esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Computational Complexity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Three** & **Secondary** & **Time-Domain** & **Frequency-** & **Frequency-** & **Frequency-** & **Domain Filter** & **Time-Domain** & **Filter** & **Computational** \\
**Algorithms** & **Path** & **Reference** & **Regerence** & **Domain** & **Error Signal** & **Coefficient** & **Update** & **Coefficient** & **Complexity** \\ \hline FDSFBANC & 0 & 0 & 4\(\text{Nlog}\)2\(N+8N\) & 2\(\text{Nlog}\)2\(N\) & 8\(N\) & 0 & 6\(\text{Nlog}\)2\(N+16N\) \\ \hline WSMANC & 0 & \(N\Delta\) & 0 & 0 & 0 & \(N^{2}\) & \(N^{2}+N\Delta\) \\ \hline SAIMC & \(2N^{2}+N\) & \(N^{2}\) & 0 & 0 & 0 & \(N^{2}\) & 4\(N^{2}+N\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relationships between the calculation complexity and the adaptive filter length \(N\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Adaptive Filter** & **SAIMC** & **WSMANC** & **FDSFBANC** \\
**Length \(N\)** & **SAIMC** & **WSMANC** & **FDSFBANC** \\ \hline
2 & 18 & 40 & 56 \\ \hline
4 & 100 & 88 & 136 \\ \hline
8 & 264 & 208 & 320 \\ \hline
16 & 1040 & 544 & 736 \\ \hline
32 & 4128 & 1600 & 1664 \\ \hline
64 & 16,448 & 5248 & 3712 \\ \hline
128 & 65,664 & 18,688 & 8192 \\ \hline
256 & 262,400 &
## 3 Materials and Methods
### Spectrophotometric Analysis of LE and DM
#### 3.5.4 Radical Scavenging Activity  
The antioxidant activity was determined in compliance with [PERSON] et al. [64], by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical. Specifically, 4.5 mL of DPPH in EtOH (0.1 mM) were added to the aqueous solutions of LE (0.5 mL), to be then kept at room temperature for 30 min in the dark. The analysis was performed at 517 nm, with the same spectrophotometer employed for TPC and TFC evaluation. The radical scavenging activity was expressed, referring to the trolox calibration curve, as mg of trolox equivalents (TE) per g of DE. For DM, the same ethanol/water extracts were prepared, as for the two previous spectrophotometric tests, and then they were analyzed as reported in the present section. The antioxidant capacity was measured as mg TE per g of DW.
## Nomenclature  
\begin{tabular}{l l} \(A\)EG & Annual energy gain (\%) \\ \(A\) & Height above sea level of the location (km) \\ \(a_{0}\), \(a_{1}\) & Empirical constants of the Hottel model (dimensionless) \\ \(DEG\) & Daily energy gain (\%) \\ \(M\)\(\!\!
## 5 Optimum \(M\) with Background Light  
### Power of Background Light  
In an FSO receiver, background light from the sun or sunlight reflected by celestial bodies or clouds, air, and ground structures, are significant sources of noise and electronic offsets for detectors. BGL-induced current flowing through the APD will add extra shot-noise, just as the detector's dark current does.  
Values of \(P_{BGL}\) depend on the receiver's aperture size, the field-of-view (FoV) angle of the detector, the optical bandwidth of background blocking filters, and the spectral behavior of the background source itself, which also implies the wavelength (e.g., blue sky and haze at certain elevations, sunlit clouds, and spectral luminance of the background structure behind a data source) [28; 29; 30; 31; 32]. These environmental parameters can be summarized by the spectral irradiance, \(L_{\sigma,\Omega,\lambda}\), in [W/m\({}^{2}\)/nm/sr], see Table 1. We only state here some of the typical figures regarding the sun far above the horizon to allow a rough estimation of \(P_{BGL}\), where a detailed analysis would be required for the precise values in individual  
Figure 11: Dependency of BER against \(M\), without and with BGL (\(P_{BGL}=50\) nW), and three received signal levels for 1 Gbps.  
scenarios (more precisely, the angle of the sun-earth target and the sun elevation would have to be considered):  
Small celestial bodies (like planets or stars) exhibit a very limited apparent size which normally falls below the detector's FoV. Looking into the sun must be absolutely avoided in any case.  
\[P_{BGL}=L_{e,\Omega\lambda}\cdot A\cdot\Omega\cdot\Delta\lambda \tag{22}\]  
where the receiver-telescope aperture area, \(A\), is in m\({}^{2}\), the optical filter bandwidth, \(\Delta\lambda\), is in nm, and the detector's solid angle FoV, \(\Omega\), is derived from the more common full-flat angle \(\omega\): \(\Omega=4\pi\sin^{2}\omega\) (Table 2).  
With typical OGS geometries, and for a 1550 nm signal wavelength, we find the following exemplary values for the background light:  
We find that 88 nW is a large value for \(P_{BGL}\) from the horizon sky during daytime (and a similar value when looking onto the sun-illuminated moon disk), while 2.4 nW serves as a minimum value. At nighttime, of course, BGL should be negligible (except when celestial bodies are in the FoV). For further examples, we chose 50 nW as a typical strong BGL value.
## 4 Security Threats and Attack Methods in Blockchain-Based Supply Chain Management
### Smart Contract Vulnerabilities and Manipulation Threats  
While smart contracts employed in supply chains enhance trust and efficiency through decentralized and autonomous operations, they can also harbor vulnerabilities arising from programming errors or malicious attacks. Such vulnerabilities may result in data manipulation, a loss of funds, or operational disruptions within the supply chain.  
* _Re-entrancy attacks_: Re-entrancy attacks occur when attackers exploit vulnerabilities in smart contracts by invoking a function multiple times before the target contract updates its state [65]. Within the supply chain context, malicious actors can manipulate payment processes or withdraw additional tokens from contracts. This poses a significant threat to secure payments or deliveries managed through smart contracts in supply chains.
* _Denial of Service (DoS) attacks_: DoS attacks disrupt the operation of smart contracts in blockchain systems, causing resource exhaustion and transaction delays. Attackers may execute transactions that consume excessive gas, preventing smart contracts from functioning properly, or overwhelm the mempool with unnecessary transactions, obstructing transaction verification [66]. In supply chain management, such attacks can disrupt critical processes, like product tracking, payment transactions, and inventory updates, causing delivery delays and inventory inaccuracies. To mitigate these risks, smart contracts should undergo rigorous security audits, gas limits must be optimized, and protections against spam transactions should be implemented.
* _Integer overflow/underflow_: Smart contracts utilized in supply chain management perform crucial calculations related to inventory tracking, product deliveries, and payments. However, vulnerabilities, such as programming errors and integer overflow or underflow, can lead to significant financial losses [67]. For example, a warehouse smart contract tracking inventory could generate incorrect inventory data due to integer boundary violations, spreading these inaccuracies throughout the supply chain. Attackers could exploit these calculation errors to gain unauthorized access, block order completions, or trigger unintended deliveries [68; 69]. Integer boundary violations in Solidity may cause incorrect calculations by the Ethereum Virtual Machine (EVM) [68]. Since traditional tests are insufficient to detect these errors, secure mathematical libraries such as SafeMath or the automated error detection provided in Solidity version 0.8.0 should be utilized. Alternatively, a safer language such as Vyper may be preferred [70; 71].
## 1 Introduction
Various approaches and experiments are available to extract different parameters in forest \(AGB\) estimation using optical and microwave remote sensing datasets, including tree height, tree diameter at breast height (_DBH_), and tree volume [25; 26]. There are various empirical models and allometric equations developed to estimate forest \(AGB\) from field data at plot level using hand-measured techniques, viz., tree \(DBH\), height, and sometimes wood density to improve accuracy [27]. There are various relational models developed between basal area or tree diameter, weighted height (also known as Lorey\({}^{\prime}\)s height) parameters, and \(AGB\) for more accurate biomass evaluation [28; 29; 30], and direct forest \(AGB\) achieved from \(SAR\) and \(LiDAR\) data using the above models from large areas [7; 31; 32; 33]. Similarly, optical remote sensing studies show that \(LAI\), \(FVC\), and vegetation indices have significant potential to evaluate forest \(AGB\)[34]. The \(FVC\) and \(LAI\) have proved useful in leaf biomass evaluation through a proportionality function [35]. Usually, \(AGB\) using field data at the plot level calculates the sum of foliage and wood biomass [36]. To calculate the \(LAI\) or \(FVC\), several vegetation indices have been experimented [37; 38; 39], which are still challenging for specific forest types. Furthermore, the accuracy of the \(AGB\) estimation from both optical and microwave remote sensing data is limited by the accuracy level of data processing and parameter extraction, including ecological, biotic, and topographic factors [6; 13; 14; 22; 23; 24; 25; 26; 40; 41; 42]. In this study, an empirical model based on field \(AGB\) and \(FVC\) from optical remote sensing has been worked to simplify and more rapidly evaluate forest \(AGB\). The field information (tree height, tree \(DBH\), number of trees at plot level (30 by 30 m), tree stem density, and \(FVC\)) were collected for field \(AGB\) calculation from a test site within the Tundi reserved forest, Jharkhand state of eastern India.
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 4 The Performance Analysis of Dual-Hop Single-Relay System  
We assume that there is only one transmitter aperture and one DF relay \(R_{I}\), where the receiver uses the received instantaneous SNR to adjust the data rate. In this case, the received end-to-end (e2e) SNR of the system is limited by the minimum SNR of the individual hop. Therefore, the e2e SNR can be expressed by  
\[\gamma_{I}=min(\gamma_{s,I},\gamma_{I,d}), \tag{10}\]  
where \(\gamma_{s,I}\) is the SNR of the \(S\)-\(R_{I}\) link and \(\gamma_{I,d}\) is the SNR of the \(R_{I}\)-\(D\) link. The CDF of \(\gamma_{I}\) can be written as  
\[F_{\gamma(\gamma)}=F_{\gamma_{s},\gamma}(\gamma)+F_{\gamma_{I},d}(\gamma)-F_{ \gamma_{s},\gamma}(\gamma)F_{\gamma_{I},d}(\gamma). \tag{11}\]  
Using (4), the CDF of \(\gamma_{I}\) can be represented by  
\[F_{\gamma_{I}}(\gamma) =1-\overline{F}_{\gamma_{s,I}}(\gamma)\overline{F}_{\gamma_{I},d }(\gamma)\] \[=1-\overline{F}_{\gamma_{I}}(\gamma), \tag{12}\]  
where the complementary CDF of \(\gamma_{I}\) is given by  
\[\overline{F}_{\gamma_{I}}(\gamma)=\frac{\xi_{s,I}^{2}\xi_{I,d}^{ 2}}{\Gamma(a_{s,I})\,\Gamma(b_{s,I})\,\Gamma(a_{I,d})\,\Gamma(b_{I,d})}G_{3,3} ^{3,1}\Bigg{[}\frac{a_{s,I}h_{s,I}}{(b_{s,I}-1)}\bigg{(}\frac{\gamma}{\bar{ \gamma}_{s,I}}\bigg{)}^{\frac{1}{2}}\Bigg{|}\begin{matrix}1-b_{s,I},1+\xi_{s,I }^{2},1\\ 0,a_{s,I},\xi_{s,I}^{2}\end{matrix}\Bigg{]}\] \[\times G_{3,3}^{3,1}\Bigg{[}\frac{a_{I,d}h_{I,d}}{(b_{I,d}-1)} \bigg{(}\frac{\gamma}{\bar{\gamma}_{I,d}}\bigg{)}^{\frac{1}{2}}\Bigg{|} \begin{matrix}1-b_{I,d},1+\xi_{I,d}^{2},1\\ 0,a_{I,d},\xi_{s,d}^{2}\end{matrix}\Bigg{]}. \tag{13}\]
## 2 Tailoring Stiffness via Cellular Materials
#### Cellular Materials in Compliant Systems as Flexure Elements  
There is a great amount of evidence for the capability of Cellular Materials to achieve desired levels of compliance in certain directions while maintaining high levels of stiffness in others. However, only [PERSON] and [PERSON] [22] have applied a cellular lattice structure directly in the flexures elements of compliant joints, as shown in Figure 8. In [22], [PERSON] and [PERSON] introduced the concept of _lattice flexure_, which was not common before additive manufacturing techniques were widely available. They demonstrated that these types of flexures have a reduced bending stiffness when compared to traditional rectangular-section blade flexures of similar dimensions while maintaining comparable stiffness in off-axis directions. Making use of additive-manufactured flexures, they found that both type of lattice flexures--X-type and V-type--shown in Figure 8 have the potential to reduce the bending stiffness by 60-80% compared to a traditional blade flexure of similar size, and at the same time to exhibit a torsional/bending stiffness ratio as much as 1.7 times higher than an equal aspect-ratio blade flexure, and a transverse bending/motion-direction bending stiffness ratio up to 6.5 times higher than an equal aspect-ratio blade flexure.  
Figure 7: Guideline diagram to select an appropriate cellular material for tailoring stiffness.  
Aiming to modify or allow compliance, it is possible to tailor the stiffness of flexure elements via Cellular Materials, while obtaining other advantageous mechanical conditions such as mass reduction, stress relief, and increments in stiffness ratios. However, when specific stiffness values are desired, tailorability is possible by using optimization processes that modified the final configuration of predefined cellular structures (see Figure 2). Then, one may express the effective stiffness as a function of cell parameters (strut or wall thickness, length, width, angle, etc.). Although this approach to design is powerful, its disadvantage is that the true optimization of the resulting materials architecture is ultimately limited by the designers initial choice of cellular structure. Therefore, other types of optimization processes that overcome this limitation are reviewed in the following section. This approach, termed as Topology Optimization, results in the optimal topology by removing or adding material to a design domain.
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 2 Related Work
### Hybrid Terrestrial-Satellite Routing Schemes
Due to the limited number of ground stations, the short delay can hardly be guaranteed. In graph-based routing, the dynamic topology is modeled as a time-involved graph. Even though global information can be utilized to optimize the routing paths, the selected terrestrial-satellite link selection could disappear due to the frequent moving satellite. To this end, a dynamic analysis of the time-varying links is fundamental for enhancing the throughput. We exploit the hybrid routing algorithm in [39] to provide seamless integration of the satellite network with terrestrial networks, in which transmission paths can be selected adaptively according to the traffic demands of user terminals. Instead of setting a fixed time interval, an optimal topology updating interval is derived in this paper, through which effectiveness of the hybrid routing path is further improved.
# Polynomial Cancellation Coded DFT-s-OFDM for Low-PAPR Uplink Signaling
## 4 Simulation and Discussion  
In this section, the performance of PCC-DFT-s-OFDM discrete baseband signals is evaluated in terms of PAPR, BER, and PSD, where the latter two also consider the scenarios of a single user (medium bandwidth only) and two users (medium bandwidth and narrow bandwidth). The system parameterization conforming to the 3 GPP baseband standards [2; 3] are listed in Table 2. In the simulations, the Rapp model for a solid state PA (SSPA) [29] with smoothness factor \(p=2\) and 3 dB IBO are considered at the transmitter and perfect synchronization and channel estimation are assumed at the receiver. Furthermore, when \(d=0\) in the proposed method, conventional DFT-s-OFDM is carried out.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 5 Formation Flying Nanosatellite Bus
### Subsystems Design
#### 5.2.1 Guidance, Navigation, and Control Subsystem (GNC)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Range (km)**} & \multirow{2}{*}{**State**} & \multicolumn{3}{c}{**Relative Navigation Error (Mean, 3\(\sigma\))**} \\ \cline{3-5}  & & **Radial** & **In-Track** & **Cross-Track** \\ \hline \multirow{2}{*}{**50**} & Position [cm] & \(0.62\pm 11.50\) & \(0.03\pm 5.28\) & \(0.06\pm 11.59\) \\  & Velocity [cm/s] & \(0.24\pm 0.84\) & \(-0.00\pm 0.39\) & \(0.00\pm 0.58\) \\ \hline \multirow{2}{*}{**100**} & Position [cm] & \(0.85\pm 10.91\) & \(-0.00\pm 5.57\) & \(0.03\pm 15.64\) \\  & Velocity [cm/s] & \(0.48\pm 0.80\) & \(-0.01\pm 0.41\) & \(0.00\pm 0.59\) \\ \hline \multirow{2}{*}{**200**} & Position [cm] & \(2.24\pm 12.70\) & \(0.01\pm 5.81\) & \(0.04\pm 25.09\) \\  & Velocity [cm/s] & \(0.95\pm 0.94\) & \(-0.01\pm 0.43\) & \(0.00\pm 0.64\) \\ \hline \multirow{2}{*}{**500**} & Position [cm] & \(5.06\pm 15.82\) & \(-0.06\pm 5.80\) & \(0.69\pm 49.47\) \\  & Velocity [cm/s] & \(2.39\pm 1.22\) & \(-0.03\pm 0.45\) & \(0.01\pm 0.63\) \\ \hline \multirow{2}{*}{**1000**} & Position [cm] & \(10.53\pm 24.57\) & \(0.06\pm 6.30\) & \(0.35\pm 89.34\) \\  & Velocity [cm/s] & \(4.79\pm 1.94\) & \(-0.05\pm 0.53\) & \(0.02\pm 0.61\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Simulation results of the relative navigation by inter-satellite distances. Figure 17: Diagrams and simulation results of the line-of-sight (LOS) errors for the PAT sequences: (a) LOS errors with uncertainties of relative navigation and body pointing can be corrected using angle-of-arrival (AOA) on tracking sensor (CAM); (b) body-pointing errors and angular velocity profiles in time-series; (c) body-pointing errors on a projected plane (body coordinated x-y).
## 4 Trajectory Planning  
### Human-Robot Workspace  
Before planning the training trajectory, the workspace of the robot needs to be determined. The workspace refers to the spatial point set of the end effector, which represents the activity scope of the robots. As a virtual link (\(l_{3}\)) is assumed between the robot and the human in the kinematic analysis of Section 3, the human ankle joint (point \(D\)) is regarded as the mechanism end point in the workspace analysis. The workspace is analyzed with the numerical method, and the robot workspace is a hexahedron as shown in Figure 9. Substituting the extreme value of each joint into the forward kinematic, and workspace boundaries of the robot could be determined.  
In effect, the robot workspace could not directly be applied for the training planning due to the limited range of human joint motions. The intersection of the robot workspace and the lower limb motion space is feasible for the training planning. Besides, patients with different limb lengths and joint ROM own different motion spaces; therefore, the stable workspace is not suitable for each patient. To guarantee the patient's safety and to avoid secondary damage, a variable human-robot workspace is proposed.  
The variable human-robot workspace is the overlapping part of two spaces, and it is changed depending on different parameters of the human limb. Figure 10 shows a situation of the sagittal training,  
Figure 8: Three axis comparisons of end point motion results.  
Figure 9: The spatial workspace of the robot.  
and the variable human-robot workspace is the shadow part. The hip joint positions (\(X_{\rm F}\) and \(Z_{\rm F}\)) are set as 1790 mm and 600 mm. The lengths of the human thigh (\(l_{5}\)) and crus (\(l_{6}\)) are 396 mm and 496 mm.
###### Abstract  
The need for smart and sustainable communication systems has led to the development of mobile communication networks. In turn, the vast functionalities of the global system of mobile communication (GSM) have resulted in a growing number of subscribers. As the number of users increases, the need for efficient and effective planning of the \"limited\" frequency spectrum of the GSM is inevitable, particularly in densely-populated areas. As such, there are ongoing discussions about frequency (channel) allocation methods to resolve the challenges of channel allocation, which is a complete NP (Nondeterministic Polynomial time) problem. In this paper, we propose an algorithm for channel allocation which takes into account soft constraints (co-channel interference and adjacent channel interference). By using the Manhattan distance concept, this study shows that the formulation of the algorithm is correct and in line with results in the literature. Hence, the Manhattan distance concept may be useful in other scheduling and optimization problems. Furthermore, this unique concept makes it possible to develop a more sustainable telecommunication system with ease of connectivity among users, even when several subscribers are on a common frequency.  
graph coloring; channel allocation; GSM; cellular frequency; communication +
Footnote †: journal:  
0000-0001-4500-3223-1800][PERSON]  
1 Department of Computer Science, Faculty of Science, Adekulne Ajasin University, Akungba 234034, Nigeria 1  
[PERSON]  
2 Department of Geology, Faculty of Geography and Geoscience, University of Trier, Universitatsring 15, 54296 Trier, Germany 23 Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic 3  
[PERSON]@uhk.cz4
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
###### Abstract  
The recent 3 GPP initiative to extend IAB technology to mobile nodes in recently stated Release 18 opens up new opportunities for services operators in highway scenarios, where the extreme density of base stations (BS) is required to deliver uninterrupted coverage. The latter problem is specifically important for millimeter wave (mmWave) and future sub-terahertz (sub-THz) deployments. However, in such systems, there are inherent trade-offs between the rate provided over the multi-hop chain, the so-called \"bridge\", and the inter-site distance. One of the critical factors involved in this trade-off is the choice of the last hop. In this paper, we utilize realistic channel measurements at 300 GHz to develop a framework characterizing the above-mentioned trade-off. Then, we proceed proposing a simple technique to maximize the latter by addressing the \"last-hop problem\" and compare its performance to the set of alternative solutions. Our numerical results illustrate that bumper location is better in terms of relaying communication distance. Furthermore, the proposed last hop selection strategies allow for extreme performance gains in terms of data rate as compared to the traditional approaches reaching 100% for large ISD and 400-500% for small ISDs. In absolute numbers, the proposed relying with the last hop selection strategy allows for reducing the required BS density along the highways by 15-30% depending on the vehicle density and required level of connectivity.  
IAB; V2X; 6G sub-terahertz; relay; blockage 37M05; 60D05