 & 2683 \\ \hline Average & 6 & 7195 & 6315 & **2724** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of time taken for feature point computation and matching (unit: ms). Figure 5: (**a**–**h**) are experimental images of scene 1. Figure 6: The results obtained based on the binary tree model. Figure 7: Results obtained with the left-to-right approach. Figure 11: Result obtained by stitching from left to right. Figure 8: Results obtained with the method proposed in this paper. Figure 12: Result obtained with the MSIS method. Figure 10: The result of stitching based on the binary tree model. Figure 9: (**a**–**n**) are the experimental images of scene 2. The above experiments show that the method proposed in this paper could not only achieve the stitching of multi-scene images and produce a good stitching effect but also had improved stitching efficiency.
# A Fuzzy Path Selection Strategy for Aircraft Landing on a Carrier
## 2 Problem Statement and Conceptual Model Establishment
### Problem Statement  
According to the tactical imperatives, an aircraft receives returning information from the air control system on the carrier. To ensure the safety of returning flight, the real-time position information is provided by the Tactical Air Navigation (TACAN), which contains the relative position of the carrier. The returning aircraft is also instructed by an airborne early warning (AEW) system, which provides the returning aircraft with the overall air traffic situation. The information from an AEW is essential when managing the landing paths for a large amount of airplanes in order to guarantee a safe and orderly landing. The sea weather is obtained from the carrier and is transmitted to the pilot. To sum up, the landing path selection problem is a process in which the pilot and the LCO combine/analyze various information and then make quick decision accordingly. The details of the problem are shown in Figure 1.  
The dotted line in Figure 1 shows that when the pilot selects the optimal landing path, he/she treats the weather information as a reference instead of relying on it to make a judgment. This is because the weather information is relative to the carrier, and there may be differences between the places around the aircraft and around the carrier. This explains why the pilot merely regards the weather information from an observatory as a reference.  
In Figure 1, the pilot and the LCO are the two decision makers regarding the optimal landing path. The LCO is the major decision maker because he/she has more information on the air traffic condition. Furthermore, the feeling of a pilot is considered limited although he/she has a comprehensive understanding of the weather information and the performance of the aircraft. The reason is as follows. In actual work, the pilot usually has a heavy workload, e.g., executing a flight mission and analyzing various information simultaneously; therefore, he/she will be distracted from other work and his/her judgment will be affected accordingly.
# Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators
## 2 Modeling and Control
### Mathematical Model
#### 2.1.3 Erythrocytes Compartment  
In the erythrocytes, CO\({}_{2}\) exists in all three forms--dissolved, bound to proteins, and as a bicarbonate. Here, the hydration of CO\({}_{2}\) is catalyzed by the enzyme carbonic anhydrase, which increases the reaction rate by a factor of \(\sim\)10\({}^{4}\). Thus, when the partial pressure of CO\({}_{2}\) (\(p_{\text{CO}_{2},pl}\)) is high, CO\({}_{2}\) would diffuse in the erythrocytes from the plasma, where it gets hydrated to HCO\({}_{3}\), which then diffuses back into the plasma compartment. However, this generates a charge imbalance, because H\({}^{+}\) ions are accumulating in the erythrocytes, due to the dissociation of carbonic acid, leading to a decrease in pH. To compensate for that, chloride diffuses from the plasma into the red blood cells (Hamburger shift [15]). The CO\({}_{2}\) can also bind to hemoglobin and form carbamate. One hemoglobin molecule can bind to up to four CO\({}_{2}\) molecules. The CO\({}_{2}\) competes with O\({}_{2}\) to bind to hemoglobin through the Bohr and Haldane effects. The Bohr effect describes the decreasing O\({}_{2}\) affinity of hemoglobin at lower pH, thus improving O\({}_{2}\) release under venous conditions. The Haldane effect refers  
Figure 1: An overview of the system and controllers: (**a**) A schematic representation of the compartmental model of the membrane oxygenator; (**b**) A drawing of the physical system. The catheter—membrane oxygenase and blood pump—is inserted in the vena cava superior. The extracorporeal part of the device-controller and a compressor are worn by the patient; (**c**) A schematic representation of the PI controller; (**d**) A schematic representation of the NMPC controller; (**e**) A schematic representation of the deep RL controller.  
to the reduced CO\({}_{2}\) affinity of oxygen-saturated hemoglobin, which allows for improved CO\({}_{2}\) release in the lungs.
# Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems
## 3 Direct Mobile Coaching
### Mobile Client  
Providing functions for both data collection as well as data presentation, the mobile client is the centrepiece of Direct Mobile Coaching. In the context of this paper, this client can usually be seen as a running app on a smartphone. However, other clients are also possible, and the model has no restrictions on the technology used for clients. Data collection is focused on data from sensors (see Section 3.2), which can be either integrated into the mobile device or attached to the device using wired communication protocols, such as USB, or wirelessly. Feedback can be given to the users using graphical elements on the display, vibrations of the device or the text-to-speech function of the device which the client uses. The client communicates directly with the backend server (see Section 3.4), using a high-level API-based interface. This design principle is different to previous implementations of the MC paradigm, such as the one by [PERSON] et al. [14], where the app/client communicates with the server using low-level directives. In these previous implementations, individual data packets were created from scratch at various locations in the program logic and sent directly to the server using a socket connection. The high-level API of the current implementation is object-oriented. Consequently, data packets are created as objects without the necessity of accessing the data packets individually. Moreover, the socket for the communication is wrapped inside the API.  
The framework enables the adaption of a client's behaviour to different situations or \"sports\" via parametrisation. One part of this parametrisation or definition is the sensors (see Section 3.2) to be used during an activity with the feedback system. This list of sensors can be divided into required and optional ones. An activity of the feedback system can only be started when all the required sensors are connected. It is, furthermore, possible to define default views to be used during an activity with the feedback system provides a consistent user experience. These views are composed of several different value displays, for example, floating-point or integer values. Moreover, these value views can be configured to show values using graphical displays, such as a tacho needle, rather than numbers.  
A further task of clients is to upload arbitrary additional data to the server asynchronously, i.e., after an activity has finished. For some sports, this is a requirement as it is not always possible to stream data during the recording for various reasons (safety of equipment, rules, etc.). Furthermore, as not all sensors have wireless interfaces, it is not always possible to live-record data during an activity. Consequently, this improves the generality of the model.
## 4 Harmonic Emission Profiles
### Grouping of Emission Profiles
#### 4.2.1 Min-Max Normalization and Clustering of Emission Profiles  
The range of harmonic current values within the different LV networks strongly varies. Therefore, min-max normalization was applied in order to avoid problems using emission profiles with different ranges of values. The value ranges of a time series \(x\) were scaled between 0 and 1 in order to preserve the relation between the original values and to get a fixed range of values [24]:  
\[x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)}. \tag{2}\]  
As displayed in Figure 4, five different classes of emission profiles can be distinguished. A hierarchical cluster analysis was used in order to assign each of the 360 emission profiles for the 3 rd harmonic current to one of the classes. The Euclidean distance measure was used to calculate the required distance matrix for the clustering process. Other different distance measures (e.g., dynamic time warping distance) were tested and showed almost identical results compared to the Euclidean distance measure. The clustering used average linkage within each iteration of joining multiple clusters. The clustering process was stopped at five groups. A manual revision of the group assignment was carried out afterwards to ensure coherent profiles within each group. Based on the authors experience with other measurement campaigns in public low-voltage grids (e.g., [12]), the consumer configurations and their harmonic emission behavior are fully representative for Germany.
## 3 Research on the Characteristics of High-Voltage Switching with Three-Electrode Plane Spark Gap
### Static Self-Breakdown Characteristics  
The three-electrode planar spark gap high-voltage switch based on copper foil, like other high-voltage switches, needs to have a specific operating voltage range; that is, the gap between the main electrode and the trigger electrode of the switch can withstand a certain high voltage without self-breakdown. Therefore, the self-breakdown voltage \(U_{SB}\) of the switch is also the maximum working voltage of the switch. The high voltage capacitor (0.2 \(\mu\)F) is slowly charged at a rate of about 20 V/s from the high voltage DC power supply, until the air in the gap between the two electrodes occurs self-breakdown. A high-voltage probe and Rogowski coil were used to record the self-breakdown voltage \(U_{SB}\) and loop current I changes. When the width \(a\) of the trigger electrode is 0.6 mm and the width \(b\) of the gap between the two main electrodes is 1.2 mm, the test waveform of the self-breakdown characteristic of the switch is shown in Figure 6a. The maximum operating voltage \(U_{SB}\) is 2361 V, and the maximum output current peak value is 2892 A. The \(U_{SB}\) test curves of different parameters of the plane spark gap switch by the orthogonal method are shown in Figure 6b.  
It can be seen from Figure 6.b that as the main electrode gap increases from 0.8 mm to 2.6 mm, the self-breakdown voltage of the planar spark gap switch gradually increases, and the operating voltage also increases. When the main electrode gap is a maximum of 2.6 mm, the self-breakdown voltage of the switch can reach 3480 V, which indicates that the maximum operating voltage of the switch is 3480 V. According to [PERSON]'s theory and [PERSON]'s law [24; 25] of uniform electric field self-discharge under low pressure, the breakdown voltage U\({}_{SB}\) of the air gap is related to the air pressure \(p\) and the main electrode gap \(b\), and under the condition of a fixed gas atmosphere, the maximum operating voltage \(U_{SB}\) is positively correlated with b. By carrying out a large number of self-breakdown characteristic tests, it is shown that the self-breakdown voltage fluctuates greatly (the  
Figure 6: Waveform and curve of self-breakdown characteristic of three-electrode planar switch. (**a**) Self-breakdown characteristic waveform (C = 0.2 \(\mu\)F) Figure. (**b**) Self-breakdown characteristic curve.  
range is about 180 V), and the larger the gap, the greater the fluctuation. According to the principle of electrostatic discharge, this is because the trigger electrode is right-angled, and tip discharge is prone to occur during the conduction process, which makes the field strength here larger, resulting in an increase in the unevenness of the electric field.