esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Background  
Numerous approaches have been proposed to separate the heartbeat waveform from respiration and estimate the heart rate [8, 9]. [PERSON] et al. used arctangent demodulation to process the quadrature signals and recover the body movement, reducing the influence of respiration on heartbeat [10]. Arctangent demodulation regards a human subject as a single scatter point, which is not appropriate and can lead to variable results. [PERSON] et al. recovered the heartbeat signal by direct high-pass filtering of the sampled radar signal, with a passband frequency of 0.85 Hz, and estimated the heart rate by fast Fourier transform (FFT) [11]. It is also a feasible solution, but the human Doppler signal collected by the radar has rich harmonics, which results in the mixing of the respiratory harmonics and the heartbeat component in the frequency domain, especially for the mm-wave radars with high carrier frequencies [12]. It invalids the common band-pass filters with a cut-off frequency of about 1 Hz [13], thus seriously hindering the extraction of the heartbeat and the estimation of the heartbeat rate [14,15].  
In recent years, [PERSON] et al. proposed using high-order heartbeat harmonics for heart rate estimation, which are at high frequencies and almost unaffected by respiration [16]. Although the strongest fundamental frequency is completely abandoned in this way, its effective suppression of the respiratory interference is still beneficial for the heart rate estimation. A similar idea was adopted by [PERSON] et al. for wall vital signal monitoring [17]. However, how to estimate the heartbeat rate from the numerous and weak high-order harmonics is still a problem. Finding a single second- or third-order harmonic in the spectrum is obviously not enough to make full use of the signal; so the performance is poor in the case of a low signal-to-noise ratio [18]. On the other hand, a period length estimation method of an unknown waveform signal in the time domain based on the maximum likelihood theory originally designed for pitch period estimation by [PERSON] et al. in [19] was introduced in radar-based monitoring by [PERSON] et al. [20]. The estimator makes no assumption about the signal waveform in the time domain, which is suitable for the heartbeat signal [21].
## 4 Selecting the Straight Line Segments
### Decrease Time Complexity  
In a 2-D plane, \(n\) points can form \(C(n\),2) straight line segments. The time complexity is \(O(n^{2})\) if we compare a line segment length with others one by one to select the longest one. The amount of computation will increase sharply when the value of \(n\) is very large. Therefore, we must design an algorithm to cut down the computing time and find the longest line segment rapidly. The convex hull theory is employed to solve this problem.  
If we assume that \(CH\) is a convex hull of a given points set \(Q\), \(CH\) is defined as the unique minimal convex set containing \(Q\). It implies all of the points in set \(Q\) must be in or on the boundary of convex hull \(CH\). Therefore, If there are \(n\) points in the \(Q\), then we can get \(C(n\),2) straight line segments which consist of all the points. The two endpoints of the longest one must be on \(CH\).  
Convex hull of points set \(Q\) can be obtained by Graham scan. The time complexity of the method is \(O(nlgn)\). We assume that there are \(n\) points in the set \(Q\), and \(m\) points among \(n\) belong to the convex hull of set \(Q\). Thus we can find the longest straight line segment from the \(C(m\),2) straight line segments instead of \(C(n\),2) ones. In general, \(m\ll n\) when \(n\) is a large number. It means we can decrease the computational complexity of seeking the longest straight line segment by using the convex hull.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 3 Cycle Slip Detection Strategy for a UAV
### Time-Differenced Dual-Frequency Carrier Phase Combination (TDDFC)  
First, TDDFC values are calculated as follows [31]:  
\[\begin{split} T_{TDDFC}&=\delta\phi_{i,f_{1}}^{k}- \delta\phi_{i,f_{2}}^{k}\\ &=-\delta I_{f_{1}}+\delta I_{f_{2}}+\lambda_{f_{1}}\delta N_{i,f_ {1}}^{k}-\lambda_{f_{2}}\delta N_{i,f_{2}}^{k}+\delta M_{f_{1}}+\delta M_{f_{2 }}+\varepsilon_{\delta\phi_{f_{1}}}+\varepsilon_{\delta\phi_{f_{2}}}\end{split} \tag{7}\]  
where the multipath-induced range errors in each measurement are denoted as \(M_{f}\). \(\delta(\cdot)\) is a differencing operator between successive epochs. Assuming that ionospheric delay can be ignored owing to ionospheric inactivity, Equation (7) can be expressed as follows if the integer ambiguities are the same in consecutive epochs.  
\[\begin{split} T_{TDDFC}&=\delta\phi_{i,f_{1}}^{k}- \delta\phi_{i,f_{2}}^{k}\\ &=\varepsilon_{TDDFC,comb}.\end{split} \tag{8}\]  
Figure 4 shows the TDDFC values from the flight data within the threshold value at which no cycle slips occur. The threshold for \(T_{TDDFC}\) is formulated from our flight test data at a false alarm level of \(10^{-5}\) using a third-order polynomial equation.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **GPS** & **Galileo** & **BeiDou** \\ \hline \multirow{2}{*}{\(f_{1}\)} & Band & L1 & E1 & B1 \\  & Wavelength [m] & 0.19029 & 0.19029 & 0.19204 \\ \multirow{2}{*}{\(f_{2}\)} & Band & L2 & E5b & B2b \\  & Wavelength [m] & 0.24421 & 0.24835 & 0.24835 \\ \hline \end{tabular}
\end{table}
Table 1: Dual-frequency GNSS signals (\(f_{1,f_{2}}\)) used in this paper according to each satellite constellation and frequency.  
Although the TDDFC thresholds are sufficiently low to detect small cycle slips, there are some particular dual-frequency cycle slip combinations that result in low values of TDDFC such that no cycle slips can be observed. Some examples of these particular combinations and the corresponding TDDFC values are listed in Table 2.  
As an example, Figure 5 shows the missed detection probabilities of the TDDFC detector for 39 and 30 cycle slips in \(f_{1}\) and \(f_{2}\) bands, respectively. As shown in Figure 5, all of the TDDFC values of GAL and BDS were smaller than the threshold, indicating that certain cycle slip combinations would be difficult to detect in practice using TDDFC alone. To address these challenges, the DACSD was used in parallel with the TDDFC.
# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
###### Abstract  
Mini-EUSO is a wide-angle fluorescence telescope that registers ultraviolet (UV) radiation in the nocturnal atmosphere of Earth from the International Space Station. Meteors are among multiple phenomena that manifest themselves not only in the visible range but also in the UV. We present two simple artificial neural networks that allow for recognizing meteor signals in the Mini-EUSO data with high accuracy in terms of a binary classification problem. We expect that similar architectures can be effectively used for signal recognition in other fluorescence telescopes, regardless of the nature of the signal. Due to their simplicity, the networks can be implemented in onboard electronics of future orbital or balloon experiments.  
machine learning; neural network; pattern recognition; meteor; fluorescence telescope; orbital experiment; UV illumination; atmosphere
## 5 Conclusions  
The millimeter wave band in 5G greatly reduces the time for each communication symbol. Hence, utilizing a small number of symbols in one frame for sensing can greatly alleviate the problem of low efficiency. In this paper, we design a TD-ISAC sensing waveform based on a 5G communication base station with its millimeter wave band. We design two dual-PRF sensing waveforms subject to uplink-flexible and uplink-fixed mode. More precisely, we allocate only one OFDM symbol for sensing in one time slot at most to deduce the impact on communication efficiency. And the designed waveforms occupy only 53 OFDM symbols, whereby it has 1120 OFDM symbols total in one communication frame. In fact, it usually has some reserved symbols in a communication frame which can be utilized for sensing. The sensing performance of the designed waveforms with the DFT-based method is a little poorer compared with that of the uniform sensing waveform. But we know that it cannot realize uniform sensing in 5G communication. The designed sensing waveform can also realize vehicle detection and estimation by applying the DFT-based method in real traffic scenarios. In addition, it can improve estimation performance by applying a sparse recovery method, e.g., the IAA and OMP method, at the expense of computational complexity. The designed dual-PRF waveform can work well under a general communication status. Our future research work will focus on designing sensing waveforms under burst communication services in order to be adaptive to any communication status.  
Data curation, J.Z.; Writing--original draft, P.C.; Writing--review & editing, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Guangdong Basic and Applied Basic Research Foundation under grant 2022A1515140014, in part by the Science and Technology Project of Shenzhen under grant JCYJ20190808142803565, and in part by the National Natural Science Foundation of China under grant 62101207.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 5 Adaptive Load Vectors
### Adaptive Upper-Bound (AUB) Pushover Analysis
Perform a nonlinear static analysis: Conduct a standard nonlinear static analysis using the lateral load pattern \(Sn=m\Phi n\). From this analysis, determine the base shear-roof displacement (\(Sn-m\Phi U_{n}\)) or pushover curve of the structure for the \(n\)-th mode. (See Figure 7a.) 5. Idealize the pushover curve as bilinear: Draw a straight line ('Line 1' in Figure 7b) through the origin to match the elastic segment of the pushover curve. Draw a flat line ('Line 2' in Figure 7b) through the pushover curve at the maximum base-shear capacity, \(V_{bn,\max}\). The ultimate roof displacement, \(u_{rm}\), is assumed as a point of the pushover curve where a loss of 20% in base-shear capacity is observed (see Figure 7b). 6. Determine the force-deformation relationship: calculate the force-deformation relationship (see Figure 7d) of the 'nth-mode' inelastic SDOF system based on the bilinear idealized pushover curve using the following relationships: \[\frac{F_{sn}}{L_{n}}=\frac{V_{bn}}{M_{n}},\] (60) \[D_{n}=\frac{u_{bn}}{T_{n}\Phi_{nr}},\] (61) \[L_{n}=\frac{m_{n}}{\Gamma_{n}},\] (62)
7. Construct the inelastic SDOF system: Create the 'nth-mode' inelastic SDOF system (see Figure 7c) with unit mass and a force-deformation relationship defined in Step 2. Assign a damping ratio equal to the value for the nth mode of the original MDOF structure (\(\zeta n\)). 8. Compute the peak deformation of the SDOF model:
9. Calculate the peak deformation of the SDOF model, \(D_{n}\), using Equation (62), where \(D_{i}\) is the absolute peak deformation for the ith ground motion record. 10. Calculate the target displacement for nth mode: determine the target displacement of the structure at the roof level for the nth mode, \(u_{mt}\), using Equation (63): \[u_{rtnt}=\Phi_{nr}\Gamma_{n}\tilde{D}_{n}\] (63)
11. Perform adaptive multi-stage pushover analyses (the MADP procedure): Apply gravity loads and perform adaptive displacement-control pushover analyses. This involves multiple sub-steps, including calculating new lateral load patterns based on the formation of plastic hinges and updating the target displacement values (see Steps 10.1-10.5.4) [64]. 12. Repeat for the essential modes: Repeat Steps 3-11 for as many modes as deemed necessary. Typically the first three modes of vibration are adequate for most common buildings. 13. Determine the final structural responses: combine the modal responses obtained from Step 12 using an appropriate modal combination rule, such as square root of the sum of squares (SRSS) or complete quadratic combination (CQC), as guided by references [37].
## 2 Signal Model and Problem Statement
### Anti-Jamming Effect on the Carrier Phase  
Substituting Equations (1) and (3) into Equation (4) yields  
\[\begin{split} y_{\rm{PI}}(t)&=\frac{\mathbf{b}^{ \rm{H}}\mathbf{R}_{xx}^{-1}\mathbf{a}_{0}}{\mathbf{b}^{\rm{H}}\mathbf{R}_{xx}^ {-1}\mathbf{b}}\mathbf{s}(t)+\mathbf{w}_{\rm{PI}}^{\rm{H}}\Bigg{[}\sum_{q=1}^{Q }\mathbf{a}_{q}j_{q}(t)+\mathbf{n}(t)\Bigg{]}\\ &=\alpha\mathrm{e}^{\mathrm{j}\phi}s(t)+n(t)\end{split} \tag{5}\]  
where \(\alpha\) and \(\psi\) denote the scaling factor and the phase bias induced by the adaptive array, respectively. \(n(t)\) represents the noise term after interference is effectively suppressed. Note that \(\psi\) depends on the autocorrelation matrix \(\mathbf{R}_{xx}\). Therefore, the phase bias varies when the operating environment changes.  
The in-phase (I) and quadrature (Q) local carrier replicas are, respectively, generated by the numerically controlled oscillator (NCO) in the receiver as  
\[\begin{split} i_{\rm{carrier}}(t)&=\cos(2\pi(f_{\rm{ IF}}+\hat{f}_{\rm{d}})t+\hat{\phi})\\ q_{\rm{carrier}}(t)&=-\sin(2\pi(f_{\rm{IF}}+\hat{f}_ {\rm{d}})t+\hat{\phi})\end{split} \tag{6}\]  
where \(\hat{f}_{\rm{d}}\) is the local estimated Doppler frequency, and \(\hat{\phi}\) is the local estimated initial carrier phase.  
The focus of this work is on carrier tracking, so it is assumed that the local code replica is synchronized with the received code. The array output in Equation (5) is correlated with the local signal replicas, and the I and Q correlation signals are obtained as  
\[\begin{split} I&=A\cdot D\cdot\mathrm{sinc}(\delta f \cdot T)\cdot\cos(\pi\delta f\cdot T+\delta\varphi+\psi)+n_{\rm{I}}\\ Q&=A\cdot D\cdot\mathrm{sinc}(\delta f\cdot T) \cdot\sin(\pi\delta f\cdot T+\delta\varphi+\psi)+n_{\rm{Q}}\end{split} \tag{7}\]  
where \(A\) is the correlation amplitude depending on the useful signal amplitude, \(D\) is the navigation data bit that is assumed to be constant over the integration time, \(\mathrm{sinc}(x)=\sin(\pi x)/\pi x\), \(T\) is the coherent integration time, \(\delta f=f_{\rm{d}}-\hat{f}_{\rm{d}}\) is the Doppler frequency error, \(\delta\varphi=\varphi-\hat{\varphi}\) is the initial phase error, and \(n_{\rm{I}}\) and \(n_{\rm{Q}}\) are correlation noise terms.  
The two-quadrant arctangent phase discriminator can be used to obtain the phase error, and the discriminator output is given by  
\[\delta\tilde{\varphi}=\arctan(Q/I)=(\pi\delta f\cdot T+\delta\varphi)+\psi+\xi \tag{8}\]  
where \(\xi\) denotes the discriminator noise, and the term enclosed in parentheses is the true phase error.  
It can be seen from Equation (8) that the phase bias is induced into the discriminator output and this can be viewed as the phase step input [17]. The steady-state error of the phase-locked loop is zero under this circumstance [17], i.e., \(\hat{\varphi}=\varphi+\psi\) at steady state. This indicates that the phase bias is eventually introduced into the GNSS carrier phase measurement, resulting in a significant phase discontinuity.
# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 3 Overview of Precision Agriculture
### Devices and Platforms  
This layer of the proposed system model comprises the sensor platform and associated sensing technologies. Sensors capture and digitize the measurement parameters of physical objects and their environments, such as plants, animals, soil, and water. Precision applications in agriculture require the identification of specific measurement parameters and corresponding sensors. The overall characteristics of the IoT ecosystem, including hardware, operating systems, and network stack protocols, also play a role in determining the sensor computing platforms.  
The identification of measurement parameter requirements is influenced by three factors: the type of animal or plant being considered (e.g., horse, dog, rice, palm, or olive tree), the farm environment where the species are studied (such as large arable fields, small/medium-sized arable farms, animals in the field or house, indoor horticulture, greenhouses, beehives, fishery and aquaculture, and leisure agriculture [55]), and the specific applications being used (such as monitoring livestock food supply chain, tracking, traceability, early warning systems, and pest management). Figure 2 illustrates these factors.  
Table 1 summarizes commonly used plant sensory data in precision agriculture. The information is adapted from the primary vendor, Libelum, in the IoT domain. This list is useful for IoT system developers to optimize their applications and make informed decisions about sensor selection. The data type of the sensor is also provided to aid in programming.  
Sensor device platforms, as shown in Table 2, are built from hardware and software components. The functional element of the software is responsible for managing the underlying hardware platform and providing a set of services for upper-layer applications. Operating systems (OS) play a crucial role in the functionality of the sensor device platform. The OS kernel, which is the core component of the OS, includes a scheduler that manages resource sharing among users, such as CPU, main memory, and I/O device operations. Additionally, the programming model of the OS, such as threading, supports concurrency and enables efficient resource management [56].  
Figure 2: Process of identifying the measurement parameters requirements.
## 3 Test Results
### Wave Forces According to Wave Heights of Regular Waves
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sea Levels**} & \multirow{2}{*}{**Wave**} & \(\mathbf{H_{w}}\) & \multirow{2}{*}{**Monopile**} & \multirow{2}{*}{**GBS**} & \multirow{2}{*}{**Hybrid 0\({}^{\circ}\)**} & \multirow{2}{*}{**Hybrid 45\({}^{\circ}\)**} \\ \cline{3-3} \cline{5-7}  & & **m (Ratio)** & & & & \\ \hline \multirow{4}{*}{HSWL} & R-\#3 & 3435 (1.00) & 783 (1.00) & 2377 (1.00) & 1110 (1.00) & 1047 (1.00) \\  & R-\#4 & 6870 (2.00) & 1451 (1.85) & 4877 (2.05) & 2238 (2.02) & 2180 (2.08) \\  & R-\#5 & 12,780 (3.72) & 2610 (3.33) & 7774 (3.27) & 3752 (3.38) & 3633 (3.47) \\ \hline \multirow{4}{*}{MSL} & R-\#3 & 3435 (1.00) & 627 (1.00) & 2748 (1.00) & 1198 (1.00) & 1191 (1.00) \\  & R-\#4 & 6870 (2.00) & - & - & - & - \\  & R-\#5 & 12,780 (3.72) & 2375 (3.79) & 8541 (3.11) & 4070 (3.40) & 4087 (3.43) \\ \hline \multirow{4}{*}{LSWL} & R-\#3 & 3435 (1.00) & 619 (1.00) & 2906 (1.00) & 1287 (1.00) & 1282 (1.00) \\  & R-\#4 & 6870 (2.00) & 1332 (2.15) & 6435 (2.21) & 2631 (2.04) & 2618 (2.04) \\ \cline{1-1}  & R-\#5 & 9500 (2.77) & 2391 (3.86) & 9412 (3.24) & 4640 (3.61) & 4537 (3.54) \\ \hline \hline \end{tabular} (ratio) = (wave force of R-#N)/(wave force of R-#3), N = 3, 4, 5.  
\end{table} 
Table 4: Summary of maximum wave forces for real-scale models: \(\mathrm{H_{w}}\) under regular waves. Figure 13 plots the wave heights measured under application of wave R-#3 and wave R-#5 with the support structures. The wave heights were measured at three spots: two spots (H1, H2) located 26 m in front of the models and one spot (H3) located 2 m behind of the models with respect to the wave direction, as presented in Figure 4. Wave nonlinearity or shoaling can be clearly identified in wave R-#5 at LSWL [4; 5; 14]. Namely, the wave crest is seen to increase sharply and the wave trough to flatten. This wave nonlinearity at shallow water depth is thus likely to influence the wave force acting on the support structures for wave R-#5 at LSWL. As indicated in Table 5, the wave conditions at LSWL of this study correspond to those of a transitional zone. When the waves enter the transitional zone, shoaling starts and makes the waves become higher and steeper to finally undergo breaking. Figure 14 provides the relationship between the wave conditions of this study and the wave breaking criteria (diagonal line) at the transitional zone. It appears that wave R-#5 of this study (circle area), which corresponds to the high wave height case, is close to the wave breaking criteria. This makes it possible to confirm the occurrence of wave shoaling for wave R-#5 and its influence on the wave force, especially in shallow water at LSWL, as presented in Figure 14.  
Figure 12: Extreme wave force to wave height and diameter of structure [3]. Figure 11: Wave force patterns to \(\mathrm{H_{w}}\) under regular waves: (a) monopile; (b) GBS; (c) hybrid.
## 3 Results
### Comparison between the Protocols for Physiological Responses and Subjective Perception  
Significant differences were found for the participants' HR and power between the 2 protocols. Specifically, higher HR and power were found during the THR protocol (Table 1). Participants' lactate concentration was higher than the onset of blood lactate accumulation (OBLA) point of 4 mmol/L in both protocols (THR 6.2 \(\pm\)2.1; BORG \(5.7\pm 2.2\) mmol/L), with no difference between the protocols. In addition, no differences were found in the participants' RPE, degree of enjoyment and fatigue between the two protocols.  
HR, RPE, and power were measured every 5 min during the exercise and are presented in Figure 1. The analysis of a two-way ANOVA (2 protocols THR/BORG) and time (four time points: 5, 10, 15, 20 min) demonstrated a main effect for time (F (3) = 21.4, \(p\) > 0.001), for the protocol (F (1) = 11.19 \(p\) > 0.002), and for an inter  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **BORG Protocol** & **THR Protocol** & \multirow{2}{*}{_p_**-Value**} \\  & **Mean \(\pm\) SD** & **Mean (SD)** & \\ \hline
**HR** & & & \\
**(bpm)** & 122.6 \(\pm\) 19.5 & 132.9 \(\pm\) 9.8 & **0.002** \\ \hline
**RPE** & & & \\
**(0–10 scale)** & 7.00 \(\pm\) 0.1 & 6.6 \(\pm\) 1.5 & 0.39 \\ \hline
**Power** & & & \\
**(watt)** & 48.6 \(\pm\) 15.4 & 52.6 \(\pm\) 17.9 & **0.03** \\ \hline
**\(\Delta\) Lactate** & & & \\
**(mmol)** & 2.7 \(\pm\) 0.42 & 3.3 \(\pm\) 0.39 & 0.33 \\ \hline
**Exercise enjoyment (1–10 scale)** & 6.1 \(\pm\) 2.0 & 5.8 \(\pm\) 2.3 & 0.48 \\ \hline
**Fatigue (1–10)** & 4.9 \(\pm\) 2.1 & 5.8 \(\pm\) 2.5 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exercise variables and statistical differences between THR and BORG protocols.  
X time) (F (3) = 11.194.83 \(p>\) 0.004). Participants' heart rate in the THR protocol was higher compared to the BORG protocol. For the RPE variable, no significant main effects were noticed. Lastly, the power variable demonstrated a significant main effect for time (F (3) = 2.97, \(p>\) 0.049) and the protocol (F (1) = 5.24 \(p>\) 0.03) with no interaction. The THR was conducted at a power of 5-10 watts more compared to the BORG protocol.