esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recently, intelligent reflecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of air-ground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reflecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme based on successive convex approximation and fractional programming (FP) called the maximal signal-to-noise ratio (SNR)-FP (Max-SNR-FP) is proposed. Given its high complexity, we propose a low-complexity maximal SNR-equal amplitude reflecting (EAR) (Max-SNR-EAR) scheme based on the maximal signal-to-leakage-noise ratio method, and the criteria of phase alignment and EAR. Given that the active and passive IRS phase shift matrices of both schemes are optimized separately, to investigate the effect of jointly optimizing them to improve the achievable rate, a maximal SNR majorization-minimization (MM) (Max-SNR-MM) scheme using the MM criterion to design the IRS PSM is proposed. Simulation results show that the rates harvested by the three proposed methods were slightly lower than those of the active IRS with higher power consumption, which were 35% higher than those of no IRS and random phase IRS, while passive IRS achieved only about a 17% rate gain over the latter. Moreover, compared with the Max-SNR-FP, the proposed Max-SNR-EAR and Max-SNR-MM methods caused obvious complexity degradation at the price of slight performance loss.  
hybrid intelligent reflecting surface; unmanned aerial vehicle; directional modulation; beamforming; phase shift +
Footnote †: journal: Advances in Neural Information Processing Systems
# A Short-Term Forecast Model of foF2 Based on Elman Neural Network
## 2 Model Principle
### Architecture of the ENN  
In order to build the ENN model, the number of hidden layers and the number of neurons in each hidden layer need to be determined. First, we assumed that the number of hidden layer neurons is a fixed value and the number of hidden layers is a variable. After determining the optimal number of hidden layers, the optimal number of neurons in each hidden layer is calculated.  
However, there is still no method to accurately calculate the number of hidden layer neurons in the neural network, and only the approximate range can be calculated. The formula is as follows:  
\[N_{\text{h}}\;=\;\sqrt{N_{i}+N_{o}}\;+\;\alpha, \tag{15}\]  
where \(N_{h}\) is the number of hidden layer neurons, \(N_{i}\) is the number of input layer neurons, \(N_{o}\) is the number of output layer neurons and \(\alpha\) is a random number between [1; 10]. Too few neurons in the hidden layer will increase the model error and too many neurons will make the model training more complicated. According to formula (15), we determine that the number of the hidden layer neurons range from 6 to 12, and set the number of hidden layers to vary from 1 to 5. These models are trained with the BP algorithm and evaluated by RMSE. The result can be seen in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**FACTOR** & \multicolumn{3}{c}{**Diurnal and Seasonal Variation**} & \multicolumn{3}{c}{**Solar and Magnetic Activities**} & \multicolumn{3}{c}{**Present Value**} \\  & HRS & HRC & DNS & DNC & SSN & F10.7 & Kp & Dst & foF2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inputs of the model.  
Figure 2: Root mean square error (RMSE) of Elman neural network (ENN) model under different hidden layers.  
It can be seen from Figure 2 that the model takes the minimum value when the number of hidden layers is 3, regardless of the number of neurons in hidden layers. Thus, the number of hidden layers is set to 3.  
Then, the number of each hidden layer neurons was set to vary from 6 to 12. The number of neurons from the first hidden layer to the third hidden layer is expressed as a combination \(\{h_{1},h_{2},h_{3}\}\), \(h\in[6,12]\). Finally, 343 combinations are obtained. According to the order of \(\{6,6,6\}\), \(\{6,6,7\}\ldots\)\(\{12,12\}\), these combinations are numbered. The result is shown in Figure 3.  
As shown in Figure 3, the learning ability of the model increases and RMSE decreases with the increase of the number of hidden layer neurons. The numbers of five combinations which have the smallest RMSE are 242, 268, 294, 327 and 333. Since the increase of the number of hidden layer neurons will increase the training complexity of the IPSO, this paper uses the combination of [11; 9; 7], which is numbered 268, as the number of hidden layer neurons.
## 2 Material and Methods
### Retrieval of Sun-Induced Chlorophyll Fluorescence
A semi-automated script was developed for facilitating the extraction of sun-photometer measurements collected simultaneously with airborne imagery. Conversely, to most of the other retrieval techniques such as 3 FLD [PERSON] et al. (2016); [PERSON] et al. (2018) and iFLD [PERSON] et al. (2018), this semi-physically-based algorithm does not require reference surfaces within the image scene to constrain the atmospheric radiative transfer modelling (i.e., bare soil or other non-fluorescence pixels). On the other hand, the accuracy of such a physical approach depended on the overall quality of the atmospheric input parameters.
# A Fuzzy Path Selection Strategy for Aircraft Landing on a Carrier
## 4 Experimental Study on Landing Path Selection in Different Environments
### Results of Landing Path Selection by the Proposed Method and the Discussion  
In this section, the proposed landing path selection strategy is used to determine the optimal landing path under four different validating environments. The results are listed in Tables 8-11. For each test case, the optimal landing path and the ranking of the four alterative paths are given.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & \(\mathbf{l_{1}}\) & \(\mathbf{l_{2}}\) & \(\mathbf{l_{3}}\) & \(\mathbf{l_{4}}\) \\ \hline i & 2 & 7 & 1 & 0 \\ ii & 0 & **8** & 2 & 0 \\ iii & 0 & 1 & 4 & **5** \\ iv & 0 & 0 & 1 & **9** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of landing path selection.  
Figure 8: Experience of the LCO.  
From case i to case iv, with the changes of the environmental setups, the path selection strategy is able to determine the optimal landing path for each validation case. Note that \(l_{4}\) always ranks in the last place when the environmental condition is generally good for the aircraft landing. It demonstrates that the pilot will not land the aircraft on the ground unless the situation is too bad.  
Compared to the results obtained by the LCO's experience, the same optimal landing path is selected in case ii and case iv, while in case i, \(l_{2}\) and \(l_{1}\) are regarded as the best choice by the LCO's experience and the path selection strategy, respectively. In this case, the condition is good in general, and the two LCOs' decision is the same as the path selection strategy. However, the majority of LCOs make a conservative decision and \(l_{2}\) is their choice, and even a more conservative decision is made by an LCO and \(l_{3}\) is his/her choice. As for case iii, in bad weather, half of the LCOs tend to make the aircraft land on the ground to ensure safety, which is different from the result of the path selection strategy where \(l_{3}\) is the optimal landing path in this case. In summary of the results of landing path selection by the two methods, the decisions made by the experience of LCO are more conservative, and most LCOs choose \(l_{1}\) as the optimal landing path only when all the factors are good enough. In other situations, the experience of the LCO will also lead to a more conservative decision. Compared to the experience-based method, the proposed path selection strategy evaluates the environment through scores and is able to cope with the fuzziness of the aircraft performance, environment and traffic situation. A more reasonable decision can be made in some cases. The proposed path selection strategy can provide the pilot and the LCO with a useful tool to guide the aircraft landing safely. Further, the workload of the pilot and LCO is also reduced.
# Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
## 2 Beamforming and Deconvolution
### Deconvolved Beamforming
#### 2.2.2 R-L Algorithm  
At present, there are many algorithms for deconvolution and the R-L algorithm is one of them. The R-L algorithm is an iterative algorithm based on [PERSON] conditional probability theorem [13; 14]. The algorithm is only applicable to non-negative real numbers. The beam pattern can be used to deconvolve the beam power because the beam power and beam pattern are nonnegative.  
Figure 2: The equivalent process of CBF.  
Equation (3) can be rewritten to consider the noise, and the discrete model of CBF is given by:  
\[\mathbf{F}=\mathbf{F}_{p}\otimes\mathbf{S}+\mathbf{n} \tag{6}\]  
where \(\mathbf{S}\) denotes source power distribution, \(\mathbf{F}\) denotes beam power, \(\mathbf{F}_{p}\) denotes beam pattern (also known as the point scattering function), \(\mathbf{n}\) denotes noise, and \(\otimes\) denotes convolution operation symbol. Then, the iterative R-L algorithm [13] is given by  
\[\hat{\mathbf{S}}_{k+1}=\hat{\mathbf{S}}_{k}(\mathbf{F}_{p}*\frac{\mathbf{F}}{\mathbf{F}_{p}\otimes \hat{\mathbf{S}}_{k}})\equiv\psi(\hat{\mathbf{S}}_{k}) \tag{7}\]  
where \(\hat{\mathbf{S}}_{k}\) denotes the estimate of \(\mathbf{S}\) in the \(k\) iteration and \(*\) denotes correlation operation. \(\psi(\ldots)\) is referred to as the R-L function. Deconvolution can be realized by utilizing the recursion expression above.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 4 Results
### Results Extended to an LEO-PNT System  
To analyze the effects of the ionospheric delay on LEO satellites, we ran four simulations using the IAMM model for 50 different users at random locations, dates, and times. We remark that the IAMM model does not depend on the satellite's orbital height. However, we will observe how the frequencies of different satellite constellations influence the ionospheric delay.  
Figure 7: Map of the car path predicted by NMEA and WLS estimates.  
The first run (Figure 8a) shows the ionospheric delay for users receiving signals from GPS satellites having an \(L1\) frequency. The second through fourth runs (Figure 8b-d) show the ionospheric delay for users receiving signals from LEO satellites having C-band, X-band, and Ku-band frequencies, respectively. It is worth noting that the figure corresponding to GPS has a different scale than the LEO figures.  
The mean ionospheric delay decreased by 91.3%, 97.2%, and 98.9%, respectively, for C-band, X-band, and Ku-band frequencies compared to the \(L1\) GPS frequency. It is clear that the ionospheric delay for LEO satellites is significantly less than that of GPS satellites. This finding is based on a simple extension of the IAMM model to LEO-PNT, which was supported by the fact that IAMM is a generic and correction-independent model, which, once it is generated, remains valid indefinitely. Nevertheless, as the discussion in the next section shows, there are still several open challenges to be considered when extending a GNSS ionospheric model to LEO-PNT applications.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 3 System Model
### Local Computing Model  
The UAV in the local computing mode can harvest energy from the AP and compute its tasks simultaneously. Thus, the UAV can continue computing for the entire time block \(T\). Let \(O\) denote the number of cycles required by the processor to process one bit of task data, and its size depends on the nature of the computing task. Let \(R_{i}\) denote the computing task size (bit) of the \(i-\)th UAV, and the number of computing bits \(R_{i}^{loc}\) and \(R_{i}\) in the local computing mode are equal. Moreover, the relationship between \(R_{i}^{loc}\) and CPU computing speed \(f_{i}\) is:  
\[R_{i}^{loc}=R_{i}=\frac{f_{i}\ t_{i}}{O},\ i\in U_{0} \tag{3}\]  
Figure 2: System time allocation.  
Let \(k_{i}\) denote the calculated energy efficiency coefficient of the processor, which depends on the chip architecture of the UAV [18]. Therefore, the energy consumption model of the \(i-\)th UAV is:  
\[E_{i}^{loc}=k_{i}\,f_{i}^{3}\ t_{i},\ i\in U_{0} \tag{4}\]  
In order to ensure the sustainable operation of the UAV, the energy consumed by the UAV shall be constrained by the energy received from the AP:  
\[E_{i}^{loc}=k_{i}\,f_{i}^{3}\ t_{i}\leq\mu\ P\ c_{i}\ a\ T\leq\mu\ P\ c_{i}\ T<k_{i}\,f_{\max}^{3}\ T,\ i\in U_{0} \tag{5}\]  
Therefore, the residual energy \(E_{i}^{res}\) of the \(i-\)th UAV in a single time block can be expressed as:  
\[E_{i}^{res}=E_{i}-E_{i}^{loc}=\mu\ P\ c_{i}\ a\ T-k_{i}\,f_{i}^{3}\ t_{i},\ i\in U_{0} \tag{6}\]
## 7 Conclusions  
A method for deriving the coefficients of a proportional-integral and dual-notch controller employed for regulating the DC link voltage of single-phase converters interfacing either 50 Hz or 60 Hz mains was proposed in this work. The suggested approach allows for the optimization of the DC link voltage transient caused by step-like load changes while attaining the prescribed values of the grid-side current total harmonic distortion and the DC link voltage loop phase margin under a certain uncertainty range of mains frequency. An explicit process of controller coefficient tuning was established and successfully validated both by simulations and experimentally. It was shown that the 50 Hz and 60 Hz notch terms are nearly decoupled in frequency domain, allowing the proposed methodology to attain an approximate 5-fold increase in the DC link voltage loop crossover frequency compared to the classical PI control under a 5% grid-side current THD restriction, a 45\({}^{\circ}\) phase margin and a 1% grid frequency uncertainty. Future work on the subject will focus on distorted grid interfacing (calling for multi-dual-notch-based control structure) and examining the advantages of replacing the PI term of the controller with a type-II regulator. It is expected that the additional degree of freedom inherent in the type-II regulator may further improve the trade-off between steady-state and transient converter behavior. Moreover, multi-dual-notch-based control structures may give rise to stability issues which must be identified and appropriately resolved.  
Conceptualization, S.B. and A.K.; methodology, P.S. and A.K.; validation, S.B. and P.S.; formal analysis, A.K.; investigation, S.B. and P.S.; resources, A.K.; writing--original draft preparation, S.B.; writing--review and editing, A.K.; supervision, A.K.; project administration, A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Israel Science Foundation, grant number 2186/19, and by the Israel Innovation Authority.  
Not applicable.  
Not new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# A Programmable ROADM System for SDM/WDM Networks
## 3 Design of Programmable SDM/WDM ROADM System
### Programmable ROADM Node Structure with Bypass Connection  
The programmable ROADM node with bypass connection is shown in Figure 2. The node includes bypass configuration through core switching and WDM routing/selection based on Route-and-Select (RS) ROADM. To achieve the required configuration, all fibre cores are connected to a large-port-count fibre switch (LPFS) based on the architecture-in-demand (AoD) concept [20]. The WSSs and other components that are used for RS ROADM are also connected to the fibre switch to synthesize the RS ROADM, shown in Figure 2 separately on the top and the bottom two parts. A fixed degree ROADM can be used to replace the synthesized ROADM by connecting the ROADM as one subsystem for RS ROADM and the other subsystem for bypass connection. Figure 2 shows a 2-degree node which is a proposed node structure with four 13-core MCFs. Inside the node, the 13 cores in MCFs are regarded as 13 single mode fibres. Every core of fibre-in and fibre-out is connected to the AoD-based optical fibre Switch. The LPSF can reconfigure connections between different ports to achieve different selections of the proposed ROADM. As shown in Figure 2, the bypass connection part is on the top part of the node, which can directly connect every two cores of fibre-in and fibre-out. The other components are used to syn  
Figure 1: Concept of bypass-core configuration in an exemplary optical network with 7-core fibres. (**A**–**F**) are the node names. The color lines are the bypass links.  
thesis Route-and-Select ROADM for the residual fibre cores. Route-and-Select ROADM in the programmable node deploys 2-layer WSSs in the Figure 2. The first-layer WSSs demultiplex channels from every core of the input MCFs to select the destination cores in that node. WSSs in the second layer is used for multiplexing channels from different cores in the first-layer WSSs to the destination MCFs. The implementation of ROADM requires low port-count WSSs, which can bring the benefits of reducing hardware devices and saving the cost. Several functions are attached in the traditional Route ROADM part, it includes EDFAs, Couplers, Rx/Tx, and Functions/Subystems. Based on the AoD concept, the RS ROADM can be reconfigured in terms of input fibre cores and the corresponding add/drop configurations.
## 4 Security Issues and Solutions for CAVs
### Cyber-Attacks on Intra-Vehicle Systems
#### 4.1.3 Open Issues of Intra-Vehicle System Security
Compared with existing methods, machine-learning-based detection can extract the features of intrusion well and decrease the error rate [94]. However, due to the limited computing power, memory, and communication capacities of current vehicles, it is impractical to implement sophisticated machine learning detection on individual vehicles. CAV essentially is a large-scale distributed system composed of safety-critical individual vehicles that demand reliable real-time operations. Therefore, besides performing core intrusion detection primarily on each intra-vehicle system in distributed manner, cooperative neighboring vehicles within same cluster (e.g., a physical platoon) or cloud platform can provide further verification of the local detection results within the time constraint imposed by different applications.
## 4 Results and Discussion
### Areas for Further Research  
A much more systematic approach needs to be adopted to investigate how the FSMS interacts with other variables, such as food safety and quality culture. It is also important to see the human factor involvement in these systems and how effective training and communication could help towards a better understanding and maturation of the systems employed by food firms. A strong culture always helps firms to make decisions and act fast on recalls, or customer complaints. Models, guidelines, assessment tools and customization examples need to be developed for FSC evaluation, as well as strategies for its dissemination within companies and along the supply chain. Of course, this requires effective management commitment and effective communication across all company levels.
# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 3 Proposed Method
### Loss Functions
#### 3.3.1 Local Loss  
_Cross entropy_ (CE) is defined as follows:  
\[\text{CE}(p,\hat{p})=-(p\log(\hat{p})+(1-p)\log(1-\hat{p})) \tag{1}\]  
The predicted pixel class is the result of a sigmoid and is interpreted as a probability of belonging to a class. It is then compared with the true class (from the ground truth). Logistic regression is obtained using this loss function.  
_Weighted cross entropy_ (WCE) is defined similarly to the cross entropy but adds a coefficient for the positive examples as follows:  
\[\text{WCE}(p,\hat{p})=-(\beta p\log(\hat{p})+(1-p)\log(1-\hat{p})) \tag{2}\]  
It serves in the case of class imbalance in the training dataset. The training might not converge in these cases. The \(\beta\) param controls the expected number of false positives or negatives.  
Figure 3: A 2D slice of an augmented patch with ground truth next to it.  
Balanced cross entropy_ (BCE) is another variation that adds the coefficient for positive examples and the inverted coefficient for negative examples as follows:  
\[\text{BCE}(p,\hat{p})=-(\beta p\log(\hat{p})+(1-\beta)(1-p)\log(1-\hat{p})) \tag{3}\]  
Focal loss, introduced in [28] for dense object detection, aims to reduce the problem of huge class imbalances, in which the cross-entropy loss or any of its variations is not enough to achieve stable training. It downweights the \"easy\" examples to allow training on the rare events, thus the name of focal loss. It is also derived from the cross entropy but adds a focusing parameter \(\gamma\).  
\[\text{FL}(p,\hat{p})=-(\alpha(1-\hat{p})^{\gamma}p\log(\hat{p})+(1-\alpha)\hat {p}^{\gamma}(1-p)\log(1-\hat{p})) \tag{4}\]  
Another variation called _class balanced focal loss_ was introduced in [29].
Moreover, \(\Phi(x)\) is the cumulative normal distribution, which is calculated according to the following manner:  
\[\Phi(x)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{x}\exp\{-\frac{1}{2}t^{2}\}\,dt \tag{24}\]  
If the precision of the float ambiguities improves, the bootstrapped success rate keeps increasing. As a result, integer bootstrapping will benefit from the GNSS model being strengthened. Therefore, the integer ambiguity success rate can be compared using two cases for single and multi-GNSS. In the first case, the nominal (co)variance in the stochastic model is used. In contrast, the estimated variances and covariances of the data set in the realistic stochastic model are introduced in the second case. Compared to a realistic stochastic model, a nominal stochastic model will reduce the precision of calculated parameters and result in a lower integer ambiguity success rate for all the systems. This paper is a follow-up to [34], which demonstrates how using a more realistic stochastic model improves the IAR success rate for GPS observations. Table 8 shows the empirical success rate computed with nominal and realistic stochastic models and different receivers considering each system as well as multi-GNSS. Finally, the estimated baseline component values are used as a criterion for evaluating the effect of adopting a realistic (co)variance matrix. Moreover, the estimated unknowns' uncertainties (i.e., the baseline components deviation from zero) will be calculated by taking the square root of the diagonal components of the unknown covariance matrix \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Receiver** & & **G** & **R** & **E** & **C** & **GREC** \\ \hline Trimble NETR9 & Nominal & 98 & 92 & 95 & 100 & 97 \\  & Realistic & 100 & 92 & 99 & 100 & 100 \\ SEPT POLARX5 & Nominal & 97 & 92 & 95 & 99 & 98 \\  & Realistic & 100 & 95 & 100 & 100 & 100 \\ LEICA GR30 & Nominal & 91 & 92 & 93 & 92 & 91 \\  & Realistic & 100 & 93 & 99 & 98 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Estimated empirical success rate (SR) with Nominal and Realistic stochastic model in % (G:GPS, E:Galileo, R:GLONASS, C:BeiDou). approximated by \(Q_{\mathcal{R}}\approx(A^{T}Q_{yy}^{-1}A)^{-1}\). We estimated these values for two situations, one with a nominal and the other with a realistic stochastic model of the multi-GNSS observables. The difference between calculated baselines and real values, as well as their standard deviations, are shown in Table 9.