esa-sceva/satcom-chunk-collection huggingface dataset:
# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 2 System Model
### Channel Model and Quantization Model
In [30], \(\mathbf{q}_{\alpha}\) can be approximated as follows:  
\[\mathbf{R}_{\widetilde{\mathbf{y}}\widetilde{\mathbf{y}}}=\mathbf{R}_{ \mathbf{y}\widetilde{\mathbf{y}}}=\alpha\mathbf{R}_{\mathbf{y}\mathbf{y}}, \tag{5}\]  
and  
\[\mathbf{R}_{\widetilde{\mathbf{y}}\widetilde{\mathbf{y}}}\approx\alpha^{2} \mathbf{R}_{\mathbf{y}\mathbf{y}}+\alpha(1-\alpha)\text{diag}\big{(}\mathbf{R} _{\mathbf{y}\widetilde{\mathbf{y}}}\big{)}. \tag{6}\]By using the Equations (5) and (6), we can approximate the Bussgang decomposition of Equation (2) as follows:  
\[\widetilde{\mathbf{y}}=Q_{\alpha}(\mathbf{y})\approx\alpha\mathbf{y}+\widehat{ \mathbf{q}}_{\alpha}, \tag{7}\]  
where \(\widehat{\mathbf{q}}_{\alpha}\sim\mathcal{CN}\left(0,\mathbf{R}_{\widehat{ \mathbf{q}}_{\alpha}\widehat{\mathbf{q}}_{\alpha}}\right)\in\mathbb{C}^{N}\) and the correlation matrix as follows:  
\[\mathbf{R}_{\widehat{\mathbf{q}}_{\alpha}\widehat{\mathbf{q}}_{\alpha}}= \alpha(1-\alpha)\text{diag}\big{(}\mathbf{R}_{\mathbf{yy}}\big{)}. \tag{8}\]  
This approximation can effectively approximate quantization noise, but it has limitations in that it approximates nonlinear characteristics with linear ones. In [31], the optimal distortion factor \(\rho_{\alpha}\) of uniform and non-uniform quantizers according to the number of bits was tabulated. The distortion parameters are expressed in Table 1
## 3 EMF-Aware Routing Framework for WMNs
### Theoretical Approach of EMF Exposure on WMNs
#### 3.1.1 General Description of the Exposure Model  
We consider a WMN of \(N\) nodes and define \(\overline{\xi(t)}=\{\xi_{1}(t),\xi_{2}(t)\dots\xi_{N}(t)\}\) as a vector with the exposure induced by all nodes at a particular moment of time \(t\). From the routing point of view, the exposure grows every time a node sends or forwards a packet and remains constant in those moments in which the node is idle. We will use the transmission power required to send a packet as a proxy metric for the exposure. Furthermore, we assume that nodes are able to dynamically adapt their transmission power. It seems sensible to state that the growth rate of the exposure induced by a node, when transmitting a packet, can be expressed as a function of its transmission power. Hence, the exposure proxy can also be seen as the slope (first derivative) of the exposure, and which without the loss of generality is defined as a function (\(\alpha\)) of the transmission power, \(\alpha(P_{\text{tx}})\), as defined in Equation (2).  
\[\xi_{k}^{t}(t)=\begin{cases}\alpha(P_{\text{tx}})&\text{if node $k$ transmits a packet at time $t$}\\ 0&\text{otherwise}\end{cases} \tag{2}\]  
A reduction of the slope of this per node accumulated exposure function, would actually yield a smaller exposure. For instance, it would be beneficial to always establish the minimum power for transmitting or forwarding, i.e., selecting the next hop according to the power required to reach it. On the other hand, it would also be interesting studyinghow the accumulated exposure pace varies along the time. This can be matched to the second derivative, \(\xi^{\prime\prime}_{k}(t)\), of the accumulated exposure at each individual node.
## 2 Basic Principle of Digital Image Correlation Method  
From then, now, the previous studies have contributed to update the algorithm of the digital image correlation method (DICM), which is the core technology of the CCDC system, and improved the accuracy of the measurement by CCDC system. Hereby, the basic principle of the CCDC system was introduced as below.  
Digital image correlation method is an optical measurement based on digital image processing and numerical calculation. According to the basic principle of DIMC, speckle pattern is taken as the information resource of the deformation for an object [15; 16]. Figure 1 displays the initial speckle pattern and Figure 1 shows the speckle pattern after deformation. Comparison of Figure 1,b, the geometric coordination of a moving point, P, can be traced by using the digital image correlation method.  
Figure 1: Displacement information for the digital image correlation method. (**a**) Initial information; (**b**) Target information.  
Based on the digital image correlation method, shown in Figure 2, a charge-coupled device camera has been used by the CCDC system to capture a series of speckle patterns and transferred to a data collector. In the meantime, the computer software, which developed on the base of the digital image correlation method, analyzed the database efficiently, and output the transient strain of a specimen. The measurement range of the non-contact video gauge is up to a strain of 100%, which can capture the full range of strain till specimens fails. The high quality video also can be recorded to review the test process once more. The practice of the non-contact video system is shown as Figure 3.  
In present study, the CCDC pictured with 2452 pixels \(\times\) 2056 pixels and a constant aperture of 1:1.8. The object distance of the special lens was 174 mm for material tests, and the magnification ratio of the lens was 0.338. The maximum frequency of data collection was 15 Hz. The resolution ratio of displacement was 0.012 \(\upmu\)m and that of the strain was 5 \(\upmu\)e.
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 4 EBET and EEBET: Proposed Techniques
### Protocol Operation  
The following sub-sections provide detailed functioning of our proposed schemes; EBET and EEBET.
# A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation
## 2 Previous Works
Edges are extracted with a Prewitt filter and pseudo-edges are removed using Zernike moments. Final parameter estimation for elliptical model fitting is performed using the M-Estimator Sample Consensus (MSAC) algorithm. In [16], the authors propose a multi-step image processing pipeline for optical navigation. In their pipeline authors include non-local filtering, morphological opening, and gradient direction processing to pre-process the input image and obtain relevant edges. Final centroid and radius estimation is performed with Least Squares. In the paper, the authors do not take into account other sources of noise other than Gaussian noise. In our work, we extend the analysis including image distortion and frame drop. In [17], authors derive a mathematical model for a nanosatellite and implement an Extended Kalman Filter to support Autonomous Optical Navigation. In [18], authors describe a flexible and easy to implement interplanetary autonomous optical navigation system. The algorithm is able to use images of two celestial bodies to improve positioning and achieving high accuracy in noise-free images. In [6; 9; 11], the authors used the Canny edge detector algorithm as a mean to obtain a cleaner thin edge. We preferred to reduce the computational complexity by using the Prewitt edge filter and by moving the outlier filtering in the last steps of the processing pipeline. The RANSAC algorithm has been already used in [8; 14] to estimate celestial bodies radius. In our work, we introduced an edge mask based on morphological operations and an online model validation by sector binning. We also tested the robustness of our approach to various sources of noise (Gaussian noise, scanline drop, and frame alteration).
# Deployment Strategy of Shore-Based Cooperative Units for the Internet of Inland Vessels
## 2 Ship-Shore Communication Needs of Internet of Inland Vessels
### System Architecture and Operating Mode of the Internet of Inland Vessels
Signaling needs to be transmitted between different links in a communication network, and each link needs to be analyzed and processed. A series of operations and controls are formed through interaction to ensure that the user information can be transmitted effectively and reliably. Assuming that \(R_{sig}\) is the signaling ratio in the network is, the total amount of information and data required to be transmitted in the system per unit time is  
\[I_{total}=\frac{L_{DLL}}{1-R_{sig}} \tag{2}\]  
Re-transmissions and packet losses are mainly affected by the congestion of the communication network and the communication environment. Assuming that \(P_{ret}\) is the probability of data packet re-transmissions in a certain scenario and \(\delta\) is the maximum number of allowed data packet re-transmissions, the data-packet loss rate is  
\[\eta_{lost}=P_{ret}^{\delta+1}+P_{ret}^{\delta+2}+P_{ret}^{\delta+3}+\cdots= \frac{P_{ret}^{\delta+1}}{1-P_{ret}} \tag{3}\]  
The average number of transmissions per data packet is  
\[F_{RLL}=1+P_{ret}+P_{ret}^{2}+\cdots+P_{ret}^{\delta}=\frac{1-P_{ret}^{\delta +1}}{1-P_{ret}} \tag{4}\]  
After considering the re-transmission rate, the total amount of data transmitted by the system per unit time (i.e., the system throughput) is  
\[Q_{sys}=I_{total}\cdot F_{RLL} \tag{5}\]
## 3 Double-Beam System Traversed by a Moving Force
### Mathematical Formulation
#### 3.1.2 Pinned-Pinned Boundary Conditions  
Substituting Equations (28) and (29) into Equation (53) and setting \(C\) to be one result in  
\[M_{nn}^{(i)}=\frac{1}{2}\rho_{2}A_{2}l\left[1+s_{\rm m}\Big{(}A_{n}^{(i)} \Big{)}^{2}\right] \tag{69}\]  
where  
\[A_{n}^{(i)}=1+\frac{1-\Big{(}\frac{\omega_{n}^{(i)}}{\omega_{2,n}}\Big{)}^{2} }{s_{e}}(n\pi)^{4} \tag{70}\]  
and \(\omega_{n}^{(i)}\) and \(\widetilde{\omega}_{2,n}\) are obtained from Equations (9) and (30), respectively.  
Substituting Equation (29) into Equation (52) and setting \(C\) to be one result in  
\[\tilde{q}_{n}^{(i)}+\Big{(}\omega_{n}^{(i)}\Big{)}^{2}q_{n}^{(i)}=-\frac{fA_ {n}^{(i)}}{M_{nn}^{(i)}}\sin\Big{(}\frac{n\pi v}{l}t\Big{)} \tag{71}\]  
It is noticed that Equation (71) has the same form as that of a pinned-pinned single beam excited by a moving force. The solution of Equation (71) is  
\[q_{n}^{(i)}(t)=\frac{p_{n}^{(i)}}{1-\Big{(}s_{n}^{(i)}\Big{)}^{2}}\Big{[}\sin \Big{(}\frac{n\pi v}{l}t\Big{)}-s_{n}^{(i)}\sin\Big{(}\omega_{n}^{(i)}t\Big{)} \Big{]} \tag{72}\]  
where \(p_{n}^{(i)}\) is the same expression as that in Equation (59) and \(s_{n}^{(i)}\) is  
\[s_{n}^{(i)}=\frac{n\pi v}{l\omega_{n}^{(i)}} \tag{73}\]  
The displacements of the primary beam and the secondary beam are  
\[w_{1}(x,t)=\sum_{n=1}^{m}\sin\Big{(}\frac{n\pi}{l}x\Big{)}\Big{[}A_{n}^{(1)}q _{n}^{(1)}(t)+A_{n}^{(2)}q_{n}^{(2)}(t)\Big{]} \tag{74}\]  
\[w_{2}(x,t)=\sum_{n=1}^{m}\sin\Big{(}\frac{n\pi}{l}x\Big{)}\Big{[}q_{n}^{(1)}( t)+q_{n}^{(2)}(t)\Big{]} \tag{75}\]  
For a pinned-pinned beam, \(x_{\rm max}=0.5l\) and \(w_{\rm s}\) becomes  
\[w_{s}=-\frac{fL^{3}}{48E_{2}I_{2}} \tag{76}\]
# Space Debris In-Orbit Detection with Commercial LiDAR Sensors
## 2 Materials and Methods  
Figure 1 presents the key characteristics of the LiDAR sensors currently available on the market that are suitable for installation on small satellites, including their lightweight, compact size, and low power consumption. For this reason, only short-range sensors are included in this table. Short-range proximity LiDARs can support up to 500 m with one-(1D) or three-dimensional (3D) imaging. Long-range LiDAR configurations can support up to 1000 km range, but their power consumption, size, and weight are too substantial for integration onto a small satellite or CubeSat.  
Thus, if we use an automotive LiDAR sensor onboard a satellite, on the one hand, we eliminate the presence of clouds, thus avoiding their major disadvantage for particle detection from the ground. On the other hand, we take advantage of its light weight, dimensions, and power consumption to use it in particle detection directly in the vicinity of the satellite.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
###### Abstract  
In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST). The anatomy of a 5G NR frame as a sequence of OFDM symbols is presented, and different PRS configurations are described. It is shown that spectral analysis of time-varying channel impulse response weights, estimated with the help of PRS pilots, can be used for the detection of transmitted signal reflections from moving vehicles and the calculation of their time and frequency/Doppler shifts. Different PRS configurations with varying time and frequency reference signal densities are tested in simulations. The peak-to-noise-floor ratio (PNFR) of the calculated radar range-velocity maps (RVM) is used for quantitative comparison of PRS-based radar scenarios. Additionally, different echo signal strengths are simulated while also checking various observation window lengths (FFT lengths). This study proves the practicality of using PRS pilots in remote sensing; however, it shows that the most dense configurations do not provide notable improvements, while also demanding considerably more resources.  
Keywords:integrated sensing and communication (ISAC); 5G NR; positioning reference signals (PRS); channel impulse response (CIR); remote sensing; moving vehicle detection +
Footnote †: journal: Sensors
## 2 Results and Discussion
### Effect of Germination on the Volatile Profile of IWG  
#### 2.2.1 Fiber Selection  
The choice of coating extraction phase is the first and most important step in the development of any SPME method since the fiber coating material determines the affinity between the analytes and the fiber [34]. The selection should be made considering the analytes, the sample, and the extraction mode [35]. For this study, three fibers (polydimethylsiloxane--PDMS, divinylbenzene/polydimethylsiloxane--DVB/PDMS, and divinylbenzene/carboxen/polydimethylsiloxane--DVB/CAR/PDMS) were evaluated regarding the suitability for the extraction of volatiles from IWG. Total area was used as the response (Figure 2).  
The fiber composed of PDMS presented the lowest response for total area, while the fiber composed of DVB/CAR/PDMS showed the highest, with an area 5 times larger than that presented by the PDMS fiber. The DVB/PDMS fiber showed a total area 20% lower than the DVB/CAR/PDMS fiber.  
Many factors contribute to the large variation in response to the different types of fibers. Firstly, the greater thickness of the triple fiber (80 \(\upmu\)m) compared to the others (7 \(\upmu\)m and 65 \(\upmu\)m) achieves better extraction efficiency [36]. Secondly, the coatings have different affinities for volatile compounds and different extraction mechanisms, resulting in varying responses. PDMS is a liquid polymer that extracts volatile compounds based on the absorption of molecules inside the coating and has shown good efficiency in extracting nonpolar compounds. Conversely, in solid coatings such as CAR and DVB, extraction occurs only on the surface via an adsorption process through various interactions such as pi-pi bonding, hydrogen bonding, or van der Waals interactions [34]. When PDMS is combined with DVB, a bipolar fiber is achieved, allowing the extraction of both polar and nonpolar compounds [37]. Based on the best response obtained, the following optimization step was conducted using DVB/CAR/PDMS fiber.
## 5 Conclusions  
A method of signal strength enhancement was proposed for the magnetostrictive patch transducer (MPT) by dynamic magnetic field optimization with a soft magnetic patch (SMP) in guided wave inspection. The enhancement was achieved by decreasing the resistance of the magnetic circuit in wave generation and restricting the induced dynamic magnetic field in wave detection. A numerical investigation was successfully performed for the improvement of dynamic magnetic fields in the magnetostrictive patch and region around the coils. Experimental validation was carried out for the SH0 wave inspection in an aluminum plate. The main findings are summarized as follows:  
1. The amplitude enhancement of the dynamic magnetic field in the magnetostrictive patch is beneficial for SH wave generation and the concentration of the induced magnetic field by the magnetostrictive patch in wave detection is good for the measurement by the coils.
2. The SMP can significantly decrease the resistance of the magnetic circuit for the dynamic magnetic field in wave generation and concentrates the induced magnetic field in wave detection. The relative permeability should be more than 2000 and the thickness should be over 0.15 mm. The increased distance between the SMP and the coils was bad for the improvement.
3. Covered by the SMPs with the relative permeability of 2800, the MPTs provided a better performance in both processes of wave generation and detection compared to those without the SMPs. The improvement of the SMP in wave detection was more than that in wave generation. The largest magnification can be 12.7 dB when the transducer is used as an actuator and sensor at the same time.
4. For ideal results, using the SMPs with the relative permeability of 2800, a thickness over 0.3 mm and close fitting between the SMP and coils is recommended.  
The SMP is expected to improve the signal strength of the MPT for guided wave inspection without sacrificing the flexibility and lightness of the transducer or increasing the system power. For future studies, the SMPs made of different materials with different relative permeabilities will be tested and different types of environmental factors, including temperature and electromagnetic disturbing, will be investigated. The MPT with the SMP will also be examined in field tests of pipe and tank floor scanning.  
[PERSON] conceived and designed the models and methods presented; [PERSON], [PERSON] and [PERSON] helped with the conception and experiments; [PERSON] performed the simulations, experiments and data analysis; [PERSON] and [PERSON] wrote this paper.  
The work was supported by the National Natural Science Foundation of China (Grant No. 51875511) and National Key Research and Development Program of China (Grant No. 2018 YFC0809000).  
The authors declare no conflicts of interest.  
Figure 17: Amplitude magnification for the magnetostrictive patch transducer with different lift-off distances of the SMP at several typical frequencies. T1 was an actuator and sensor.
# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
###### Abstract  
The smart city is an ecosystem that interconnects various devices like sensors, actuators, mobiles, and vehicles. The intelligent and connected transportation system (ICTS) is an essential part of this ecosystem that provides new real-time applications. The emerging applications are based on Internet-of-Things (IoT) technologies, which bring out new challenges, such as heterogeneity and scalability, and they require innovative communication solutions. The existing routing protocols cannot achieve these requirements due to the surrounding knowledge supported by individual nodes and their neighbors, displaying partial visibility of the network. However, the issue grew ever more arduous to conceive routing protocols to satisfy the ever-changing network requirements due to its dynamic topology and its heterogeneity. Software-Defined Networking (SDN) offers the latest view of the entire network and the control of the network based on the application's specifications. Nonetheless, one of the main problems that arise when using SDN is minimizing the transmission delay between ubiquitous nodes. In order to meet this constraint, a well-attended and realistic alternative is to adopt the Machine Learning (ML) algorithms as prediction solutions. In this paper, we propose a new routing protocol based on SDN and Naive Bayes solution to improve the delay. Simulation results show that our routing scheme outperforms the comparative ones in terms of end-to-end delay and packet delivery ratio.  
s smart city; routing protocol; SDN; Naive Bayes; IoT +
Footnote †: journal: Computer and Software Engineering
# Reduction of Dust on Solar Panels through Unipolar Electrostatic Traveling Wave
## 3 Electrode Design
Figure 2:1 Dust distribution and accumulation as a result of single channel application to: (**a**) M1 model; (**b**) M2 model. The cleaning performance of the panels is generally expressed as the ratio between the weight of the dust before cleaning and the weight of the dust removed after cleaning [14; 23; 43]. The M1 plate (Figure 12) had 0.56 g of dust evenly spread over its surface. After cleaning, 0.10 g of dust remained, giving an efficiency of 82.14%. The M1 plate drew 2.44 A during the cleaning process. The M2 plate (Figure 13) had 0.56 g of dust evenly spread over its surface. After cleaning, 0.06 g of dust remained, giving an efficiency of 89.28%. The M2 plate drew 1.33 A during the cleaning process at the same voltage and frequency. The M2 design is more efficient than the M1 design both in terms of cleaning and electrical power consumption.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 1 Introduction
The goal of this review is to describe the state of art of gasification of plastic waste, examining different types of feedstock: Polyolefin for packaging (PP, PE), plastic mixes containing other polymers such as PVC, PET, PMMA, and plastic waste with biomass (co-gasification). Operating conditions such as the gasification temperature, the gasifying agent, the bed material, and the use of catalysts have been carefully reviewed. Tar formation and the operational techniques for its elimination were also examined. Finally, an in-depth section was dedicated to supercritical water gasification (SCWG), a thermochemical conversion route well known for biomass but nowadays little used for plastic waste. The present work reviews the literature of the past fifteen years (2005-2020). To the best of the Authorsknowledge, it was not possible to find review focusing on supercritical water gasification as chemical recycling technologies for plastic.