esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Drainage Basins Extraction  
It is obvious that drainage basins statistical characterization is critical for understanding the geomorphic processes.  
During the last decades, geographical information systems (GIS) coupled with digital elevation models (DEMs) have been widely used for the automatical extraction of drainage networks and drainage basins as well as for landforms classification [39; 40]. In the present work, ASTER GDEM v2, is used as downloaded from the LP DAAC at one arc sec resolution (30 m) [41]. In comparison with other free DEMs, Aster GDEM has lower RMS errors in mountainous areas [42; 43] and since Greece is such an area we decided to use it for the watersheds extraction. Aster GDEM was clipped to the extent of the study area of Greece and re-projected to the Hellenic Geodetic Reference System '87 (HGRS'87). Using ArcHydro, an extension of ArcGIS software, we were capable to efficiently delineate the drainage basins of Greece. The procedure used is as follows: firstly, the cells with elevation values abnormally low or high in comparison to their neighboring cells (known as sinks or spikes respectively) were removed. After the removal of the erroneous data, a new, hydrologically corrected (i.e., free of sinks and spikes), digital elevation model, was obtained [44]. After that, a flow direction computation using the commonly used D8 algorithm [45] was applied. D8 flow direction is an integer raster whose values range from 1 to 255 providing 8 different directions. It is then analyzed to find all sets of connected cells that belong to the same drainage basin. The drainage basins are delineated within the analysis window by identifying ridge lines. The drainage basins of the Greek territory were extracted in the form of a raster layer which was later converted to a vector polygonal one, containing a few thousands of basins.  
From these basins only that with an area greater than 0.1 km\({}^{2}\) (Figure 1) were selected for further analysis based on visual comparison with the national drainage basins dataset. For the area distribution analysis, the final drainage basin dataset was extracted into several layers (sub-datasets) according to different attributes and spatial characteristics such as, the geotectonic environment, the mean elevation value, and the mean topographic position index using the \"select by attributes\" and \"select by location\" functions of ArcGIS software.
## 2 Materials and Methods  
### Dataset  
EEG data were drawn from the TUH EEG seizure database (TUSZ), which is a part of Temple University Hospital EEG corpus, the largest open-source EEG corpus. The latter comprises more than 16,986 sessions of EEG recordings collected from 10,874 unique subjects [40; 41]. The version of the database used in this work was v1.5.1, released in March 2020. In this study, the dataset consists of EEG signals collected from 341 patients, providing a substantial amount of data for seizure detection investigation. The EEG segments were labelled by a team of neuroscience and bioengineering students who underwent several months of intensive training to gain annotation skills. They have worked closely with a team of neurologists at Temple Hospital to understand their workflow and their clinical needs while labeling EEG segments. There have been numerous revisions of the corpus during the annotation process (two or three annotators per file system), each time applying more refinement and criteria to enhance the clarity and accuracy of the data. Annotations are created by viewing files with open-source software known as an EDF viewer or by using a customized annotation tool developed by the team, which provides time and frequency domain visualizations. The annotation was made available publicly, except for an evaluation corpus not released to the public, to be allocated for research competition purposes. The dataset used contains 886 sessions that were broken down into 7634 files, of which 1780 are seizure activities of different lengths, in seconds, for a total of 40.40 h. The data were collected in real-time clinical environments, including an intensive care unit, an epilepsy monitoring unit, emergency rooms, cardiac intensive care, and surgical and respiratory intensive care units [41]. Most of the EEG recordings have at least 19 electrodes corresponding to the international standard 10/20 system and range from one second to one hour in duration. The sampling rate varied between 250 Hz and 500 Hz. We resampled all data at 256 Hz. In this study, we discarded seizure events that lasted less than our sliding window of 20 s. Eight types of seizures were present in the TUSZ database: focal non-specific seizure, generalized non-specific seizure, complex partial seizure, simple partial seizure, tonic-clonic seizure, absence seizure, tonic seizure, and myoclonic seizure. Since our goal was not to detect the type of seizure but to determine the presence or absence of seizures, we combined all these types under one label, \"seizure.\"  
The dataset description is summarized in Table 1, and Figure 1 shows an example of seizure and seizure-free EEG epochs. Detailed information about the database annotation can be found in the following reference [42].
## 6 Existing Routing Protocols
### Geographic Adaptive Fidelity (GAF)  
GAF is an energy efficient location-based routing protocol. This protocol was initially conceived for mobile _ad hoc_ networks, but it can also be applied to sensor networks. GAF can be implemented both for non-mobile and mobile nodes. Although GAF is a location based protocol, it may also be implemented as a hierarchical protocol where the clusters are based on geographic location.  
Initially the area of interest is split into some fixed zones forming a virtual grid for the covered area. Nodes in each zone have different functionalities and each node uses its GPS-indicated location to associate itself with a point in the grid. Nodes which are positioned at the same point on the grid are considered equivalent in terms of the cost of packet routing. Such equivalence is exploited in keeping some nodes located in a particular grid area in a sleeping state in order to save energy. Thus GAF can increase the network lifetime as the number of nodes increases. GAF conserves energy by turning off unnecessary nodes in the network without affecting the level of routing fidelity. GAF defines three states: discovery, active, sleep. The 'discovery' state is used for determining the neighbors in the grid; the 'active' state participates in routing process and at the time of'sleep' state, the radio is turned off. In order to handle the mobility, each node in the grid estimates it's leaving time of grid and sends this to its neighbors. The sleeping neighbors adjust their sleeping time accordingly in order to keep the routing fidelity. Before the leaving time of the active node expires, sleeping nodes wake up and one of them becomes active.
# Space Charge Accumulation and Decay in Dielectric Materials with Dual Discrete Traps
## 2 Dynamic Responses of Charge Trapping and De-trapping
### Continuous Charge Injection  
First, we consider the first case where the free charges are injected continuously. As mentioned above, we assumed that the free charges injected continuously to keep the total number of free charges in the bulk unchanged. In this condition, we can derive the following first-order charge trapping/de-trapping equation to obtain the time-dependent trapped charge density, \(q_{trapi}(t)\).  
\[\frac{\mathrm{d}q_{trapi}(t)}{\mathrm{d}t}\ =\ q_{fneq0}P_{T}\!\!\left(1-\frac{q_{ trapi}}{Q_{Ti}}\right)-P_{Di}q_{trapi},(i\ =\ 1,2) \tag{5}\]  
Equation (5) can be rearranged by moving \(\mathrm{d}t\) to the right and the term including \(q_{trapi}\) to the left part of the equation. Integrating the left and right parts of the rearranged equation, we can obtain the following equation,  
\[\left(\frac{q_{fneq0}P_{Ti}}{Q_{Ti}}+P_{Di}\right)q_{trapi}(t)-q_{fneq0}P_{Ti }\ =\ C\exp\left[-\left(\frac{q_{fneq0}P_{Ti}}{Q_{Ti}}+P_{Di}\right)t\right],(i \ =\ 1,2) \tag{6}\]  
where \(C\) is an undetermined coefficient.  
It is assumed that there are no charges in the traps at the beginning of the charge trapping/de-trapping process. The undetermined coefficient \(C\) in Equation (6) can be estimated  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Charge carrier mobility, \(\mu 0\), (m\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\)) & \(1.0\times 10^{-13}\) \\ Energy of trap level 1, \(E_{T1}\), (eV) & 0.88 \\ Energy of trap level 2, \(E_{T2}\), (eV) & 1.01 \\ Charge density of trap level 1, \(Q_{T1}\), (cm\({}^{-3}\)) & 100 \\ Charge density of trap level 1, \(Q_{T2}\), (cm\({}^{-3}\)) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The assigned parameter values used in the charge trapping/de-trapping dynamic analysis.  
Figure 1: Schematic dynamics of charge trapping/de-trapping events in dielectric materials containing two discrete levels of traps for (**a**) continuous charge injection and (**b**) instantaneous charge injection. Here \(E_{C}\) is the conduction band, \(E_{T1}\) and \(E_{T2}\) the energies of traps, \(E_{F}\) the Fermi level, \(P_{T1}\) and \(P_{T2}\) the trapping probabilities for free charges, and \(P_{D1}\) and \(P_{D2}\) the de-trapping probabilities of trapped charges.  
by this initial condition \(q_{trap1}(0)=0\). Consequently, the trapped charge density as a function of time \(q_{trap1}(t)\) can be derived as,  
\[q_{trap1}(t)\ =\ \frac{q_{free0}P_{Ti}Q_{Ti}}{q_{free0}P_{Ti}+P_{Di}Q_{Ti}}\times \left[1-\exp\left[-\left(\frac{q_{free0}P_{Ti}}{Q_{Ti}}+P_{Ti}\right)t\right] \right],(i\ =\ 1,2) \tag{7}\]  
Equation (6) shows that the trapped charge density, \(q_{trap1}(t)\), increases monotonically with an increase in time until it approaches its maximum value, which is equal to \(q_{free0}P_{Ti}Q_{Ti}(q_{free0}P_{Ti}+P_{Di}Q_{Ti})\). The largest increasing rate of \(q_{trap1}(t)\) is \(q_{free0}P_{Ti}\) that appears at \(t=0\). Then, the increasing rate decreases gradually with time.
## 6 Results  
Our experiments were conducted with a satellite-like environment by creating emulated delays and packet-losses between clients and servers. The experiments were conducted with different assumptions for the packet-loss of the links emulating different weather conditions. A delay of 8 ms was configured on the links connecting servers to the OVS, besides a 300 ms delay imposed by the CloudLab while provisioning the resources. Packet-loss ratios used in our test scenarios are listed in Tables 2-8. The performance of each algorithm was measured by considering HTTP GET request transmissions for static files of varying sizes between 100 KB to 1 MB and sending HTTP POST requests with a data payload of 10 KB from the client machine. Each experiment was run for 10 min and repeated six times to get statistical averages.
## 2 Materials and Methods
### Physical and Mechanical Properties Testing  
The strength grade was identified as optimal through comprehensive testing. Compressive strength was measured at 28 days using a universal testing machine following ASTM C39 protocols, achieving 20-30 MPa. Porosity was maintained above 15% as determined by the vacuum saturation method according to ASTM C642 [15]. Permeability coefficient was measured using a falling head permeameter following ASTM D2434, maintaining values above 1 \(\times\) 10\({}^{-4}\) cm s\({}^{-1}\)[16].
## 5 Conclusions  
Successive relaying-based cooperative non orthogonal multiple access (CNOMA) transmission schemes are proposed. Two half-duplex relaying user terminals deliver the AP signals toward two NOMA UTs one-by-one. Thus, they emulate the full-duplex relaying transmission and the end-to-end transmission of one NOMA data block is carried out in a single time slot. The removal of cross-relay interference between the two HD relays is the key technical challenge and the beamforming with multiple antennas at the relaying UTs is employed for this purpose. Furthermore, the relaying UT beamforming does the additional role of maximizing the CNOMA transmission efficiency. Among the two CRI zero forcing beamforming schemes proposed, the first method employs semi-definite relaxation (SDR) with a four dimensional search, where the four dimensions reflect the four parameters of the CRI levels at the relaying UTs and signal strengths at the NOMA UTs. This scheme alternately updates beamformer sets until the 4-D search range is reduced and the sum throughput rate of CNOMA saturates. The second method avoids the SDR and alternation and that the beamformer set is found in one shot calculation. The performances of these two schemes and HDR relay-based cooperative NOMA scheme are compared in the numerical experiments. The two proposed schemes outperform the HDR-based cooperative NOMA in most of the cases though the advantage of the first scheme over the second scheme diminishes in the high relaying power regime (high CRI regime), where the two schemes perform almost the same. When the number of antennas increases into the range of massive ones, the second scheme fails to outperform that of the HDR-based scheme since it lacks the optimization of CNOMA transmission of the second hop. The results of this work indicate that the coverage area of the NOMA service can be extended by a set of beamforming schemes for the cooperative relaying UTs with no extra spectral resources.  
[PERSON] proposed the beamforming principles and associated algorithms for the SR cooperative NOMA wireless networks; [PERSON] and [PERSON] supplemented in improving the algorithms and derived and analyzed all simulation results. Furthermore, [PERSON]-[PERSON] provided the experimental materials for better computational simulations and revised critical errors in the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2020R1A6A1A0 3038540) and the Institute of Information and communications Technology Planning and Evaluation (IITP) under the metaverse support program to nurture the best talents (IITP-2023-RS-2023-00254529) grant funded by the Korean government (MSIT).  
Data is contained within the article.  
The authors declare no conflicts of interest.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 4 5G Mobile Services
### Public 5G Services  
#### 4.5.1 Disaster Monitoring  
Multiple sensors can be used to monitor mountains, seas and radioactively-contaminated places, as well as to minimize damage under disaster situations by providing a quick response through interworking with public safety networks. Sensors may also monitor forest fires and landscape changes by sensing temperatures, vibrations, wind speeds and wind direction. Radioactivity detection sensors can be used to monitor changes in the radioactivity index at radioactively-contaminated areas. It is possible to monitor radioactive contamination in real time and to utilize that information for access control or disaster notification services. Moreover, sensors can be installed in infrastructure facilities, such as dams, bridges and expressways, and can be utilized for the maintenance of facilities along with monitoring the risk of collapse. As of now, such monitoring networks use existing communication standards, such as Wi-Fi or ZigBee. However, these networks are unable to provide the desired quality of service (QoS) in terms of coverage, energy efficiency, network reliability and cost efficiency. On the other hand, 5G networks may play a role in public safety networks using the highest-priority protocol when an event of emergency occurs. The integration of satellites in future 5G networks will be seen as an essential part of the terrestrial infrastructure to provide strategic solutions for critical and lifesaving services. Satellites will be able to supply critical and emergency services and keep the network alive in cases of disaster. In terms of the sensors' battery lifetime, energy efficiency is one of the most significant features for 5G. Disaster monitoring networks require extreme levels of reliability in their network connections. Hence, hyper-energy-efficiency and hyper-reliability are mandatory features for 5G networks. At the same time, the main requirements of sensor networks, such as coverage and cost effectiveness, shall also be considered in 5G networks.
## 1 Introduction
A control concept for distribution networks with EVs and distributed energy resources has been previously presented. An analysis of the control strategies, market models, and characteristics of existing EVs was performed to take into account the main drawbacks of existing solutions and a new concept of distributed control for the management of the energy system [22]. Another document proposes an allocation planning method for EVs in a multi-agent-based microgrid [23]. A new scheme to provide the necessary primary reserve for EVs by controlling multiple agents of each vehicle is proposed in [24], and a new methodology to determine the optimal EV load-discharge strategy based on the uncertainty of the voltage drop is proposed in [25]. The consulted references show that previous works have focused on active and reactive power control, but few have considered virtual impedance as part of the solution. However, they have not worked on using variable virtual impedance to share the reactive power in a microgrid that considers the distances to the connection of EVs. The objective of this paper is to show reactive power sharing and voltage regulation in a microgrid using the concept of variable virtual impedance related to the line distance between the inverter and the consumption of EVs charging in the power grid. Therefore, the contributions in this article are as follows. Variable virtual impedances are calculated from each inverter to the possible vehicle connection and disconnection point; a voltage control is applied based on the line distance to the EVs connected; and reactive power sharing from distributed generation is achieved to compensate for the nodes in a network once some variations in the loads are presented. The rest of the document is organized as follows. Section 2 explains the new control strategy to share reactive power after each load variation. Section 3 presents the formulation for the small-signal stability analysis applied to the control strategy. Section 4 shows the results of simulations performed with the software MATLAB-Simulink. Finally, Section 5 presents conclusions and future work.
## Materials and Methods
### NDVI and Its Derivative Parameters
In this study, the blue band (450-520 nm), green band (520-600 nm), red band (630-690 nm), and near infrared (760-900 nm) were selected as the calculation range of each parameter according to the spectral division criteria of Landsat 5/7.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 4 Safe Margin Control Space
### Example: Robot as Single-Integrator  
To illustrate the concept of safe control space, take an example of a simple robot \(R_{A}\) that was considered in [27]. The position of the center of a disc-shaped robot \(R_{A}\), at time \(t\), for a given control input \(u\), is given as  
\[p_{a}(t,u)=p_{a}(0)+tu \tag{12}\]  
where \(p_{a}(0)\) is the current position of robot. Similarly to [19], consider the constraint on robot states as follow:  
\[\sqrt{\hat{x}(t)^{2}+\hat{y}(t)^{2}}\in[0,v_{m}] \tag{13}\]  
where \(v_{m}\) is the maximum speed of the robot. For system in (12), the control input directly corresponds to the velocity of the system \(\dot{p}_{a}(t,u)=u\); therefore, \(U_{ad}\) is a set of control inputs to a robot such that \(||u||\leq v_{m}\). Geometrically, \(U_{ad}\) is a disc of the radius \(v_{m}\) with its center at the origin in the control space.  
The temporary control obstacle \(UO_{j}(t_{i+n})\) is the set of control inputs for which \(p_{a}(t_{i+n},u)-c(t_{i+n})\in B\). For the single integrator, temporary control obstacle is equivalent to temporary velocity obstacle, \(NLVO(t_{i+n})\) defined in [27], and is shown in Figure 1a. Geometrically, it is a disc of the radius \((r_{a}+r_{a})/t_{i+n}\) with its center at \((c(t_{i+n})-p_{a}(0))/t_{i+n}\) in robot control space.  
Consider the following situation of the robot:  
* current position at \(p_{a}(0)=(0,0)\), radius \(r_{a}=0.4\), and maximum linear speed limit of 1 unit/s.
* disc shaped static obstacle of radius \(r_{b}=0.4\) centered at \((2,0)\).
* temporary control obstacle considered at every time step of 0.1 s up to time horizon of \(\tau=5\) s.  
Figure 3 shows the obtained safe velocity space and the path undertaken by a robot for selected velocities with different margins.
## 1 Introduction
* The proposed RHF smoothers do not require any a priori information of the initial state or any assumption of the invertible state transition matrix. * Through the numerical experiments, it is shown that the proposed RHF smoothers have advantages over the existing RHF smoothers and fixed-lag Kalman smoother and are applicable for practical application. The remainder of this paper is organized as follows. In Sections 2 and 3, UMVRHF smoothers in the batch and recursive forms are proposed, based on two independent optimal estimators for separated sub-horizons and [PERSON]'s theorem. Thereafter, the extension method to estimate a posteriori states is introduced. The Rauch-Tung-Striebel RHF smoother and the computational complexity reduction method based on the smoothing algorithm are presented in Section 4. In Section 5, the performance and effectiveness of the proposed RHF smoothers are demonstrated via numerical experiments. Finally, the conclusions of this study are presented in Section 6.