esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Error Metric  
To quantify the accuracy of the FT, the peak spatial-average power density (psPD) was chosen as figure of merit, which is used in all major exposure standards to demonstrate compliance for millimeter-wave frequencies [1; 2; 35]. A circular averaging area of 1 cm\({}^{2}\) was used. The psPD is defined as  
\[\text{psPD}=\max_{\mathbf{r}}\left\{\text{sPD}(\mathbf{r})\right\},\]  
where sPD(\(\mathbf{r}\)) is the spatial-average power density at any point \(\mathbf{r}\) on the evaluation surface. To evaluate sPD(\(\mathbf{r}\)), two different formulas were used: the first one represents the spatial-average power density flux crossing the surface A, which for time harmonic fields is given by  
\[\text{sPD}_{n}(\mathbf{r})=S_{n,\text{avg}}(\mathbf{r})=\frac{1}{2A}\iint_{A} \text{Re}\left\{\mathbf{E}\times\mathbf{H}^{*}\right\}\cdot\mathbf{n}dA \tag{5}\]  
and the second one is the spatial-averaged norm of the Poynting vector on surface A:  
\[\text{sPD}_{tot}(\mathbf{r})=S_{tot,\text{avg}}(\mathbf{r})=\frac{1}{2A} \iint_{A}\left|\text{Re}\left\{\mathbf{E}\times\mathbf{H}^{*}\right\}\right|dA \tag{6}\]  
As an error metric, the difference between the psPD computed using the FT algorithm and the reference was used:  
\[\text{err}_{\text{dB}}\left(\text{psPD}\right)=10\log_{10}\left(\frac{\text{ psPD}^{\text{FT}}}{\text{psPD}^{\text{ref}}}\right) \tag{7}\]  
Figure 1: Antenna configurations used for finite-difference time-domain (FDTD) simulations. (**a**) Slot array on a horn antenna designed for 10 GHz, 30 GHz and 90 GHz. A 20 dBi commercial horn (SAR-2013-28-S2) manufactured by SAGE Millimeter Inc. was used, while the array of rectangular slots was fabricated in a 0.15 mm stainless steel sheet (42.5 mm\(\times\)33.8 mm). (**b**) Cavity-fed dipole array designed for 10 GHz, 30 GHz and 60 GHz. At 30 GHz, this antenna consists of an array of nine dipoles arranged in an irregular lattice. The dipoles (0.8 mm \(\times\) 1.86 mm) are excited by non-resonant slots (1.9 mm \(\times\) 0.4 mm) that share a 0.508 mm dielectric substrate of relative permittivity \(\varepsilon_{r}=3.63\). The excitation modes are generated by a resonant cavity of size 21.18 mm \(\times\) 21.18 mm \(\times\) 5.0 mm, which is fed by a 2.92 mm (K) connector situated underneath. (**c**) Planar 3 \(\times\) 3 patch array at 60 GHz. Details for the slot array and cavity-fed dipole array can be found in [32]. Details for the planar 3 \(\times\) 3 patch were as follows: grounded substrate (\(t=0.15\) mm, \(\varepsilon_{r}=2.5\), \(\sigma=0.0038\) S/m), \(S=2.5\) mm, \(P=1.44\) mm, \(\Delta x=\Delta y=0.33\) mm (offset of the feed with respect to the center of the patch for each linear polarization).
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## Abbreviations
In Proceedings of the 9 th Latin American Conference on Communications (LATINCOM), Guatem City, Guatemala, 8-10 November 2017; pp. 1-6. * [PERSON] et al. (2010) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Novel block constructions using an intrafix for CPM with frequency domain equalization. _IEEE Trans. Wirel. Commun._**2010**, \(9\), 951-955. [CrossRef]
* [PERSON] et al. (2009) [PERSON], [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Training sequence versus cyclic prefix for CPM with frequency domain equalization. In Proceedings of the GLOBECOM IEEE Global Telecommunications Conference, Honolulu, HI, USA, 30 November-4 December 2009; pp. 1-5. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] Joint channel estimation and detection of SOQPSK using the PAM decomposition. In Proceedings of the 25 th International Conference on Telecommunications (ICT), Saint-Malo, France, 26-28 June 2018; pp. 154-158. * [PERSON] and [PERSON] (2011) [PERSON]; [PERSON] Channel estimation and equalisation for single carrier continuous phase modulation. In Proceedings of the Military Communications Conference, Baltimore, MD, USA, 7-10 November 2011; pp. 334-340. * [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON] Double turbo equalization of continuous phase modulation with frequency domain processing. _IEEE Trans. Commun._**2009**, _57_, 423-429. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Low-Complexity LMMSE-SIC turbo receiver for continuous phase modulation, based on a multiaccess-multipath analogy. _IEEE Trans. Commun._**2020**, _68_, 7672-7686. [CrossRef]
* [PERSON] and [PERSON] (2007) [PERSON]; [PERSON] Signal recovery from random measurements via orthogonal matching pursuit. _IEEE Trans. Inf. Theory_**2007**, _53_, 4655-4666. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Equalization techniques of control and non-payload communication links for unmanned aerial vehicles. _IEEE Access_**2018**, \(6\), 4485-4496. [CrossRef]  
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## 2 Structure of AFPMBLDCM  
### Topology of AFPMBLDCM  
The AFPMBLDCM has a variety of topologies. These mainly include the single-stator and single-rotor structure, double-stator and single-rotor structure, double-rotor and single-stator structure, and multi-disc structure. In addition, it also includes slotted stator and non-slotted stator structures. Because the AFPMBLDCM with a single rotor and a single stator has a greater advantage in axial size, it is favorable for flat mounts and thin drives. Furthermore, compared with the non-slotted stator AFPMBLDCM, the slotted stator AFPMBLDCM can reduce the equivalent air-gap length and is convenient for winding installation, and it can further reduce the number of permanent magnets. Thus, the AFPMBLDCM described in this paper has a slotted single-stator and single-rotor structure, as shown in Figure 1.  
Figure 2 shows the main magnetic circuit of the AFPMBLDCM, in which the leakage flux is ignored. The main flux starts from the N-pole of the arc-shaped permanent magnet, passes through the air gap, the stator core, and rotor back iron and returns to the S-pole of the adjacent permanent magnet to form a closed path.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 2 Related Works
### Active Movement-Based Routing  
In active movement-based routing mechanisms, some ferrying nodes provide communication services through active movement. In [13], a mobility-assisted approach is proposed, which utilizes a set of special mobile nodes called message ferries to communicate for nodes in a sparse network. Reference [14] presents a message ferrying (MF) scheme to exploit controlled mobility for data transmission in delay-tolerant networks. However, some prior knowledge is assumed of networks of stationary nodes and known traffic demands.
## 2 Suggested Topology and Its Operation
### Operating States
#### 2.2.2 Inverting State  
The operation during this state ensures inverting characteristics of the output voltage with respect to the input voltage. It means that output voltage will be negative and positive during the intervals in which the input voltage is positive and negative respectively. The gating signals to control the switching states of the controlled switching units are shown in Figure 6a. Here it can be viewed that their characteristics are reversed as compared with the switching signals of the non-inverting mode. Also, the operating states of the controlled switching units \(Q_{1}\), \(Q_{3}\), \(Q_{5}\) and \(Q_{2}\), \(Q_{4}\), \(Q_{6}\) are governed from gating signals \(c_{1}(c_{1}=c_{3}=c_{5})\) and \(c_{2}\) (\(c_{2}=c_{4}=c_{6}\)). The controlled switching units \(Q_{1}\) and \(Q_{2}\) are although on but their conducted current ensured zero by reverse biasing their series-connected diodes \(D_{1}\) and \(D_{2}\) as their anode are connected to the negative input voltage during its positive and negative values respectively. The closed current loops of Figure 6b,c ensure inverting outputs for any polarity of the input voltage and validate the negative voltage transfer ratio or boosting factor. This behavior of the circuit can be modeled by ignoring the internal voltage drops of the switching devices and the intermediate inductor.  
\[v_{o}(\omega t)\ =\ -\ v_{in}(\omega t)\ =\ V_{m}\sin(\omega t+\pi) \tag{6}\]  
Figure 5: Voltage non-inverting operation: (**a**) switching control scheme; (**b**) current closed-loop during positive input; (**c**) current closed-loop during negative input.
# A Load Balancing Routing Mechanism Based on SDWSN in Smart City
## 6 Conclusions  
The existence of a diversity set of paths for sensor nodes in SDWSN raises many technical issues related to data routing, where some routes become preferable for the nodes and lead to an imbalance. In order to improve the route optimization of the communication link and realize the load balancing of the network in the smart city sensor network, based on the characteristics of SDN centralized control and flexible traffic scheduling, the Elman neural network is used for the optimization calculation and the improved load balancing forwarding path is obtained. The simulation results show that the load balancing algorithm proposed in this paper can improve the average bandwidth utilization and reduce the link load jitter of the network, thus improving the performance of the entire network. It can also provide programmable data flow control method for software-defined sensor networks. The research in this paper has practical guidance and application significance for the smart city wireless infrastructure node link load balancing optimization problem.  
**Author Contributions:** For this articles, individual contributions are as follows, conceptualization, X.H. and X.C.; methodology, X.C.; software, Q.M.; resources, Y.M.; writing--original draft preparation, X.C.; writing--review and editing, X.H., Y.M. and X.C.; funding acquisition, X.H. and X.C.  
**Funding:** This research was funded by \"Ministry of Education China Mobile Research Fund Project, grant number MCM20160304\" and \"National College Student Innovation Training Project, grant number 201810433023\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication
## 4 System Model and Approach Overview
### System and Attack Models  
FeRHA operates under the assumption that all IoT devices participating in the authentication process have embedded PUFs, as well as a fuzzy extractor. The authentication process necessitates the use of a strong PUF. The latter involves the generation of a large number of CRPs to render it infeasible for an adversary to try all possible challenge bit-strings, i.e., brute force. The paper's presentation is based on the use of an arbiter-PUF, as discussed in Section 3.1. However, the proposed authentication protocol can be applied to other strong PUF designs. Due to the high noise that affects PUFs and RFF, a fuzzy extractor is utilized to mitigate the effect of such noises on the authentication tokens. In other words, fuzzy extractors are proposed to enable stable matching. Our proposed protocol leverages the non-cryptographic fuzzy extractor, as elaborated in Section 3.3 [56].  
Given the random and uncontrollable variations among the manufacturing processes, a PUF cannot be physically cloned. However, the PUF operation can be mimicked, something that is often referred to as a modeling attack. Basically, an adversary may eavesdrop on the network to intercept the prover's response to the various verifiers. The captured CRPs are then used to train a machine learning model of the PUF's behavior. Upon capturing a sufficiently large subset of CRPs, the accuracy of the model grows high enough to predict the PUF response to any challenge bit-string. Considering the major role that mutual authentication of IoT plays, an attacker would target one of the legitimate nodes to model its PUF and impersonate such a node to become part of the network. Quite a few machine learning techniques have been shown to be effective for such a purpose [60]. Nonetheless, in validating FeRHA, we assume that the adversary applies powerful techniques, namely, convolutional neural networks (CNNs) and extreme gradient boosting (XGBoost), in order to show FeRHA's resilience to modeling attacks. In addition to PUF modeling attempts, an eavesdropper may replay the intercepted authentication messages to impersonate legitimate IoT nodes. As explained in the balance of the paper, FeRHA thwarts the aforementioned impersonation threat by obfuscating the PUF response of node \(\delta_{x}\), known as the prover, and each other IoT device, which acts as a verifier, \(\delta_{y}\), while factoring in the RFF of the communicating nodes.
## 2 Materials and Methods
### Population Structure Analysis  
Approximately 26 K SNPs from these 104 _B. napus_ lines were used for phylogenetic tree construction. The phylogenetic tree was constructed with Tassel 5.0 using a neighboring algorithm [16] and displayed by the iTOL web tool ([[https://itol.embl.de/](https://itol.embl.de/)]([https://itol.embl.de/](https://itol.embl.de/)), accessed on 28 August 2021). Linkage disequilibrium between any two SNPs on one chromosome was estimated with r\({}^{2}\) using PLINK [17]. The parameters with a sliding window of 50 kb (in steps of 10 SNPs) were used, and SNPs related to other SNPs in the window with r\({}^{2}\) > 0.2 were removed. The population structure was determined by ADMIXTURE v.1.3.0 [18] and STRUCTURE 2.3.4 [19]. For ADMIXTURE five runs were performed with default parameters for each number of populations (K) set from 1 to 10, and the most likely K value was determined by the log probability of the data. For STRUCTURE, five independent runs were performed under the admixture model with K-value from 1 to 10, burn-in period of 10,000 iterations, and 100,000 Markov chain Monte Carlo. The principal component score of the samples was also computed by the principal component analysis (PCA).
###### Abstract  
On 1 April 2022, the AlfaCrux CubeSat was launched by the Falcon 9 Transporter-4 mission, the fourth SpaceX dedicated smallsat rideshare program mission, from Space Launch Complex 40 at Cape Canaveral Space Force Station in Florida into a Sun-synchronous orbit at 500 km. AlfaCrux is an amateur radio and educational mission to provide learning and scientific benefits in the context of small satellite missions. It is an opportunity for theoretical and practical learning about the technical management, systems design, communication, orbital mechanics, development, integration, and operation of small satellites. The AlfaCrux payload, a software-defined radio hardware, is responsible for two main services, which are a digital packet repearer and a store-and-forward system. In the ground segment, a cloud-computing-based command and control station has been developed, together with an open access online platform to access and visualize the main information of the AlfaCrux telemetry and user data and experiments. It also becomes an in-orbit database reference to be used for different studies concerned with, for instance, radio propagation, attitude reconstruction, data-driven calibration algorithms for satellite sensors, among others. In this context, this paper describes the AlfaCrux mission, its main subsystems, and the achievements obtained in the early orbit phase. Scientific and engineering assessments conducted with the spacecraft operations to tackle unexpected behaviors in the ground station and also to better understand the space environment are also presented and discussed.  
AlfaCrux LEOP; CubeSat; educational mission; amateur radio +
Footnote †: journal: Applied Sciences
# Qubit-Based Clock Synchronization for QKD Systems Using a Bayesian Approach
## 4 Synchronization Simulations
Thus, our algorithm can tolerate realistic clock drift rates in this example. Figure 3: Average calculated synchronization probability as a function of string length on a logarithmic scale for different received mean photon numbers. The probability of registering a dark count during one communication bin width is \(d=8\times 10^{-4}\).
# Shadow Fleets: A Growing Challenge in Global
## 4 Approaches to Detecting Dark and Gray Fleet Characteristics
### Age of Vessels  
Dark and gray fleet vessels, especially oil tankers, are frequently older, with a significant portion being over 15 years of age. The utilization of aging vessels, which may not meet the most current safety and environmental standards, heightens the risk of accidents, spills, and environmental harm.  
Older vessels are often preferred by operators of dark and gray fleets due to their lower acquisition and maintenance costs. However, these vessels may lack the latest safety features and technologies, making them more prone to mechanical failures and accidents. Additionally, their inferior environmental performance can lead to significant ecological damage, particularly in the event of an oil spill or other hazardous incident.
## 2 Materials and Methods
### Model Parameterization Versions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **M** & & **P\({}_{\text{max}}\)** & **G\({}_{\text{y}}\)** & \\  & **S** & **M** & **H\({}_{\text{max}}\)** & **Max.** & **Maximum** & **W** \\
**Parameter** & **S** & **Mortality** & **Max.** & **Photo-Synthesis** & **Yearly** & **Wood** \\  & **[1 ha\({}^{-1}\)]** & **Rate** & **Height [m]** & **[\(\mu\) molCO\({}_{2}\) /** & **Increment** & **Density** \\  & **[1 year\({}^{-1}\)]** & **Height [m]** & **[\(\mu\) molCO\({}_{2}\) /** & **of DBH** & **[t/m\({}^{3}\)]]** \\  & & & & **[m/year]** & & **[m/year]** \\ \hline \multicolumn{8}{c}{**M\({}_{6}\) with 6 PFTs**} \\ \hline PFT 1 & 30 & 0.015 & 56 & 2.0 & 0.012 & 0.55 \\ PFT 2 & 156 & 0.030 & 33 & 3.1 & 0.012 & 0.54 \\ PFT 3 & 21 & 0.029 & 33 & 6.8 & 0.019 & 0.41 \\ PFT 4 & 300 & 0.040 & 28 & 11.0 & 0.029 & 0.40 \\ PFT 5 & 2 & 0.021 & 16 & 7.0 & 0.011 & 0.52 \\ PFT 6 & 200 & 0.045 & 16 & 12.0 & 0.029 & 0.47 \\ \hline \multicolumn{8}{c}{**M\({}_{3}\) with 3 PFTs**} \\ \hline \hline \begin{tabular}{c} Shade tolerant \\ (PFT 1, 2, 5) \\ Interm. \\ \\ \end{tabular} & 34 & 0.0154 & 55.2 & 2.05 & 0.012 & 0.549 \\ \
\begin{tabular}{c} Interm. \\ \\ \end{tabular} & 21 & 0.0290 & 33.0 & 6.8 & 0.019 & 0.410 \\
\begin{tabular}{c} Shade \\ (PFT 4, 6) \\ \end{tabular} & 293 & 0.0404 & 27.1 & 11.07 & 0.029 & 0.405 \\ \hline \multicolumn{8}{c}{**M\({}_{1}\) with 1 PFT**} \\ \hline \hline \multicolumn{8}{c}{Mean PFT} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main parameter values of the FORMIND model for different parameterization versions (c.f., Table 2). For M\({}_{6}\), parameters are taken from ([PERSON] et al., 2013). Parameters for M\({}_{3}\) and M\({}_{1}\) were derived by averaging parameter values of M\({}_{6}\) (including weightings concerning fraction of aboveground biomass observed in the field, see Table 1). S represents the number of seeds [1 ha\({}^{-1}\)], M the mortality rate [1 year\({}^{-1}\)], H\({}_{\text{m}}\) the maximum attainable tree height [m], P\({}_{\text{max}}\) the maximum leaf photosynthesis rate [\(\mu\) mol CO\({}_{2}\)/(m\({}^{2}\)s)], G\({}_{\text{y}}\) the maximum yearly increment of stem diameter in breast height (DBH) [m year\({}^{-1}\)], and W the wood density [t/m\({}^{3}\)]. A full list of all model parameter values is shown in Appendix A.
## 11 PGB Load Capacity and Attitude Angle  
Predictions of the load carrying ability of the PGB for off-centered operation (\(e>0\)) follow. Figure 8 depicts the load (\(W\)) vs. eccentricity ratio (\(e/c\)) for aerostatic operation (0 rpm) and at the mean MOS, \(\Omega^{\star}\) = 25 krpm, and increasing magnitudes of air supply pressure. The results from the FE computational model (1872 elements = 72 circumferential \(\times\) 26 axial), shown with dark symbols, reveal \(W\) increases with \(p_{S}\) and is proportional to journal eccentricity (\(e\)). The graphs include light-colored lines that represent an approximate load derived from the product of the (exact) static stiffness at \(e=0\) times the journal eccentricity, i.e., \(W_{appnx}=(K_{S}\,\times\,e)\) when \(\Omega=0\) (left graph), and \(W_{appnx}\)= (\(K_{\Omega}\times\,e\)) at the MOS. Table 2 lists the magnitudes of the analytical force coefficients for the aerostatic (\(\Omega=0\)) and hybrid (\(\Omega^{\star}\)) conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Pressure** & **Aerostatic (\(\Omega=0\))** & **Hybrid** & \(\Omega=25\)**krpm** & & **Attitude Angle** & & **Critical Mass** \\ \hline \(p_{S}\) & \(K_{S}\) & \(K_{XX}\) & \(K_{XY}\) & \(K_{\Omega}\) & \(\beta\) & \(K_{\Omega}K_{S}\) & \(M_{cr}\) \\ Bar & MN/m & MN/m & MN/m & MN/m & degrees & & kg \\ \hline
2 bar & 43 & 156 & 41 & 162 & 14.6 & 3.76 & 26.2 \\
4 bar & 116 & 271 & 96 & 288 & 19.6 & 2.48 & 68.8 \\
6 bar & 184 & 354 & 155 & 387 & 23.6 & 2.10 & 107.7 \\
8 bar & 250 & 418 & 201 & 464 & 25.7 & 1.85 & 146.1 \\ \hline \multicolumn{8}{c}{\(K_{XY}=0\)} \\ \hline \end{tabular}
\end{table}
Table 2: Aerostatic and hybrid stiffnesses for PGB at the centered condition and rotor speed \(\Omega=0\) and 25 krpm. Air supply pressure \(p_{S}=2\), 4, 6, and 8 bar. Clearance \(c=0.010\) mm (\(\Lambda_{\kappa}=5.3\)).  
Note the remarkable agreement between the analytical solution and the FE numerical solution for operation with journal eccentricity (\(e\)) as large as 70% of the bearing clearance. In short, the load capacity of the cylindrical PGB is proportional to the journal displacement (\(e\)). Furthermore, for the hybrid mode operating condition, the attitude angle (\(\beta\)) between the eccentricity vector and the applied load vector remains constant. As seen in Table 2, \(\beta\) increases from \(14.6^{\circ}\) to \(25.7^{\circ}\) as the supply pressure increases from two bar to eight bar.  
The analysis and predictions demonstrate that the PGB is a linear mechanical element. Presently, based on the results shown in Figure 8, an estimation for the bearing load capacity equals \(\frac{W}{W^{\star}}=\frac{W}{(p_{S}-p_{a})L\;D}\sim\left[0.5+0.3\frac{p_{a}}{p_ {S}}\right]\left(1+0.13\Lambda_{\Omega}\right)\).
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
###### Abstract  
The objective of this study was to investigate if the inertial data collected from normal human walk can be used to reveal the underlying terrain types. For this purpose, we recorded the gait patterns of normal human walk on six different terrain types with variation in hardness and friction using body mounted inertial sensors. We collected accelerations and angular velocities of 40 healthy subjects with two smartphones embedded inertial measurement units (MPU-6500) attached at two different body locations (chest and lower back). The recorded data were segmented with stride based segmentation approach and 194 tempo-spectral features were computed for each stride. We trained two machine learning classifiers, namely random forest and support vector machine, and cross validated the results with 10-fold cross-validation strategy. The classification tasks were performed on indoor-outdoor terrains, hard-soft terrains, and a combination of binary, ternary, quaternary, and senary terrains. From the experimental results, the classification accuracies of 97% and 92% were achieved for indoor-outdoor and hard-soft terrains, respectively. The classification results for binary, ternary, quaternary, and senary class classification were 96%, 94%, 92%, 90%, and 89%, respectively. These results demonstrate that the stride data collected with the low-level signals of a single IMU can be used to train classifiers and predict terrain types with high accuracy. Moreover, the problem at hand can be solved invariant of sensor type and sensor location.  
t 2019; 9, 3099; doi:10.3390/app9153099