esa-sceva/satcom-chunk-collection huggingface dataset:
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Cooperative Games
It is presented by the following equation:  
\[T_{net}=min(T_{co1},T_{co2},T_{non-co1},T_{non-co2}). \tag{4}\]The residual energy \(E_{o}\) from [31] is defined as:  
\[E_{o}=E_{init}-E_{t}, \tag{5}\]  
where \(E_{init}\) is the initial energy of the sensor node and \(E_{t}\) is the energy of the sensor node after a specific round. The CH-C-TEEM was compared with the trustworthy energy-efficient MIMO (TEEM) that uses the same pay-off function and game formulation to select the CH [56], and we identified that the CH-C-TEEM performed better in terms of network lifetime and residual energy. According to the simulation results, the residual energy using CH-CH-TEEM was 98% higher than TEEM and the nodes in TEEM were alive for 5000 rounds, whereas the CH-C-TEEM could successfully run 11,750 times in the same simulation environment. This drastic rise in output is achieved merely by introducing merging and splitting coalitions during the coalition formation among cluster members. In Figure 2, the CHs in clusters 1 and 2 send data to the sink node cooperatively by making a pair. Furthermore, the CHs in clusters 5 and 3 act as a pair and send their data cooperatively. Technical strengths:  
* Cooperation among CHs leads to energy efficiency;
* Consideration of network security via elimination of the malicious nodes. Technical weaknesses:
* Routing metrics like packet delivery ratio and end-to-end delay are also important and should have been considered while designing the protocol;
* In [31], it was mentioned that the coalition formation of the sensor nodes was performed using a merge and split algorithm. However, nowhere in the paper [31] was this algorithm elaborated. This led to ambiguity in terms of understanding the functionality of the protocol;
* CH-C-TEEM was compared with TEEM [56], a protocol developed by the same authors of [31]. To prove the superiority of CH-C-TEEM, it is necessary that the protocol is compared with more of the existing state-of-the-art routing techniques in terms of important routing metrics. Applications:  
* Highly applicable for a channel fading environment where network security is a priority with lesser packet loss ratio. Possible future improvements:
* Coalition formation algorithm can be elaborated;  
Figure 2: Cooperative transmission of data by cluster heads (CHs) in pairs. * More comparative analysis with the existing protocols;
* Performance analysis with important routing metrics like packet-delivery-ratio, end-to-end delay can be considered.
## 3 Proposed Algorithms
### Computational Complexity  
In Table 1, the computational complexity of the proposed SCSS-APSA, MCSS-APSA, and SMCSS-APSA are compared with that of APSA, CAPSA, and CSS-APSA in terms of the total number of additions, multiplications, comparisons, and exponents. It is clear that the simplified algorithms are more efficient in computation than CAPSA and CSS-APSA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Multiplications** & **Additions** & **Comparisons** & **Exponents** \\ \hline APSA & \(2M(K+1)+1\) & \(M(2K+1)-1\) & 0 & 0 \\ CAPSA & \(2M(2K+3)+12\) & \(M(4K+4)+6\) & 2 & 0 \\ CSS-APSA & \(2M(K+1)+M+8\) & \(M(2K+2)+4\) & 2 & 2 \\ SCSS-APSA & \(2M(K+1)+M+8\) & \(M(2K+2)+4\) & 2 & 0 \\ MCSS-APSA & \(2M(K+1)+M+8\) & \(M(2K+2)+4\) & 2 & 2 \\ SMCSS-APSA & \(2M(K+1)+M+8\) & \(M(2K+2)+4\) & 2 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity of APSA, CAPSA, CSS-APSA, SCSS-APSA, MCSS-APSA, and SMCSS-APSA at each iteration.  
```
Initialization: \(0<\mu_{2}<\mu_{1}\), \(0<\xi_{1}\leq 1\), \(s(0)=\xi_{1}\), \(1<C\), \(1\leq\beta\), and \(\mathbf{w}_{1}(0)=\mathbf{w}_{2}(0)=\mathbf{0}\). Computation:  while \(\{\mathbf{u}(n),d(n)\}_{n\geq 1}\) available do \(1:\mathbf{w}_{1}(n)=\mathbf{w}_{1}(n)+\mu_{1}\frac{\mathbf{U}_{s}(n-1)}{\sqrt {\xi_{1}+\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}}\), \(2:\mathbf{w}_{2}(n)=\mathbf{w}_{2}(n)+\mu_{2}\frac{\mathbf{U}_{s}(n-1)}{\sqrt {\xi_{1}+\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}}\), \(3:\mathbf{w}(n)=\lambda(n)\mathbf{w}_{2}(n)+(1-\lambda(n))\mathbf{w}_{1}(n)\), \(4:e(n)=d(n)-\mathbf{u}^{T}(n)\mathbf{w}(n)\), \(5:s(n)=s(n-1)-\mu_{s}\frac{\partial|e(n)|}{\partial s(n-1)}\), for SCSS-APSA \(6:\lambda_{s}(n)=\frac{C}{2-s(n)}-\left(\frac{C}{2}-\frac{1}{2}\right),\) \(7:\text{if }s(n)<-ln\left(\frac{C+1}{C-1}\right)\):  then \(s(n)=-ln\left(\frac{C+1}{C-1}\right)\), \(8:\text{else if }s(n)>ln\left(\frac{C+1}{C-1}\right)\):  then \(s(n)=ln\left(\frac{C+1}{C-1}\right)\), \(9:\text{else }s(n)=s(n)\), end. for MCSS-APSA \(6:\lambda_{\beta}(n)=\frac{C}{1+\exp(-\beta s(n))}-\left(\frac{C}{2}-\frac{1}{ 2}\right),\) \(7:\text{if }s(n)<-ln\left(\frac{C+1}{C-1}\right)/\beta\):  then \(s(n)=-ln\left(\frac{C+1}{C-1}\right)/\beta\), \(8:\text{else if }s(n)>ln\left(\frac{C+1}{C-1}\right)/\beta\):  then \(s(n)=ln\left(\frac{C+1}{C-1}\right)/\beta\), \(9:\text{else }s(n)=s(n)\), end. for SMCSS-APSA \(6:\lambda_{s\beta}(n)=\frac{C}{2-\beta s(n)}-\left(\frac{C}{2}-\frac{1}{2} \right),\) \(7:\text{if }s(n)<\frac{-2}{(C-1)\beta}\):  then \(s(n)=\frac{-2}{(C-1)\beta}\), \(8:\text{else if }s(n)>\frac{2}{(C+1)\beta}\):  then \(s(n)=\frac{2}{(C+1)\beta}\), \(9:\text{else }s(n)=s(n)\), end. end while
```  
**Algorithm 1**The proposed Algorithms
## 3 Time-Frequency Distribution of Communication Signals
### Time Frequency Distribution of Quadrature Modulated Signal  
To improve the spectral efficiency, the modulation types used in current communication are quadrature modulation, such as QPSK and M-QAM. In a single carrier pulse shaping quadrature modulation system, the two pulse shaped signals for in-phase and quadrature paths are denoted as \(I(t)\) and \(Q(t)\), respectively. Define \(s_{b}(t)=I(t)+jQ(t)\), then \(s_{b}(t)\) is reformulated as \(\sqrt{I^{2}(t)+Q^{2}(t)}e^{\phi(t)}\), where \(\phi(t)\) is  
**Case 3.** The magnitude of \(I(t)\) and \(Q(t)\) are not constant, and \(I(t)\
eq\pm Q(t)\). In this case, \(Q^{\prime}(t)I(t)-I^{\prime}(t)Q(t)\
eq 0\), thus \(\
u(t)\
eq 0\). The center frequency of the modulated signal varies with time.
## 3 Mathematical Formulation/Performance Metrics
### Power Model  
Power and energy are calculated based on the execution of a given traffic pattern. \(Pw_{act,j}\) and \(Pw_{inact,j}\) are component \(i\)'s active and passive power at 1.0 V and 1.0 GHz, respectively. Let \(alpha_{i,j}\) be the active reading of component \(j\) in router \(i\); then, the average power \(Pw_{av}\)[35] is expressed by  
\[Pw_{av}=\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{N_{i}}[\alpha_{i,j}\cdot Pw_{act,j}+(1-\alpha_{i,j})\cdot Pw_{inact,j}] \tag{4}\]  
where \(Pw_{act,j}\) is \(j\)'s component power when active, \(Pw_{inact,j}\) signifies component \(j\)'s power while inactive, and \(\alpha_{i,j}\) reflects active measurement of component \(j\) in the router \(i\).
## 6 Experimental Simulations
### Picking
#### 6.1.3 The SOP Solution  
As mentioned in Section 3.3, the Sequential Ordering Problem was adapted to the problem we study in this section. Different from the TSP solution, a zone can be visited on multiple occasions to pick up products with different priorities, thus the set \(V\) of vertices is associated with the products, and each vertex/product is characterized by the two labels (zone and priority score). The edges are constructed according to the vertices contained in \(V\), and the cost associated with each edge can be retrieved from the Origin-Destination travel time matrix in Table 1. To find the optimal SOP solution, we first simplify the graph by sorting the vertices in ascending order in terms of priority score. Simulation of travel times for the BOPS orders with the optimal SOP solution are reported in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{Order 1} & \multicolumn{2}{c}{Order 2} & \multicolumn{2}{c}{Order 3} & \multicolumn{2}{c}{Order 4} & \multicolumn{2}{c}{Order 5} \\ \hline \(t_{ij}\) & Zone & \(t_{ij}\) & Zone & \(t_{ij}\) & Zone & \(t_{ij}\) & Zone & \(t_{ij}\) & Zone \\ \hline / & 1 & / & 1 & / & 1 & / & 1 & / & 1 \\
39.84 & 3 & 39.84 & 3 & 39.84 & 3 & 39.84 & 3 & 39.84 & 3 \\
31.76 & 6 & 31.76 & 6 & 38.82 & 10 & 38.82 & 10 & 28.24 & 7 \\
27.06 & 10 & 16.47 & 7 & 16.47 & 11 & 16.47 & 11 & 23.53 & 10 \\
16.47 & 11 & 23.53 & 10 & 25.92 & 12 & 25.92 & 12 & 16.47 & 11 \\
25.92 & 12 & 16.47 & 11 & 29.69 & 15 & 29.69 & 15 & 25.92 & 12 \\
29.69 & 15 & 25.92 & 12 & & & & & 29.69 & 15 \\  & & 29.69 & 15 & & & & & & \\ \hline
170.75 s & & 183.69 s & & 150.75 s & & 150.75 s & & 163.69 s \\ \hline \multicolumn{2}{c}{Order 6} & \multicolumn{2}{c}{Order 7} & \multicolumn{2}{c}{Order 8} & \multicolumn{2}{c}{Order 9} & \multicolumn{2}{c}{Order 10} \\ \hline \(t_{ij}\) & Zone & \(t_{ij}\) & Zone & \(t_{ij}\) & Zone & \(t_{ij}\) & Zone & \(t_{ij}\) & Zone \\ \hline / & 1 & / & 1 & / & 1 & / & 1 & / & 1 \\
39.84 & 3 & 15.58 & 2 & 15.58 & 2 & 39.84 & 3 & 15.58 & 2 \\
31.76 & 6 & 24.26 & 3 & 24.26 & 3 & 31.76 & 6 & 24.26 & 3 \\
23.53 & 9 & 38.82 & 10 & 31.76 & 6 & 23.53 & 9 & 31.76 & 6 \\
16.47 & 10 & 16.47 & 11 & 20.00 & 8 & 16.47 & 10 & 20.00 & 8 \\
16.47 & 11 & 25.92 & 12 & 16.47 & 9 & 16.47 & 11 & 16.47 & 9 \\
25.92 & 12 & 29.69 & 15 & 32.99 & 15 & 25.92 & 12 & 20.00 & 11 \\
29.69 & 15 & & & & & 29.69 & 15 & 25.92 & 15 \\ \hline
183.69 s & & 150.75 s & & 141.06 s & & 183.69 s & & 154.00 s & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation of travel times for \(10\) BOPS orders when the TSP optimization is implemented. Figure 2: Partial representation of the oriented cost graph for order 8.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
###### Abstract  
Most of Optical Camera Communication (OCC) systems benefit from the rolling shutter mechanism of Complementary Metal-Oxide Semiconductor (CMOS) cameras to record the brightness evolution of the Light-Emitting Diode (LED) through dark and bright strips within images. While this technique enhances the maximum achievable data rate, the main difficulty lies in the demodulation of the signal extracted from images, subject to blooming effect. Thus, two main approaches were proposed to deal with this issue, using adaptive thresholds whose value evolves according to amplitude changes or detecting signal variations with the first-order derivative. As the second method is more robust, a new demodulation method based on the detection of the zeros of the first-order derivative of the extracted signal was proposed in this paper. Obtained results clearly show an improvement in the extracted signal demodulation compared to other methods, achieving a raw Bit Error Rate (BER) of \(10^{-3}\) around 50 cm in a Line-Of-Sight scenario, and increasing the maximum communication distance by 43.5%, reaching 330 cm in the case of a Non-Line-Of-Sight transmission.  
Optical Camera Communication; rolling shutter; demodulation; first-order derivative; On-Off Keying; Bit Error Rate +
Footnote †: journal: _Photonics_  
0
Footnote 0: _E-mail:_ [EMAIL_ADDRESS]
## 5 Conclusions  
The performance of a three-elements grazing-incidence monochromator designed for high spectral resolution HHs experiments are presented. The optical configuration uses a plane grating illuminated by the light converging from a concave focusing mirror. The set-up maintains the spectral focus in a fixed position on the output-slit-plane by changing the subtended angle on the grating. The realized monochromator works in the 12-50 eV region. An output bandwidth lower than 10 meV has been achieved in the whole region of operation. When compared with synchrotron PGMs, for which the working range is between 60-410 eV, comparable performances in terms of resolution (5000-20,000) and efficiency have been obtained. The peculiarity of this design is related to the cost-to-benefit ratio. Since all the optical components used in the monochromator are available on the market with high optical quality and modest prices, the configuration can be useful for cost-effective instrumentation, both on synchrotron beamlines and on novel laboratory-based applications using HHs sources at high-repetition rates.  
[PERSON] wrote the paper and performed the ray-tracing simulations; [PERSON] conceived the optical configuration and coordinated all the activities; [PERSON], [PERSON], and [PERSON] performed the instrument characterization.  
The results presented in the paper have been partially funded by Extreme Light Infrastructures (ELI) funds of the Italian Ministry for Education, University and Research. The authors would like to thank [PERSON] for his support on the efficiency measurements performed at ELETTRA Synchrotron.  
The authors declare no conflict of interest.  
Figure 10: Top-view schematic representing the insertion of the high-resolution monochromator within a high-repetition rate beamline dedicated to pump–probe experiments. The system includes a laser system and optics, the XUV beamline, and the IR delay-line. BS, beam splitter; FL, piano-convex focusing lens; TM\({}_{1}\), tangential focusing and sagittal collimating toroidal mirror; CM, tangential focusing cylindrical mirror; PG\({}_{1}\)-PG\({}_{2}\), interchangeable rotating plane gratings; PM, rotating–translating plane mirror; TM\({}_{2}\), tangential focusing and sagittal focusing toroidal mirror; RM, IR recombination mirror.
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Packet Formulation
#### 3.4.5 Filtering  
The ChipSats used matched filtering as the filtering technique, as required by requirement 6.0 (see Table 4). Matched filtering not only boosts the S/N ratio and adds signal rigidity like FEC, but it also allows for greater theoretical gain. It does this using a 64-byte sequence for each binary bit, which are called PRN codes. Specifically, the codes used for alpha are gold codes. Each ChipSat has two PRN codes: one for sending a binary 1 and the other for sending a binary 0. Matched filtering also has the unique feature of enabling multiple devices to use the same communication channel. This is known as code division multiple access (CDMA), and it works by assigning different pairs of PRN codes that are as orthogonal as possible to each device. The receiver then adjusts to detect the set of PRN codes that are being sent. See Figure 23 for a graphical explanation.  
The number of devices that are using the CDMA determines how the PRN codes are selected. The PRN codes are created to maximize the orthogonality between codes. Orthogonality is essential to optimize the allowable hamming distance. For example, with only one device, one could choose to send a binary zero 511 times and a binary one 511 times. But with multiple devices, to distinguish which device is transmitting the data, the PRN codes cannot be exactly opposite. So, the more devices in a system, the more likely  
Figure 23: CDMA allows the receiver to recognize which device is transmitting.  
Figure 22: Addition of a preamble.  
there is cross-correlation. Fortunately, in Alpha, only two ChipSats are used, so there are four PRN sequences. With only four PRNs, a hamming distance of 127 or 63 possible bit flips is achieved. In the ChipSat flight code, bits for future matched filtering are added after FEC. The respective PRN sequences are defined at the start of the TX task. A set of \"for\" loops runs through the preambles and each of the FEC-encoded bits. An \"if\" statement assigns them either PRN [0] or PRN [1] based on the respective binary value. The final transmitted parcel is built in order by appending each of the 16 PRN sequences (from the 16-bit FEC-encoded data). The final transmitted parcel length is 1024 bytes. Since there are 22 parcels, that makes each packet length a total of 22,528 bytes.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 2 Antenna and Optimization Environment Description
### Antenna Geometry  
Reflectarrays (RAs) are antenna structures originally aimed to improve directivity. They consist of a low profile planar array of printed radiating elements illuminated by a primary feed source [22].  
Usually these antennas are characterized by a flat reflector: this solution reduces the production costs and the antenna volume which represents an important aspect especially in aerospace applications. Moreover, with respect to parabolic reflectors, it is possible to have more customizable solutions, such as conformal reflectors [23].  
The planar reflector consists of several patches with different geometrical parameters, which affects their reflection proprieties, such as the reflection phase shift and the attenuation in the field amplitude. A proper selection of the geometrical parameters and electromagnetic response of all the patches can be used for obtaining the desired antenna performance [24].  
Figure 1 shows the design scheme of the two most important components of the analyzed antenna: the upper element is the feed, that in this specific case is a horn antenna, while the lower element is the reflector. The reflector properties are achieved by properly selecting the patches (red elements in the figure). The analyzed geometry is composed by \(24\times 24\) square patches of different size.  
The selected patches are square shaped because they are able to provide good reflection properties at different sizes. These are shown in Figure 2: the upper diagram shows the reflection amplitude as function of the patch size, while the bottom part shows the angle variation induced by the reflection. This patch is suitable for the specific application because the reflection losses are low for all the analyzed patch sizes, and the reflection angles almost cover \(360^{\circ}\).  
The analyzed problem is the achievement of scanning capabilities, i.e., the possibility of changing the main radiation direction. In this application, the reflector has a fixed geometry, while the feeder can move in the space creating a relative angle to the reflector normal direction (\(\theta_{inc}\)). This movement causes a rotation of the output radiation pattern of the entire antenna.  
The radiation pattern properties can be modified by means of two different groups of design variables: the first one consists in the patches' sizes. Changing these, the radiation pattern is modified for all scan angles. The second group of design variables are the beam deviation factors.  
Figure 1: Geometry of the planar reflectarray antenna: feeder (upper dark red horn antenna) and the reflector with in red the all patches.  
For a given scan angle (the desired output angle of the radiation pattern with respect to the vertical line, indicated with the symbol \(\theta_{scan}\)), the beam deviation factor (BDF) is defined as the difference between the incident angle (\(\theta_{inc}\)) and the direction of the maximum amplitude of the radiation pattern (\(\theta_{max}\)). This is shown in Figure 3.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 3 Materials and Design Methods
### Electrical Wiring  
The wiring diagram for the mechatronics components is shown in Figure 4. Holybro PIX32 Pixhawk controller was selected to regulate commands for heading and steering. The controller is integrated with the necessary sensors to operate for example 3-axis gyroscope and a 3-axis accelerometer. The package includes a 168 MHz CPU with 256 KB of RAM. The electronics speed controller (ESC) from Blue Robotics controls the speed of the motor on the thruster to generate thrust for driving the USV motion. The ESC has a maximum current of 30 A and an operating voltage of 7-26 Volts (2-6 s). T-100 thruster is preferred as a propulsive device used for attitude control. The thruster can generate a maximum thrust of around 5.2 lbf. Hyperion Lithium polymer battery is utilized as the power supply for the USV system. The selected battery is 2500 mAh, three cells (3 s), with a nominal voltage of 3.7 V per cell. Holybro Telemetry Radio 915 MHz is chosen as the wireless data communication between the USV and ground control station (GCS). The maximum wireless communication range is around 1.6 km. Remote control (RC) transmitter-receiver is used for remote command from a pilot. Holybro Buzzer is employed as an indicator for the completed process, or an error occurs in a controller. Holybro Power Modul is applied to distribute energy supply from the battery to the controller. Holybro Micro M8N Compass and GPS are sensors implemented as navigation device for the USV. Even though IMU and GPS are sensitive to the magnetic environment, they are lightweight and small in size, and have low energy consumption, which are important advantages for USV [4].  
Additionally, apart from its compatibility with open-source ground control stations, the essential aspect of choosing Holybro products is its price. All Holybro items shortlisted above only cost $180. As a result, the total cost is roughly $524.97 in which $246.46 is allocated for the T-100 Blue Robotics thruster, $27.95 for the battery, and about $70.42 for frame material and manufacturing process. Whilst the Notebook Computer and the  
Figure 3: Exploded view dan materials of the proposed USV.  
remote control are an asset which has been ready. Compared with an affordable Unmanned Surface Vehicle developed by [PERSON] et al., this cost is steeply low as its price is highly under $3000 [11]. The development cost is the same as the USV developed by [PERSON] et al. which lower than $1000 or $1131.07 [23].