esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 3 SDN-Based Implementation As A Proof-Of-Concept
### SDN-Based Mesh Backhaul Architectural Principles  
As a proof of concept for a SDN-based mmWave mesh network, we deploy a testbed environment composed by 4 mesh nodes and an additional node, responsible for the SDN controller functionalities. Each node is a Gigabyte GB-BKi7 HA-7500 mini-PC, running Ubuntu 16.04 LTS with Linux kernel version 4.4.0-36-generic. Each mini-PC has 16 GB RAM and a 3.5 GHz Intel processor. In addition, we install 2 IEEE 802.11 ad WiGig dongles from Panasonic Inc. Japan on each mesh node. The dongles are configured with MCS9 (Modulation and Coding Scheme), whose theoretical physical layer (PHY) rate can be referred to [31]. In addition, they can only operate on the two middle channels e.g., #2 and #3, among the 4 channels of IEEE 802.11 ad.  
As depicted in Figure 9, Node 1 (N1) has a link with Node 2 (N2) in channel #2, and a second link with Node 3 (N3) in channel #3. Additionally, Node 4 (N4) is connected to N2 in channel #3 and to N3 in channel #2. Here, the channels are switched to avoid inter-link interference. For each link, one end has the IEEE 802.11 ad module configured to operate in AP (access point) mode, while the other is in UE (station) mode. A corresponding photo of the constructed testbed is shown in Figure 10.  
Figure 9: Used testbed topology.  
To enable the management of the mesh nodes through the SDN controller we install Open vSwitch (OVS) [34] 2.7.0 as an OpenFlow-enabled software switch, and added the WiGig interfaces as switch ports. The built SDN controller is based on OpenDaylight (ODL) [35] and features modules that support the installation of OpenFlow forwarding rules through its Southbound API (Application Programming Interface), along with a Northbound API which can be used for issuing configuration commands from network management applications. The communication between the controller and the mesh nodes is performed through an out-of-band Ethernet channel.  
Given our testing topology, and for the easiness of understanding the upcoming results, we present the maximum throughput and latency values with no background traffic on 1 and 2 hops in Table 2. In this baseline test, User Datagram Protocol (UDP) flows are generated. The ping command is used to get a rough estimation of the RTT (Round Trip Time). The result shows that the maximum throughput of 1.51 Gbps that the WiGig devices can provide is achieved, even for the case of 2 hops since two orthogonal channels are assigned for each hop to prevent interference. The RTT for the 2-hop case is naturally higher than that of the 1-hop case due to multi-hop communication [36].
## 6 Summaries and Outlook
### Outlook  
In recent years, solving the localization problem in UWSNs has resulted in many innovative solutions and ideas. However, the research in this field is still at the start-up phase. The probable future research direction may be localization algorithms for mobile and hybrid UWSNs, since absolutely stationary network does not exist in real applications. We believe that in addition to the existing research issues of localization algorithms, the possible hot research topics are:  
* Localization algorithms which are suitable for large scale UWSNs, especially for sub-sea environment, are still unexplored. At present, localization algorithms in UWSNs are mainly researched in the small scale network. Many practical applications require the use of large scale UWSNs. Therefore, it is essential to carry out the research of localization algorithms for large scale UWSNs.
* In UWSNs, developing an realistic model of sensor node's mobility is an important issue. Many models, such as the Meandering Current Mobility (MCM) model, which has been studied in [48], have been proposed for mobility. However, these models are not suitable for sub-sea network. More efforts are needed to develop more realistic mobility models which can adjust anchor node's movement path according to the corresponding underwater information, e.g., depth, current velocity, water salinity, _etc._
* The issue of anchor node's placement should not be neglected. Anchor node's effective placement can greatly improve localization accuracy. For example, in the two-dimensional network, the localization error is least when three anchor nodes form an equilateral triangle.
* The present localization algorithms are lack of researches on anchor node's path planning model in which the mobile anchor node traverses the entire network to provide larger localization coverage. In this case, the edge nodes and isolated nodes can be efficiently localized.
* Develop an efficient localization mechanism in which multiple anchor nodes dynamically collaborate with each other to localize unknown nodes. Especially needed is the research on how anchor nodes should collaborate to localize unknown nodes when the number of anchor node is not enough.  
* The current localization algorithms are based on a safe and credible environment. However in real application, UWSNs are always deployed in complex and unsafe environment. Thus, secure localization and position verification algorithms are needed.
* To the best of our knowledge, there are few research on UWSNs' localization problem under other conditions, such as duty-cycle environment, which is an interesting research issue. In duty-cycle environment, sensor nodes go to sleep according to some sleep scheduling mechanism. Therefore, current localization algorithms in which all sensor nodes wake up all the time cannot be adopted in the duty-cycle environment.
* Analysis of various influence factors is the premise of researching localization algorithms. It is important to design a realistic and systematic performance evaluation model and mechanism.
## 2 Mathematical Model of the Controlled Object  
The problem of controlling a one-dimensional inverted pendulum is considered, i.e., one that can be understood as a pendulum with its weight pointing upwards. The plane of rotation of the pendulum has only one degree of freedom (tilting is only possible to the left or right). In classic experiments described in the literature, a one-dimensional inverted pendulum model is most often placed on a cart that can move with an acceleration (\(u\)) [32]. In this paper, the inverted pendulum model is simplified. We adopted a mathematical model of an inverted pendulum, the base of which is stationary at the ground, with the pendulum capable of rotating in one plane (tilting is only possible to the left or right). Figure 1 shows the inverted pendulum considered in this study.  
The inverted pendulum model [32] described by Equation (1) of motion was used for this research.  
\[\ddot{\varphi}Rm=mg\sin(\varphi)-F_{x} \tag{1}\]  
Model parameters:  
* \(m\)--mass of the inverted pendulum;
* \(R\)--radius (length of the pendulum arm), i.e., the distance from the centre of mass to the point of the centre of rotation;
* \(\varphi\)--angle of deviation of the pendulum from the vertical;
* \(g\)--acceleration due to gravity,
* \(F_{x}\)--force for stabilisation of the pendulum.  
The pendulum can swing away from the vertical by an angle denoted as \(\varphi\). The angle (\(\varphi\)) is assumed to take values in the range of \(+90^{\circ}\) to \(-90^{\circ}\). Stabilisation of the pendulum's position is achieved by forcing it with a righting moment (\(M\)), where \(M\) is equal to the product of the forcing force (\(F_{x}\)) and the radius (\(R\)) (pendulum arm--distance to the centre of gravity). The research presented here can be used to solve the classical model. With reference to the classical method, in which the pendulum is placed on a moving cart, it can be assumed that the relation of the force (\(F_{x}\)) with the acceleration (\(u\)) can be described by Formula (2).  
\[F_{x}=\frac{mu}{\cos(\varphi)} \tag{2}\]  
The actual \(F_{x}\) force implementation system has technical limitations. The \(F_{x}\) force is limited to maximum and minimum values, and the actuator has its own inertia. Hence, a limitation on the force value was introduced into the model, and the \(F_{x}\) signal was delayed by means of a second-order transfer function (\(F_{c1}\)) described by Formula (3).  
\[F_{c1}=\frac{1}{0.0025s^{2}+0.1s} \tag{3}\]  
The problem of one-dimensional inverted pendulum control is considered, where the pendulum can be understood as a rod with its mass concentrated at the upper end and pointing upward. The plane of rotation of the pendulum has only one degree of freedom (tilting is possible only to the left or right). The classic model of the one-dimensional inverted pendulum is often placed on a cart that can accelerate. In this article, the model is simplified.  
Figure 1: Inverted pendulum model.
# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
## 3 Data Analysis
### Sensitivity of the C-Band to SSM for Wheat and Grassland  
Figure 3 shows the sensitivity of the C-band in VV (C-VV) and VH (C-VH) polarizations to SSM for different NDVI ranges. For an NDVI between 0.1 and 0.7, the sensitivity of the C-VV and C-VH (incidence angle approximately 39\({}^{\circ}\)) to SSM is approximately 0.13 dB/vol.% and 0.10 dB/vol.%, respectively (Figure 3a-d). Moreover, Figure 3 shows that crop growth until an NDVI of 0.7 (vegetation height of 70 cm and LAI of approximately 1.5 m\({}^{2}\)/m\({}^{2}\)) does not attenuate the C-band SAR signal since the sensitivity of C-band to SSM is the same for an NDVI between 0 and 0.4 and for an NDVI between 0.4 and 0.7 (Figure 3a-d). For NDVI values higher than 0.7, the C-VV and C-VH become insensitive to SSM for SSM between 5 and 36 vol.% (Figure 3e,f). This is due to the high attenuation of C-band SAR signal by the well-developed vegetation cover. The insensitivity of C-band SAR to SSM in case of well-developed vegetation was reported in the study of [PERSON] et al. [19]. In [PERSON] et al. [19], the sensitivity of the radar signal in the C-band (VV polarization and 38\({}^{\circ}\)-47\({}^{\circ}\) incidence angles) was of 0.11 dB/vol.% and 0.05 dB/vol.% for grassland biomasses less than and greater than 1 kg/m\({}^{2}\) (NDVI -0.7), respectively.  
Figure 3: Sensitivity of C-band backscattering coefficient as a function of SSM for three ranges of NDVI values. (**a**): C-VV and 0.1 < NDVI < 0.4; (**b**): C-HV and 0 < NDVI < 0.4; (**c**): C-VV and 0.4 < NDVI < 0.7; (**d**): C-HV and 0.4 < NDVI < 0.7; (**e**): C-VV and 0.7 < NDVI < 0.9; (**f**): C-HV and 0.7 < NDVI < 0.9.
# Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways
## 2 Materials and Methods
### Simulation Environment  
As explained in Section 2.2, the scenarios extracted from the data set are simulated with a reference and a completely cooperative maneuver planner to filter out the relevant ones for the test scenario catalog. These simulations are performed in a self-developed environment that meets the basic requirements for modeling the maneuver planning level of highway-like road traffic. The implemented roadway model consists of a variable number of straight lanes and highway entrances that are 250 m long. The longitudinal dynamics of the vehicles is modeled by a basic kinematic approach:  
\[\left(\begin{array}{c}s_{i}^{\prime}\\ v_{i}^{\prime}\end{array}\right)=\left(\begin{array}{cc}1&\Delta t\\ 0&1\end{array}\right)\left(\begin{array}{c}s_{i}\\ v_{i}\end{array}\right)+\left(\begin{array}{c}\frac{1}{2}\Delta t^{2}\\ \Delta t\end{array}\right)a_{i} \tag{1}\]  
where the control input of vehicle \(i\) is represented by the acceleration \(a_{i}\) and the velocity \(v_{i}\) and position \(s_{i}\) in the next time step (denoted by \({}^{\prime}\)) change accordingly. The simulation time step is set to \(\Delta t=0.1\) s.  
The lateral direction is represented by discrete lanes. Therefore, lane changes comprise a fixed duration in which the changing vehicle occupies both involved lanes. Lane changes have a mean duration of 4.8 s for cars [21; 22] and 7.7 s to 8 s for trucks [22; 23]. Since, for the simulation, the duration of a lane change comprises only the time the vehicles block both lanes and not the entire lane change, the lane change durations are modeled at 4 s for cars and 6 s for trucks.  
To fulfill the criterion of objectivity as stated in Section 2.1, the duration of the performed simulations must be specified, and the simulation time should be long enough that the scenarios can be solved completely. Theoretically, we regard a scenario as solved when the vehicles reach a state in which they can drive at their desired velocity without needing to brake or change lanes, or, in other words, when the cost terms presented in Section 2.3 become zero. From this point on, the vehicles follow each other without changing their order. Practically, a vehicle can end up in a situation in which it follows a slightly slower leader, but the costs for a lane change would be higher than the costs for the time loss when staying behind. Therefore, the time costs will not become zero in all scenarios but will asymptotically approximate a constant value near zero. For the scenario generation process, we used a simulation time of 20 s and validated it using a plot that shows the cost terms over simulation time averaged for all scenarios (Appendix A). The value of 20 s proved to fulfill the demands of zero costs for energy and lane changes as well as asymptotic approximation for time costs.
# Algorithmic Matching Attacks on Optimally Suppressed Tabular Data
## 3 Matching Attack
### Algorithm for the Matching Attack
```
0: a suppressed table: \(stbl\), a minimum frequency \(t\), a threshold width: \(\delta\)
0: a set of actual feasibility intervals for the suppressed cells: \(list\)
1:\(vars\gets getListOfVariables(stbl)\)
2:for\(i=1\) to \(length(vars)\)do
3:\(vars[i].min\leftarrow\infty\)
4:\(vars[i].max\gets 0\)
5:endfor
6:\((A,b)\leftarrow genLinearEquations(stbl)\)
7:\(v\gets getFixedSolution(A,b)\)
8:\(N(A)\leftarrow getNullSpace(A)\)
9:\(S\gets getAllSolutions(v,N(A))\)
10:for all solution \(s\in S\)do
11:\(tbl\gets complement(stbl,s)\)
12:\(stbl^{l}\gets solveCSP(tbl,t,\delta)\)
13:if\(stbl^{l}=stbl\)then
14:for each variable \(i\) in \(vars\)do
15:if\(s[i]<vars[i].min\)then
16:\(vars[i].min\gets s[i]\)
17:elseif\(s[i]>vars[i].max\)then
18:\(vars[i].max\gets s[i]\)
19:endif
20:endfor
21:endif
22:endfor
23:return\(vars\)
```  
**Algorithm 1** Function \(matchingAttack(T_{0},s,\delta)\)  
The for loop in lines 10-22 performs the matching test on every candidate table \(tbl\) and incrementally obtain the effective feasibility interval of each suppressed cell in \(stbl\). Line 11 prepares a candidate table \(tbl\) by complementing suppressed cells in \(stbl\) with each solution \(s\) in the set \(S\) of all solutions. Line 12 performs cell suppression on a candidate table \(tbl\) with the function \(solveCSP\) for solving a CSP. As we discuss the adversary model in Section 2.5, we assume that an adversary uses the same deterministic algorithm \(solveCSP\) and security parameters \(t\) and \(\delta\) as is used to produce an input suppressed table \(stbl\). Line 13 checks whether a suppressed candidate table \(stbl^{l}\) is same as the input table \(stbl\). If so, lines 14-20 updates the feasibility intervals of each suppressed cell if the candidate cell values are outside the current ranges of the feasibility intervals. After examining all possible solutions for cell variables, line 23 returns a list of variables with their effective feasibility intervals.
# Through-Floor Vital Sign Searching for Trapped Person Using Wireless-Netted UWB Radars
## 2 Through-Floor Vital Sign Searching Concept  
The view of the proposed through-floor vital sign searching concept is plotted in Figure 1. To survey the trapped person, four UWB life-detection radars represented by pentagrams are activated and grouped in the wireless-netted way, and they are deployed and moved on a floor which forms a vital sign searching path in the 2D observation aperture (shown as the dark-green dotted arrow). Assuming some people with micro-motions such as respiration or limb swing are trapped, these UWB life-detection radars are configured to detect these trapped persons. The working mode of the radar system is set as follows: first, the UWB life-detection radars are activated sequentially via remote network, avoiding mutual interference between radars at different observation points; and then, the radar echoes and results collected at different observation points need to be processed in accordance with the specially designed vital sign search procedure, avoiding wasting time dealing with clutter signals. In addition, when all observation points are sequentially selected and the transmission and reception of the mono-static radar is completed, this concept assumes that the trapped person retains the same/similar micro-motion during the vital sign searching process. The detailed design and key parameters of the UWB radar system can be found in our previous work [12]. The detailed parameters of the adopted UWB radar can be found in Table 1.  
Figure 1: The concept for through-floor vital sign searching with wireless-netted UWB life-detection radars.
## 4 Distributed BS Assignment Algorithm
### Procedures at the BS  
The pseudo-code of the algorithm running at each BS is illustrated in Algorithm 1. Firstly, each BS sets and disseminates the initial radio and backhaul prices. Subsequently, BS \(j\) gets association requests from devices and groups them in an association request set denoted to as \(ar_{j}\). Each association request is assumed to include metrics related to the observed channel conditions at the device side. The total radio and backhaul costs are computed (step 6) to check if the current set of association requests does not exceed the resource constraints. In this step, if the radio or backhaul constraint is the most violated, the corresponding price is updated to alleviate the excess the load. In steps 8-12, the algorithm determines the device to be reassigned to another BS different that the requested one. To this end, the corresponding resource price (radio or backhaul) is increased (step 9-11) in such a way that only one device is moved. Notice that the device causing the highest cost is also the device with the minimum price increase according to step 11. Each BS independently chooses the device causing the minimum increase in the price and update the radio price according to Equation (10) (steps 14-16). In the end, if the stopping criterion is met BS stops updating the resource pricing, otherwise repeat step 3. The aforementioned process is repeated for updating the backhaul price when the associated constraint is exceeded (steps 18-27). For the second phase, each BS computes its remaining capacity on the radio and backhaul resources. Thus, if there are available resources, then the BSs broadcast the remaining capacity on both radio and backhaul resources. Subsequently, BS receives association requests from devices, and sorts the requests \(ar_{j}\) in terms of utility (steps 33-34). For each association request, BS determines whether device QoS requirements do not exceed the available capacity. More specifically, if device \(i\) is accepted by BS \(j\), then the sum of its cost on radio \(\alpha_{ij}\) and backhaul \(\beta_{ij}\) should not exceed the total radio \(\phi_{j}\) and backhaul \(\pi_{j}\) costs. This phase is repeated until there are not available resources on BSs or devices have already sent an association request to all BSs on its candidate set.
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 1 Introduction
This paper is structured as follows: Section 2 reviews related work in AoA estimation and indoor localization including ML approaches. Section 3 presents the proposed PLAE model, detailing its structure and algorithm. Section 4 discusses the experimental setup and results, demonstrating the model's performance. Finally, Section 5 concludes the paper and suggests directions for future research.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 4 Results and Discussion
### Collapsing on a Single Optimization Metric  
In a two-objective minimization problem, all feasible solutions are located in a two-dimensional Pareto front. It is often beneficial to simplify the problem by converting both objectives into a common metric that aligns with the other objectives. By converting CO\({}_{2}\) emissions into a monetary cost, we can merge these objectives into a single cost-based metric, thus facilitating a more straightforward optimization process. The carbon price reference is used to convert the emissions into a cost (Figure 8). Based on recent trends in the EU ETS, an average carbon price of 80 EUR/tCO\({}_{2}\) has been used as a reference value. Then, as shown in Equation (11), the lifecycle CO\({}_{2}\) emissions (\(E_{CO2}\)) are multiplied by this carbon price (\(P_{CO2}\)) and added to the total costs for each scenario (\(T_{EUR}\)), leading to a final system cost considering GHG emissions (\(Total_{costs}\)). \[C_{CO2}=E_{CO2}\cdot P_{CO2} \tag{11}\]  
\[Total_{costs}=C_{CO2}+T_{\xi} \tag{12}\]  
The calculated emission costs are added to the total annual cost of each scenario to obtain a combined metric (Equation (12)). The minimum added cost is the optimal energy solution setup that minimizes both costs and emissions. The total costs considered for each scenario range from 6.591 M EUR for the minimum and, therefore, the optimum (highlighted with a red dot in Figure 11), to about 13 M EUR for the most costly, with the current and previous scenarios for El Hierro being in the range of 10 M EUR. The actual energy setup (WH) is far from the minimum, even from any of the solutions obtained by EPLANopt. The scenario with only diesel generators, named (D), although it is classified as not environmentally friendly, has similar characteristics as other possible solutions obtained by the algorithm. The scenario with the minimum of both objectives is detailed in Table 6. Similarly to Table 5, the current table includes detailed data on the installed power, generation, capacity factors, and costs for the diesel power plant, wind, and reverse pumping storage components. Figure 11: Optimization with collapsing in economic metrics. There are several aspects to highlight from the information provided in Table 6. For example, the installed diesel power is reduced appreciably, from more than 13 MW in the current case to 9 MW, more than 30%; this reduction is not more pronounced since the system acts as a \"backup\" in those specific situations in which there is no other generation alternative available. The diesel generation is reduced from over 30 GWh in the current case. The increase in the power of the diesel power is reduced in the current case.