esa-sceva/satcom-chunk-collection huggingface dataset:
# Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
## 5 Discussion
### Expressivity  
Concerning the second aspect limiting the photonic processor performance, process variations should be considered that limit the PIC ability to implement any matrix in the SU(\(N\)) space, i.e., its expressivity. Fabrication inaccuracies primarily impact 3 dB couplers, causing an unbalance from the nominal 50/50 splitting ratio. The deviation from this nominal value decreases the MZI ER. Figure 10a reports a 3D plot of the ER as a function of the splitting ratio of the two 3 dB couplers, within a 10% deviation from the ideal 0.5 splitting. The reported value is the minimum ER between the MZI bar and cross ports. The ER quickly decreases when deviating from the ideal splitting ratio, and reaches 25 dB when the two couplers have a splitting ratio of 0.47.  
As discussed in Section 3, an MZI should ideally implement any SU(2) matrix, which can be mapped on the surface of a Poincare sphere [40]. A finite ER prevents the single element from implementing any SU(2) matrix, thus reducing its expressivity, calculated as the fraction of reachable surface on the Poincare sphere. To quantify this behaviour,  
Figure 10: (**a**) MZI extinction ratio as a function of coupler splitting ratio. (**b**) Expressivity, i.e, coverage of any SU(2) by an MZI, as a function of the extinction ratio.  
Figure 9: ENOB reduction as a function of processor depth and single element loss in SNR-limited analog processors neglecting optical I/O losses.  
Figure 10b shows the MZI expressivity as a function of the ER. From this standpoint, the silicon-on-insulator chip outperforms the silicon nitride chip in terms of worst case MZI expressivity: since the worst ER is 22.9 dB, the former guarantees an MZI expressivity \(>97\%\), while the latter, characterized by a modest ER of 7.5 dB in the worst case, reaches an MZI expressivity near 75%.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
###### Abstract  
In the H2020 project \"Satellite-borne and INsitu Observations to Predict The Initiation of Convection for ATM\" (SINOPTICA), an air traffic controller support system was extended to organize approaching traffic even under severe weather conditions. During project runtime, traffic days with extreme weather events in the Po Valley were analyzed, an arrival manager was extended with a module for 4D diversion trajectory calculation, two display variants for severe weather conditions in an air traffic controller primary display were developed, and the airport Milano Malpensa was modelled for an air traffic simulation. On the meteorological side, three new forecasting techniques were developed to better nowcast weather events affecting tactical air traffic operations and used to automatically organize arrival traffic. Additionally, short-range weather forecasts with high spatial resolution were elaborated using radar-based nowcasting and a numerical weather prediction model with data assimilation. This nowcast information was integrated into the extended arrival manager for the sequencing and guiding of approaching aircraft even in adverse weather situations. The combination of fast and reliable weather nowcasts with a guidance support system enables severe weather diversion coordination in combination with a visualization of its dynamics on traffic situation displays.  
air traffic management and airports; air traffic control; arrival manager; controller support system; severe weather visualization; nowcasting; weather research and forecasting  
24 January 2023  
21 February 2023  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 2 Related Works  
Before delving into the construction of the offshore vessel trajectory prediction model based on PSO-CNN-RGRU-Attention, it is necessary to systematically review the basic theoretical knowledge underlying this model. In this section, the core principles of the Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Attention Module will be expounded upon in sequence.
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 4 Examples
### Synthetic 2D Seismic Data Example  
For a comparative study of the LTFS with HT and GHT in seismic edge detection, one set of synthetic 2D seismic data (as shown in Figure 3a) with three horizontal reflections and one gradient reflection (the gradient is 1 millisecond per trace) is generated by a 20 Hz dominant frequency zero-phase Ricker wavelet. The time-sample interval is one millisecond, thus the Nyquist frequency is 500 Hz and the reflection coefficients of the four reflections are 1.0, \(-1.0\), 1.0, and \(-1.0\), respectively.  
Figure 2: Instantaneous amplitudes of the synthetic 1D channel data in Figure 1a, which are obtained by LTFS with: (a) \(p=\)1.0,\(f_{1}=0\) Hz and \(f_{2}=f_{N}=500\) Hz; (b) \(p=0.8,f_{1}=0\) Hz and \(f_{2}=f_{N}=500\) Hz; (c) \(p=0.6,f_{1}=0\) Hz and \(f_{2}=f_{N}=500\) Hz.  
Figure 3b shows the instantaneous amplitudes obtained by HT; Figure 3c shows the instantaneous amplitudes obtained by GHT, with \(p=0.4\) (ratio of an even number 2 and an odd number 5). Figure 3d shows the instantaneous amplitudes obtained by LTFS, with \(p=0.4\), \(f_{1}=0\) Hz, and \(f_{2}=f_{N}=500\) Hz. Figure 3e shows the instantaneous amplitudes obtained by GHT with \(p=0.2\) (ratio of two odd numbers 1 and 5), which is the same as the LTFS result with \(p=0.2\), \(f_{1}=0\) Hz, and \(f_{2}=f_{N}=500\) Hz. Figure 3f shows the instantaneous amplitudes obtained by LTFS, with \(p=0.2\), \(f_{1}=10\) Hz, and \(f_{2}=100\) Hz.  
For more clear comparison, we extracted the corresponding results in the yellow rectangles in Figure 3. Figure 4a-f shows the zoomed-in views of yellow rectangles in Figure 3a-f, respectively; and we can see that Figure 4a-f clearly detected the edges between the second horizontal layer and the gradient layer when trace numbers are greater than 40, 42, 34, 24, 21, and 21, respectively. In contrast, the GHT result in Figure 4c shows strong artifacts (or instabilities) due to the sign changing during GHT. A comparison of Figure 4b-f indicates that Figure 4e,f obtains higher-resolution results than Figure 4b-d and Figure 4e,f has no obvious differences.  
Table 1 shows the time costs of GHT and LTFS results in Figure 3e,f. Figure 4f has higher computation efficiency than Figure 4e due to the chosen frequency integration parameters \(f_{1}=10\) Hz and \(f_{2}=100\) Hz.  
Above all, LTFS have higher resolution and computation efficiency than GHT, and finally obtain high-efficiency and high-resolution results.
# Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences
## 4 Numerical Example Analysis
### Resource Allocation Quantity
In Table 7, it can be seen that in the stochastic programming model using the DPS, when the required number of resources can meet the impact of uncertainty in flight arrival and departure, the number of ferry configurations is not less than 13. When the number of ferries decreases by 1 unit, 99.43% of the samples can still obtain the optimal scheduling plan. From a statistical perspective, when the probability of an event occurring is less than 0.01, it can be defined as a low-probability event. Therefore, when the number of ferries is 12 while ensuring the feasibility of the plan, it effectively reduces resource allocation and operating costs, which is more in line with economic benefits. Similarly, when managers have lower requirements for the ability to respond to uncertainty in the plan, the amount of resources can be appropriately reduced.
## 4 Results and Discussion
### Improvement Obtained When the Random Error Component That Affects Radar Estimates Is Smoothed  
The presence of the random error component that affects radar estimates can be pointed out through a simple low-pass (smoothing) filtering. Even a (\"short-window\") three-day running median is able to significantly reduce daily radar fluctuations, and this results in an improvement in the explained variance and, most of all, in the scatter in dB (see Table 3).  
Figure 3: Daily variability of the Sun’s emission retrieved from the MeteoSwiss weather radars after having applied a three-day running median to the radar data. The reference measurements acquired by DRAO are also plotted in blue.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Albis Radar** & **Lema Radar** & **Dole Radar** \\ \hline Explained variance in \% & 88\% & 80\% & 66\% \\ Scatter in dB & 0.05 dB & 0.07 dB & 0.07 dB \\ Standard deviation in dB & 0.29 dB & 0.41 dB & 0.43 dB \\ \hline \end{tabular}
\end{table}
Table 3: Agreement between the DRAO reference (20 UTC) and the Swiss radar horizontal channel after having applied a three-day running median filter.  
From a visual comparison, a fair agreement can be observed between the DRAO reference value and the values retrieved from radar observations after the three-day median filter. Figure 3 shows the corresponding results: the solar rotation, which can be observed in the radio frequency flux during active periods, can clearly be seen in the signatures of all three radar systems.
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Cell-Free or Distributed Networks  
Cell-free or distributed networks refer to advanced network architectures where multiple distributed access points or base stations work together to provide seamless communication and sensing capabilities. In cell-free networks, there are no traditional cell boundaries. Instead, a large number of distributed access points jointly serve the users, providing uniform service quality across the coverage area. Typically, the access points are spatially distributed and work collaboratively to serve users and perform JPSAC tasks. Some of the unsolved research questions in this area are how to manage the interference between multiple distributed access points, how to efficiently allocate the resources (e.g., power, bandwidth, beams) among the distributed access points for maximum performance, and how to ensure precise time synchronization among distributed access points, when coherent signal processing is desired. The security in cell-free systems with ISACfunctionalities has been recently addressed in [54]. The transmitter beamforming design for a cell-free mMIMO system with ISAC has been recently presented in [55]. A review of cell-free mMIMO concepts can be found in [56].
# Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
## 4 Solving with Sequential Convex Programming
### Successive Linearization for Dynamic Equations  
The dynamics in Equations (10) and (11) are highly nonlinear. In order to convert _Problem 1_ into a convex subproblem, the dynamics should be linearized. Firstly, Equations (10) and (11) are rewritten into a concise form as:  
\[\dot{\mathbf{x}}= f_{0}(\mathbf{x},e)+B(\mathbf{x},e)\cdot u \tag{14}\]  
where \(\dot{\mathbf{x}}\) is the derivative of \(\mathbf{x}=\left[r,\gamma\right]^{T}\) with respects to \(e\), the column vector  
\[f_{0}(\mathbf{x},e)=\begin{bmatrix}\sin\gamma/D\\ \cos\gamma/\left(rD\right)-\cos\gamma/\left(r^{2}V^{2}D\right)\end{bmatrix} \tag{15}\]  
and the coefficient vector of the control variable  
\[\mathbf{B}(\mathbf{x},e)=\begin{bmatrix}0,&(L/D)/V^{2}\end{bmatrix}^{T} \tag{16}\]  
For simplicity, the argument \(e\) is dropped in the remainder of this paper. The nonlinear dynamics in Equation (14) can be approximated by a successive small-disturbance-based linearization method [40]. Given an existing solution \(\mathbf{x}^{z}\) from the \(z\)-th iteration of the successive approximation process, the dynamic Equation (14) is linearized at \(\mathbf{x}^{z}\) in the (\(z\) + 1)-th iteration as  
\[\dot{\mathbf{x}}=\mathbf{A}(\mathbf{x}^{z})\mathbf{x}+\mathbf{B}(\mathbf{x}^{z})\cdot u+f_{0}(\mathbf{x}^{ z})-\mathbf{A}(\mathbf{x}^{z})\mathbf{x}^{z} \tag{17}\]  
where the coefficient matrix of the state vector  
\[\mathbf{A}(\mathbf{x}^{z})=\left.\frac{\partial f_{0}(\mathbf{x})}{\partial\mathbf{x}}\right|_ {\mathbf{x}}=\mathbf{x}^{z}=\begin{bmatrix}a_{11}&a_{12}\\ a_{21}&a_{22}\end{bmatrix} \tag{18}\]  
in which  
\[\begin{array}{l}a_{11}=-\frac{D_{r}^{2}\sin\gamma^{2}}{(D^{2})^{2}},\\ a_{12}=\frac{\cos\gamma^{2}}{\gamma^{2}},\\ a_{21}=\frac{(r^{2}D_{r}^{2}+D^{2})[1-r^{2}(V^{2})^{2}]\cos\gamma^{2}+D^{2}\cos \gamma^{2}}{(r^{2})^{3}(D^{2})^{2}(V^{2})^{2}},\\ a_{22}=\frac{[1-r^{2}(V^{2})^{2}]\sin\gamma^{2}}{(r^{2})^{2}D^{2}(V^{2})^{2}} \end{array},\]  
where  
\[D_{r}^{Z}=\left.\frac{\partial D}{\partial z}\right|_{x=x^{z}}=-\frac{R_{M}D^ {z}}{h_{S}} \tag{19}\]  
To ensure the validity of linearization, a trust-region constraint is used,  
\[|\mathbf{x}-\mathbf{x}^{z}|\leq\delta \tag{20}\]  
where \(\delta\) is the constrained boundary and set in accordance with the magnitudes of the elements in \(\mathbf{x}\).
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 2 A Posteriori Error Estimation Via the Dual Weighted Residual Method
### The Error Estimate  
To estimate the error \(e^{\mathcal{J}}=\mathcal{J}(u)-\mathcal{J}(u_{h})\) in the QoI, see Equation (2), one uses the linearised adjoint problem for \(\mathcal{J}(u)\): Find \(z\in V\), resp. \(z_{h}\in V_{h}\), such that  
\[A^{\prime}(u;v,z)=\mathcal{J}^{\prime}(u;v),\quad\forall\ v\in U,\quad\text{ resp.}\quad A^{\prime}(u_{h};v_{h},z_{h})=\mathcal{J}^{\prime}(u_{h};v_{h}),\quad \forall\ v_{h}\in U_{h}, \tag{5}\]  
where \(A^{\prime}\) and \(\mathcal{J}^{\prime}\) are the Gateaux derivatives of \(A\) and \(\mathcal{J}\) with respect to \(u\) in direction \(v\). The adjoint problem is a terminal value problem (IVP backwards in time) with \(z(t_{e})=0\).  
An approximation of the error in the QoI is given by  
\[e^{\mathcal{J}}\lesssim A(u_{h},z-z_{h})-F(z-z_{h}),\]with equality for linear functionals and approximate upper bounds otherwise. Using an approximation \(z_{h}^{+}\approx z\), which is of higher accuracy than \(z_{h}\), using for example, higher order interpolation or a discrete solution on a finer grid [20], one gets a computable estimate  
\[e^{\mathcal{J}}\lesssim\eta(u_{h}):=A(u_{h},z_{h}^{+}-z_{h})-F(z_{h}^{+}-z_{h}). \tag{6}\]  
This can be further bounded by decomposing it by timesteps and thus giving a guide on where and how to adapt. For this to work, it is imperative that the solutions of the primal and adjoint problems are obtained at all points. This can cause storage problems for long time simulations and can be dealt with using check-pointing [21].
## 2 Literature Review and Research Objectives
### Flood Resilience Assessment  
Resilience plays a significant role in flood risk reduction and management. Several methods can be applied to assess flood resilience. For instance, in the United States, a study by [PERSON] and [PERSON] [35] proposed a conceptual framework to measure the resilience of flood-prone communities by considering the following three dimensions: hazard absorption capacity, resource availability, and community processes and resource utilization. According to their large-scale analysis, [PERSON] et al. [36] developed a flood resilience measurement tool piloted in nine countries by considering the following five capital dimensions: human, social, physical, natural, and financial capital. In Iran, [PERSON] et al. [37] applied a multi-criteria approach (the AHP-TOPSIS tool) to assess urban flood resilience under six dimensions: social, economic, environmental, infrastructural, institutional, and local. Despite their relevance, there is a shortage of research exploring the link between UNDRR indicators and flood disaster risk management. Therefore, this study assesses flood resilience by applying the proposed modified UNDRR indicators.  
Although Thailand is recognized as being highly vulnerable to climate variability, an updated hazard map (i.e., mapping and floodplain management) is still lacking. Comprehensive flood risk management and disaster preparedness remain extremely challenging. Furthermore, there is a dearth of research on how local authorities consider the introduction of flood resilience and protection measures from the perspective of their local flood risk management policies. There is also a clear need to strengthen and build resilience on DRR in Thailand to effectively tackle both natural disasters and the impacts of climate change.
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts
### The Relevance of Cyber Resilience in Various Segments
#### 3.2.4 Transport  
In the transport sector, while advances in information technology and interconnectivity have improved the efficiency of the transport infrastructure, they have also created greater risk associated with cyber systems [46]. With the advance of new technologies and applications, such as wireless technologies, mobile applications, cloud computing, artificial intelligence, the internet of things, etc., cyber security is becoming increasingly important, making it essential to build an environment conducive to increasing cyber resilience at all levels and reacting to cyber-attacks when they occur [47].Even in maritime navigation, cyber resilience has been a growing concern, where studies have been carried out to examine the properties of cyber resilience from three elements: combining information from multiple sensors, diagnosing non-normal behavior and detecting changes [48].
# The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning
## 4 Result and Discussion
### Performance of SPP
#### 4.1.2 Dual-Frequency
To further analyse the effect of the TGD or DCB on dual-frequency SPP, the RMS in BDS-3 dual-frequency SPP performance with different schemes is shown in Table 5. Moreover, Table 5 also shows the improvement of the three components in the \"tqd-corr\" and \"dcb-corr\" schemes compared with the uncorrected TGD and DCB scheme. Combined with Figures 15-19 and Table 5, correction of TGD and DCB can significantly improve positioning accuracy. Besides, compared with the TGD and DCB unmodified scheme, the positioning errors distribution of the other two schemes is more reasonable. Compared with Tables 4 and 5, it is also possible to obtain that single-frequency positioning accuracy is better than dual-frequency. We can explain it by the explanation mentioned before that the observation noise of dual-frequency LC is obviously greater. Nevertheless, the positioning accuracy of B1I/B3I and B1C/B2A SPP can reach 1 to 2 m horizontally and 3 to 4 m vertically. Consequently, this further supports the conclusions we reached before, which is the positioning of the BDS-3 needs to modify the TGD and DCB parameters, except for the B3I signal. To further support our conclusions, we have given the pseudorange residual for the BRECH station with different schemes in Figure 20. By comparing the pseudorange residuals of the different schemes in the graph, in the \"non-corr\" scheme, each satellite has a large systematic bias. However, the other two schemes have relatively small systematic bias. Therefore, this further validates that the TGD and DCB parameters of BDS-3 signals (except B3I signal) that need to be modified. Figure 13: Pseudorange residual scatters of B1I, B3I, B1C and B2a SPP with different schemes at BRECH station. Figure 14: The visible BDS-3 satellites number and PDOP of the six selected stations on DOY 92, 2020. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{3}{c}{**B1I/B3I**} & \multicolumn{3}{c}{**B1C/B2a**} \\ \cline{2-7}  & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline dcb-corr & 1.33 & 1.75 & 2.83 & 1.35 & 1.81 & 2.91 \\ tgd-corr & 1.26 & 1.76 & 2.92 & 1.64 & 2.06 & 3.28 \\ non-corr & 3.84 & 5.83 & 6.75 & 5.28 & 5.17 & 6.17 \\ Improvement (\%) & 65.4 & 70.0 & 58.1 & 74.4 & 65.0 & 52.8 \\ (dcb-corr/non-corr) & 67.2 & 69.8 & 56.7 & 68.9 & 60.2 & 46.8 \\ Improvement (\%) & & & & & & \\ (tgd-corr/non-corr) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMS in BDS-3 dual-frequency SPP performance with different schemes (m). Figure 16: Vertical positioning error scatters with and without TGD or DCB correction of B1I/B3I, SPP at six stations. Figure 15: Horizontal positioning error scatters with and without TGD or DCB correction of B1I/B3I, SPP at six stations. Figure 17: Horizontal positioning error scatters with and without TGD or DCB correction of BIC/B2a-SPP at six stations. Figure 18: Vertical positioning error scatters with and without TGD or DCB correction of BIC/B2a-SPP at six stations.
# Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
## 3 Game Design
### Scheduling Algorithm
The convergence of the algorithm when faced with cycling can be thought of as an early termination of the scheduling process which provides a feasible but incomplete schedule. Hence, the impact of cycling can be considered trivial as such phenomenon leads to low global and local utility due to \"incompleteness\" in the scheduling process where agents are unlikely to prefer such collective action. Generally, cycling tends to occur when the number of agents considered for the problem is too low when compared to the hold period and therefore, proper selection of hold period will minimize the occurrence of cycling. Using the proposed game model and scheduling algorithm for a game with spatial and temporal coupled constraints, the game will always converge to a feasible solution where all the coupled constraints are satisfied. We prove by contradiction. Consider a game with both spatial and temporal coupled constraint which converged to an infeasible solution where agent \(i\) selected a path \(a_{i}^{t}\) which contains \(k_{j}\) that violates \(y\) spatial coupled constraints. We also know that there exists a null action which is always feasible. If \(a_{-i}^{*}\) violates \(x\in\mathbb{R}^{+}\) number of spatial coupled constraints, then  
\[u^{k_{j}}(a_{i}^{t},a_{-i}^{*})=-G(x+y),\quad u^{k_{j}}(0,a_{-i}^ {*})=-Gx \tag{16}\] \[\mu_{i}^{k_{j}}(a_{i}^{t},a_{-i}^{*})=-Gy<\mu_{i}^{k_{j}}(0,a_{-i }^{*})=0\] (17) \[u_{i}(a_{i}^{t},a_{-i}^{*})<u_{i}(0,a_{-i}^{*})\] (18) \[a_{i}^{t}\
eq\arg\max_{a_{i}\in\{a_{j}^{t},0\}}u_{i}(a_{i},a_{-i }^{*}) \tag{19}\]  
\(a_{i}^{t}\) is not the argument which maximizes the local utility of agent \(i\) and thus, \(a_{i}^{t}\) cannot be the preferred action at equilibrium. Explicit consideration on temporal constraint violations in the proof is not necessary as the scheduling algorithm will always propose a temporally feasible schedule. Since violations in the solution are determined by the assignments rather than allocations, any task in any path that do not meet the temporal requirements will not be assigned. Therefore, it is only necessary to show that agents will never choose an action that violates spatial constraints given current observations to prove the feasibility of the solution.