esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Approximation of the Functions \(C(\gamma)\) and \(C^{-1}(r)\)  
We approximated the function \(C(\gamma)\), reported in Equation (8) of [16], with Mathematica(r), using a numeric integration therein specified.  
Figure 4: BER with respect to \(E_{b}/N_{0}\) in dB with \(d_{l}=10\), \(n=1000\), and iterations number \(I=30\).  
Here we report 66 (approximate) values (\(\times 10^{4}\)) of \(C(\gamma)\), corresponding to \(\gamma=0.1,...,3.35\) with steps of 0.05. (The first value is 0.1314, the second is 0.1889, and so on.)  
\begin{tabular}{l l l l l l} \hline
1314 & 5738 & 7804 & 8841 & 9379 & 9664 \\
1889 & 5993 & 7929 & 8905 & 9413 & 9682 \\
2417 & 6231 & 8047 & 8966 & 9445 & 9699 \\
2905 & 6454 & 8158 & 9023 & 9475 & 9715 \\
3356 & 6663 & 8263 & 9077 & 9504 & 9730 \\
3774 & 6859 & 8361 & 9128 & 9530 & 9745 \\
4162 & 7042 & 8453 & 9176 & 9556 & 9759 \\
4523 & 7215 & 8540 & 9222 & 9580 & 9771 \\
4859 & 7376 & 8622 & 9264 & 9603 & 9784 \\
5173 & 7528 & 8699 & 9305 & 9624 & 9795 \\
5465 & 7670 & 8772 & 9343 & 9645 & 9806 \\ \hline \end{tabular}  
Figure 10 reports a graph of \(C(\gamma)\) obtained with the same software.  
To implement an approximation of the inverse \(C^{-1}(r)\) we used a spline interpolation of the above approximatively reported values \((\gamma_{i},C(\gamma_{i}))=(C^{-1}(r_{i}),r_{i})\), with \(0.1314<r_{i}<0.9806\):  
s[y_] = Interpolation[v, y];  
where \(v\) is the array of the 66 pairs of values \((C^{-1}(r_{i}),r_{i})\).  
Moreover, in [16] we also reported a couple of functions, one inverse of the other, approximating \(C(\gamma)\) and its inverse \(C^{-1}(r)\), with forms, respectively,  
\[C_{1}(\gamma)=1-e^{u\gamma^{w}+v}\qquad C_{1}^{-1}(r)=\Big{(}\frac{\log(1-r)+ v}{u}\Big{)}^{\frac{1}{u}}\]  
with  
\[u=-1.286\qquad v=0.01022\qquad w=0.9308.\]
# An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
## 2 ESP in Tubular Space
### Directed Graph  
We discretize \(\Omega\) into a series of meshed circular disks corresponding to the cross-sections perpendicular to \(\mathbf{c}(s)\). By \(S_{0},\ldots,S_{N+1}\), we denote the meshed circular disks where \(S_{0}\) contains the starting point (source) \(\mathbf{\mathfrak{P}}\) and \(S_{N+1}\) includes the destination \(\mathbf{\Omega}\). The distance between two consecutive disks along the centerline curve is \(h=\frac{L}{N+1}\). As the shortest path from the source to the destination must obviously pass through each cross-section at only one point, we have the weighted directed graph \(G(V,A)\), as shown in Figure 2. This directed graph is defined by a finite set \(V\) of _vertices_ and a set \(A\) of _arcs_ between those vertices [1]. All vertices of the graph (except \(\mathbf{\mathfrak{P}}\) and \(\mathbf{\Omega}\)) are located at the nodes of the meshed disks \(S_{1},\ldots,S_{N}\). We define two vertices as adjacent if they are connected by one arc. Then, every two adjacent nodes in the graph are located on two consecutive disks. The source \(\mathbf{\mathfrak{P}}\) is connected with all nodes of disk \(S_{1}\). Each node of disk \(S_{i}\) is connected by one arc to every node of disk \(S_{i+1}\) for all \(i\in\{1,\ldots,N-1\}\). Eventually, every node of disk \(S_{N}\) is directly connected to the destination \(\mathbf{\Omega}\).
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.6 Classification Techniques  
Different classification techniques were used in these studies. Since the goal of many researchers was to improve the classification accuracy and the ITR of their systems, they employed more than one classification technique in order to compare the results. More specifically, ref. [20] used Extended-FBCCA and TRCA; Ref. [24] employed extended CCA and extended TRCA; Ref. [25] tested CCA, FBCCA, and eOACCA; Ref. [28] compared LSVM, KNN, LDA, and DT; Refs. [31; 32] used SVM, KNN, and ANN; Ref. [38] employed BLDA, LDA, SWLDA, SVM, and SepCNN; Ref. [39] used CCA and FBCCA; Ref. [40] compared CCA with their proposed algorithm; Ref. [41] employed CCA, FBCCA, and TRCA; Ref. [44] utilized Center-ECCA and SVM; and Ref. [45] employed LDA and SVM. As for the rest of the studies that employed a single classification algorithm, KNN was used by [14], EMSI was tested by [15; 29], shrinkage-rLDA was used by [16], LDA was employed by [17; 21], FBCCA was utilized by [22; 27], SVM was used by [26; 42], FB-CNN was employed by [30], FFT was used by [34], and ANN was employed by [35].
# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
## 3 Results
### Case Study 2
This demonstrates the software capability to process GNSS measurements extracted from portable devices in a good way, with differences in the order of 5 cm from three hours later than the convergence time (i.e., from about 12:00 UTC onward), and maximum differences in the order of 10 cm in the whole considered time span. This highlights the possibility to exploit raw GNSS measurements obtained from smartphones for ZTD estimations, at the same time guaranteeing the positioning accuracy. This aspect opens new frontiers, not only in positioning solutions with portable devices, but also for atmospheric monitoring.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs in the mmWave Frequencies
#### 3.5.3 Selection of Suitable Antenna Technology  
In this section, four techniques for the construction of mmWave antennas are briefly described. Each one has pros and cons, so depending on the application and the manufacturer's objectives, the appropriate choice should be made.  
Printed circuit board (PCB) technology is known for its simplicity of use. It is more appropriate for lower mmWave frequency ranges due to its drawbacks, which include low performance at high frequencies, manufacturing limits, and difficulty supporting multi-level designs.  
Low-temperature co-fired ceramic (LTCC) technology uses multilayer structures for integrated circuit packaging, conductor printing, and passive component embedding. This procedure makes use of resistive, dielectric, or conductive pastes. After that, these several layers are laminated and burned as a single unit. LTCC solves several efficiency problems that PCB and hybrid thin-film technologies are facing. Its accuracy, miniaturization capability, and compatibility with antenna in package (AiP) and system on package (SOP) applications make it ideal for high-frequency bands.  
Radio frequency micro-electro-mechanical systems (RF MEMS) has changed mmWave communication by enabling reconfigurable antennas with low power consumption, minimal electromagnetic interference, and low insertion loss. There are two ways to implement this technique: using \"hybrid\" or \"monolithic\" MEMS devices. The method offers control of the frequency and the radiation pattern with the addition of auxiliary reconfigurable components.  
\"Thin film technology\" enables the fabrication of mmWave antennas with high efficiency and low interconnect losses and can be used to produce system on chip (SOC) and substrate-integrated circuits (SIC). Several methods are used to deposit extremely thin layers with different thicknesses on various substrates. These employ processes like \"physical vapor deposition\" (PVD) and \"chemical vapor deposition\" (CVD), offering scalable antenna arrays, compact designs, and versatile feed options such as CPW-MS integration (refs. [92; 99; 100]).
## 3 LEO-LEO Occultation Simulation and Assessment Methods
### Global Coverage Fraction of Occultation Events  
The Earth's surface is divided into spatial grid cells according to a certain rule, and the ratio of the accumulated area of grid cells visited by occultation events to the total area of the grid (i.e., the global surface area of the Earth) is defined as the global coverage fraction (GCF) of occultation events, which can be described by  
\[GCF=(A_{occ}/A_{E})\times 100\% \tag{12}\]  
where \(A_{occ}\) indicates the accumulated area of the grid cells visited by occultation events and \(A_{E}\) represents the total grid area, i.e., the entire Earth's surface in the given case of a global coverage fraction (for a prescribed regional coverage fraction, e.g., for the country of China only, one would use the entire area of the country as a reference).  
To quantitatively assess the observation efficiency of the LEO-LEO constellations, occultation events were simulated and an event ID number, latitude, longitude, time, and some other necessary information were stored in an occultation table for statistical analysis. The specific analysis process is as follows: 1 accumulate the occultation events in the study period in the corresponding geographic grids according to the location and time information; 2 calculate the global coverage fraction of the occultation events using the above method; and 3 conduct a quantitative analysis of the spatiotemporal distribution of occultation events for specific constellations. Since the locations of occultation events are commonly marked with longitude and latitude, the entire Earth was divided into 2592 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) grid cells according to latitude and longitude in this study. This density of gridding was found to be suitable for our purpose of globally illustrating geographical patterns of GCFs, without making the cell resolution too fine-grained, so that the cells receive robust coverage by occultation events for our LEO-LEO study design.
# Nanoplasmonic Sensor Based on Surface Plasmon-Coupled Emission: Review
## 7 The Effect of Nanomaterial in SPCE  
Briefly, SPCE signals were obtained by inducing the surface plasmon resonance in the metal layer due to fluorescence emission of fluorophores. SPCE is used in microscopy and sensing the chemical and biochemical molecules. The SPCE response depends on fluorophore emission and the fluorescence emission enhancement is a subject of great interest in sensor and biosensing with high sensitivity. Nanomaterials such as gold nanoparticle, silver nanoparticle, zinc oxide nanoparticle, graphene quantum dots, and carbon quantum dots have the potential of enhancing the fluorescence emission of fluorophores. Gold and silver nanoparticles enhance the fluorescence via the interaction between the induced localized surface plasmon with excited state of fluorophores during two main processes [38; 40]. First, the nonradiative emission occurs, and the excited state of fluorophores will be coupled with the localized surface plasmons, increasing the electrical field due to polarization of charge density on the surface of metal cluster. Second, the photon absorption of fluorophores located in gold or silver, is enhanced with electrical field around the nanoparticle [41; 42]. Consequently, the population of excited state increases and the intensity of fluorescence emission is enhanced. This phenomenon is called metal-enhanced fluorescence (MEF).
## 3 DCPSO with Cooperation
### Distributed Cooperative Particle Swarm Optimization with Cooperation
#### 3.2.6 Time Complexity Analysis and Remarks  
The time complexity analysis on the proposed DCPSO algorithm with cooperation is given as follows:  
(1) The time complexity of initialization of all sub-swarms is \(O\left(\sum\limits_{i=1}^{N}M\right)\);  
(2) The time complexity of updating all particles' velocities and positions is \(O\left(\sum\limits_{i=1}^{N}M\right)\);  
(3) The time complexity of calculating fitness of particles is \(O\left(\sum\limits_{i=1}^{N}\left(M\cdot N_{\text{threat}}\right)\right)\), where \(N_{\text{threat}}\) is the number of threats, including obstacles and no-fly zones;  
(4) The time complexity of fitness modification for all particles is \(O\left(\sum\limits_{i=1}^{N}\left(M\cdot N\cdot N_{\text{ph}}\right)\right)\), where \(N_{\text{ph}}\) is the number of equidistant points of the discretized PH path;  
(5) The time complexity of updating the personal best locations and group best locations is \(O\left(\sum\limits_{i=1}^{N}M\right)\).  
Hence, the total time complexity is:  
\[\begin{array}{l}O\left(\sum\limits_{i=1}^{N}M\right)+O\left(\sum\limits_{i= 1}^{N}M\right)+O\left(\sum\limits_{i=1}^{N}\left(M\cdot N_{\text{threat}} \right)\right)+O\left(\sum\limits_{i=1}^{N}\left(M\cdot N\cdot N_{\text{ph}} \right)\right)+O\left(\sum\limits_{i=1}^{N}M\right)\\ =O\left(\sum\limits_{i=1}^{N}\left(M\cdot N\cdot N_{\text{ph}}+M\cdot N_{\text {threat}}+3M\right)\right)\end{array} \tag{18}\]  
**Remark 1**.: _According to the time complexity analysis, the fitness calculations and modifications of particles occupy the most computing time. This is because the safety detection is conducted in fitness calculation and modification, with the collision detection between UAVs in particular taking up a large portion of time. For collision checking between UAVs, coordinates of each equidistant point have to be solved by the Newton-Raphson iterative method, which is a complicated and time-consuming process._  
**Remark 2**.: _It can be seen in Figure 2 that the algorithm proposed in this paper has a distributed structure. Therefore, it can be implemented in parallel on multiple computers for reducing the computing time._  
**Remark 3**.: _The length of paths produced will be closed to equal, but may not be exactly the same. The differences can be diminished by slightly increasing the parameters \(\left(m_{0},\quad m_{1}\right)\) of the resulting paths which have the smaller lengths._  
**Remark 4**.: _In view of the limited flexibility of a single PH path, only a few no-fly zones and obstacles are considered here. For complicated environments, a path of several PH curves connected one by one is more suitable._
## 3 System Model
### Trust Model  
Trust renders efficient data transmission in WSN by rejecting malicious nodes in the network to reduce miscommunication. The distrust parameter of network devices is computed such that the communication in the network mainly relies on the trust degree of the nodes. Here, the five different trust factors--known as direct trust, recent trust, indirect trust, and trust, which depend on the data bytes, and trust, which depends on error--are considered. It shows the effectiveness and reliability of the nodes and provides timely and integral services [40]. However, the trust factor is computed by Equation (7):  
\[Y_{r,s}=\frac{1}{5}\Big{[}Y_{r,s}^{dir}(k)+Y_{r,s}^{\rm indir} \left(k\right)+Y_{r,s}^{rec}(k)+Y_{r,s}^{db}(k)+Y_{r,s}^{err}(k)\Big{]}, \tag{7}\]  
where \(Y_{r,s}\) shows the trust parameter of node r towards node s, \(Y_{r,s}^{dir}(k)\) shows direct trust, \(Y_{r,s}^{indir}(k)\) represents indirect trust, \(Y_{r,s}^{rec}(k)\) implies recent trust, \(Y_{r,s}^{db}(k)\) signifies trust based  
Figure 1: System model of IoT.  
on data bytes, and \(Y_{r,s}^{err}(k)\) indicates trust based on error. Direct trust is measured using the difference of actual as well as estimated time [40] and is computed Equation (8).  
\[Y_{r,s}^{dir}(k)=\frac{1}{3}\bigg{[}Y_{r,s}^{dir}(k)-\bigg{(}\frac{k_{approx}-k_ {\text{exp}}}{k_{approx}}\bigg{)}+\tau\bigg{]}. \tag{8}\]  
Here, \(k_{approx}\) denotes approximate time, \(k_{exp}\) denotes expected time, and \(\tau\) signifies the witness factor. The indirect trust [40] is computed in Equation (9):  
\[Y_{r,s}^{\text{indir}}\left(k\right)=\frac{1}{a}\sum_{r=1}^{a}Y_{r,s}^{\text{ dir}}(k), \tag{9}\]  
where \(a\) shows the total count of the neighbours of node \(r\). The recent trust is calculated based on the above trust parameters [40] and it is represented in Equation (10):  
\[Y_{r,s}^{rec}(k)=\beta*Y_{r,s}^{\text{dir}}\left(k\right)+\left(1-\beta\right)* Y_{r,s}^{\text{indir}}\left(k\right). \tag{10}\]  
The value of \(\beta\) is set to 0.3. The trust computed for data communication between the \(r\)th and \(s\)th nodes depends on the data bytes [40], and is given in Equation (11).  
\[Y_{r,s}^{db}(k)=\frac{1}{2}*\bigg{[}\frac{Y_{r}^{db}}{m}+\frac{Y_{s}^{db}}{m} \bigg{]}. \tag{11}\]  
Here, \(Y_{r}^{db}\) denotes the total data bytes sent at the \(r\)th node, and \(Y_{s}^{db}\) indicates total bytes received at the \(s\)th node. The trust based on error [40] is computed by Equation (12):  
\[Y_{r,s}^{err}(k)=\frac{1}{TT}*\sum_{e=1}^{TT}E_{e}, \tag{12}\]  
where \(TT\) implies total transmission, and \(E_{e}\) defines the error, which can be set to 0 or 1.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 4 Simulation Tool to Evaluate Link Cost Estimation Procedures  
In order to evaluate the LS-based, the LQI-based, and our proposed estimation procedures, we used the ns-3 simulator [50]. Ns-3 is an open-source simulator specifically designed to simulate communication protocols. It has been being actively developed for almost 10 years and has been supported by grants from DARPA and NSF.  
The current version of ns-3 contains models for the IEEE 802.11 and the IEEE 802.15.4 MAC and physical layers. For this study, we implemented the required portions of the ZigBee APS and network layers, including an implementation of the M2O routing algorithm, generation of LS messages, and protocol overheads.  
Ns-3 has detailed channel models to simulate wireless channels. Ns-3 manages the transmission of packets from the transmitter to any receiver, considering both noise and interference. We used ns-3's channel and propagation model for IEEE 802.11 and IEEE 802.15.4 without changes. Details of these models can be found in [51].
# FLINT: Flows for the Internet of Things
## 2 Case Study--Port of the Future
### Localization  
Location-based Services (LBSs) are an essential aspect of IoT applications, as they provide valuable context information. For instance, sensor measurements from IoT devices in a port can only be interpreted properly if they are correlated with the correct measurement location. Typically, Global Navigation Satellite Systems (GNSSs) such as Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou Navigation Satellite System (BDS) and Galileo are used for outdoor localization. Cellular networks or LPWANs are used to transmit location data from GNSS receivers on IoT devices to an IoT platform. To reduce the overall cost of IoT devices or to enable outdoor localization on devices with an extremely low energy budget, it is possible to omit GNSS receivers and apply localization methods such as Time Difference of Arrival (TDoA), Angle of Arrival (AoA) or Received Signal Strength (RSS)-based methods to the LPWANs instead [12; 13; 14]. These methods are especially popular for indoor localization scenarios. Technologies such as Wi-Fi, Bluetooth Low Energy (BLE) or Ultra Wideband (UWB), can cope with the fact that GNSS coverage is not available in indoor environments [15].  
Hence, an LBS can obtain location information from a wide range of data sources, depending on the current environment and active technology of an IoT device. Since the performance of a wireless localization method strongly depends on estimation errors related to the environment, the network deployment and the IoT device itself [16], LBSs must be able to intelligently switch between data sources while estimating their reliability [17]. Therefore, there is a need for flexible, heterogeneous platforms such as FLINT that can incorporate an additional module for localization purposes. Specifically for a port use case, this module can switch between outdoor localization with GNSS if a valid fix is available, TDoA localization with LoRaWAN if a transmission is received by at least four gateways, or accurate indoor fingerprinting localization when FLINT detects an active Wi-Fi network.
## 3 Distributed Mission Scheduling Model
### APSOA for Single Satellite Scheduling
#### 3.3.3 Static Scheduling Evaluation  
The evaluation methods for static scheduling mainly include three indicators: the first one is the overall payoff of the scheduling plan (_Payoff_), and the second one is the mission completion rate (\(CR\)). These two indicators mainly reflect the profit of the satellite mission execution plan. The third one is the running time (\(RT\)), which mainly reflects the solving speed of the optimization algorithm.
## 2 Materials and Methods
### Methods  
To obtain the nanofibrous matrix, a 150 mL flask was prepared, followed by the addition of 15 g of PLA and 15 g of dioxane, which was then supplemented with acetone. The prepared solution was placed on a magnetic stirrer with temperature control until a homogeneous solution was obtained. The solution was subsequently drawn into a 5 mL syringe (needle: 1.2 \(\times\) 40 mm) and electrospun at a flow rate of 80 \(\mu\)L/min. To prepare the gold nanoparticle solution, 50 mL of water was heated to 90 \({}^{\circ}\)C, followed by the addition of 0.5 mL of sodium citrate solution (2.2 g sodium citrate/50 mL) and subsequently 0.5 mL of HAuCl4 solution (50 mg HAuCl4/10 mL). The solution was placed on a heated magnetic stirrer for one hour. In subsequent samples, the composition was modified by incorporating periclase and gold nanoparticles, and the flow rate was increased to 150 \(\mu\)L/min. Successfully obtained samples are listed in Table 1. Magnesium oxide (MgO) was synthesized from reagent-grade Mg(OH)2 by thermal dehydration above 300 \({}^{\circ}\)C. Calcination was conducted at 500-1200 \({}^{\circ}\)C (4 h per sample). To prevent rapid dehydration and powder ejection, \(\sim\)10-15 g of Mg(OH)2 was heated in quartz crucibles with a \(\leq\)5 mm layer thickness.  
MgO obtained below 900 \({}^{\circ}\)C was highly reactive, while above 900 \({}^{\circ}\)C, it exhibited a periclase-like crystalline structure, enhancing chemical resistance and suitability for refractory materials. X-ray diffraction confirmed increased crystallinity with temperature, with the highest peak intensities observed above 1000 \({}^{\circ}\)C, indicating well-formed periclase-phase MgO.  
Electrospinning was performed using a custom, self-built setup consisting of a high-voltage power supply, a syringe pump, and a rotating drum collector housed in a temperature- and humidity-controlled chamber. Temperature and relative humidity were digitally monitored. The flow rate (1 mL/h) was controlled via the syringe pump and was selected based on prior studies reporting optimal uniform fiber formation [41]. The electrospinning setup consisted of a high-voltage power supply, a syringe pump, a metal needle (21G), and a grounded rotating drum collector covered with aluminum foil. The electrospinning process was conducted under the following optimized conditions: applied voltage: 18-22 kV; flow Rate: 0.8-1.2 mL/h; needle-to-collector distance: 15 cm; and rotating drum speed: 300 rpm. The electrospinning was carried out at room temperature (25 \({}^{\circ}\)C) with 50% relative humidity, and the nanofibers were collected for 6 h to obtain membranes with a sufficient thickness for further characterization. After electrospinning, the membranes were vacuum-dried for 24 h to remove residual solvents. The resulting membranes were cut into circular samples (diameter: 15 mm) for subsequent characterization, degradation studies, and biological evaluation.