esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 2 Research Background
### Energy  
The hierarchical, centrally controlled energy grid does not fit the needs of Smart Cities. Smart Grids increase throughput and efficiency by optimizing demand, energy and network availability. In order to provide reliable and real time monitoring information that enables a flexible operation, Smart Grids require Security, Quality of Service (QoS) in data transmission networks and Technology Standards for interoperability [31] where ICT plays a fundamental element in its growth and performance. A reliable and fast communication infrastructure [32] is necessary for the connection among the great number of distributed elements, such as generators, substations, distribution, transmission, storage and users [33]. Smart Grids can also be included into frameworks that seek a return of investment between producers, operators and customers; these frameworks are divided into three interactive smart components [34]: smart control centers, smart transmission networks, and smart substations. The key challenges for Smart Grids include the communications interoperability between the Smart Grid and the Smart Meter and the communications network reliability, availability, latency and QoS in wireless environments subject to Denial of Service (DoS) attacks [35]. The standardization effort to harmonize communications standards and protocols is coordinated through Europe, US and China [36]. A survey of Smart Grids [37] consists on its three major systems: smart infrastructure, management and protection. The survey covers the smart energy, information, security and communication subsystems in addition to management objectives such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission.  
In addition to Smart Cities, the enabler ICT also requires an increasing amount of dispersed energy for its data centres and communications transmission equipment. Energy Packet Networks (EPN) uses energy storage units to adapt to the irregular supply of renewable energy sources and intermittent demand of cloud computing servers [38]. The EPN model stores and distributes quantised energy units, or Energy Packets, to and from a large range of devices based on mechanisms analogue to computer networks [39]. Energy Packets are managed and optimized by energy dispatching centres which receive requests from consumer's storage centres that wish to be replenished [40].
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O\({}_{2}\)-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O\({}_{2}\)-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E\({}_{790}\)/E\({}_{660}\)). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O\({}_{2}\)-A band.  
solar-induced chlorophyll fluorescence (SIF); tower-based spectral observation; atmospheric correction; O\({}_{2}\)-A band; look-up table (LUT) 2019
## 4 Results and Discussion
### Test Systems
#### 4.2.5 Test System 5  
The first-order plus time delay model is selected for testing, and the model transfer function is shown in (24). The mapping range of the activation function is set to 0 to 30. Table 8 shows the mean value and standard deviation of 10 runs. Table 9 and Figure 12 illustrate the performance characteristics and step response outputs represented by one of the results, while Figure 13 displays the 10-time average convergence characteristics of several algorithms.  
\[G(  
As shown in Table 8, compared to the control scheme using GWO and PID, the control scheme using ALGWO-BPNN-PID reduces the fitness from 27.0366 to 23.8964, improving the average accuracy. Between them, the activation function of \(t(x)\) achieves the best effect, with the lowest average fitness of 23.8964. As can be seen from Table 9, using the ALGWO-BPNN-PID control scheme causes, the overshoot to decrease from 0.18% to 0.00%, the rise time to increase by about 5s, and the adjustment time to decrease by about 8s. Between the schemes, the control scheme using the \(g(x)\) activation function has a better overall effect.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Overshoot (\%)** & **Rising Time (s)** & **Settling Time (s)** & **Fitness** \\ \hline GWO and \(g(x)\) & 0.06 & 34 & 41 & 24.2989 \\ GWO and \(t(x)\) & 0 & 38 & 49 & 25.0178 \\ GWO and \(h(x)\) & 0 & 35 & 44 & 24.7274 \\ GWO and PID & 0.18 & 41 & 52 & 26.9809 \\ ALGWO and \(g(x)\) & 0 & 35 & 45 & 24.2034 \\ ALGWO and \(t(x)\) & 0 & 34 & 44 & 23.9817 \\ ALGWO and \(h(x)\) & 0 & 30 & 36 & 24.2842 \\ ALGWO and PID & 0.11 & 41 & 53 & 26.9800 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance characteristics on test system 5.  
Figure 12: The step response results on test system 5. (**a**) The simulation results of BPNN-PID using \(g(x)\) and (**b**) simulation results of BPNN-PID using \(t(x)\) and (**c**) simulation results of BPNN-PID using \(h(x)\) and (**d**) simulation results of traditional PID.  
As can be clearly seen in Figure 12, the response speed of the GWO-PID control scheme is slower, reaching 0.98 in about 52 seconds, while the BPNN-PID control scheme is advanced to about 45 seconds. As can be seen from Figure 13, the ALGWO-BPNN-PID control scheme has a faster convergence speed and better optimization accuracy, with a decrease of about two. Therefore, the ALGWO-BPNN-PID control scheme has more advantages in the first-order plus time-delay model.
## 4 Discussion  
In this paper, we have analysed the association between trackwork and train delays, employing two distinct types of regression models: multiple logistic regression and negative binomial regression. These models provide a comprehensive understanding of the impact of trackwork on train delays. The logistic regression model sheds light on the probability of delay occurrences, while the negative binomial regression offers insights into the frequency of these delays.  
Our study concludes that trackwork is linked to an increased rate of delay occurrences and a higher probability of delay increase. Trains passing through sections with scheduled trackwork are 1.43 times more likely to experience an increase in running time delay (\(\geq\)1 min). Simultaneously, there is a 16% increase in the expected count of instances where train delays increase by at least one minute, compared to scenarios without trackwork. Conversely, the opportunity for train delay recovery diminishes in the presence of trackwork. The frequency of delay reduction decreases by 4%, and the likelihood of a delay decrease is 11% lower than when there is no trackwork. The sensitivity analysis regarding the size of the delay reveals a more pronounced effect for delays between 1 and 10 min, while the impact of trackwork on delays exceeding 10 min is insignificant. This indicates that trackwork primarily contributes to smaller, more frequent delays.  
Although the negative impact of scheduled trackwork on train punctuality is relatively minor, primarily causing smaller delays (1-10 min), it still affects the reliability of railway operations. This effect might be mitigated by providing sufficient time for the trackwork to be completed and ensuring on-time performance. One strategy for achieving this is through the use of \"maintenance windows\", which involve reserving capacity for trackwork in advance of the completion of the train timetable. This allows train paths to adapt to capacity restrictions ahead of time and avoid any negative impact on performance. However, it has been observed that this approach is not yet utilised to its full potential, and train operators may have difficulty adapting to the restrictions. Additionally, there may be uncertainty [37] in the trackwork schedule even close to the execution period, which can lead to changes in the schedule and difficulties for train operators to adapt, resulting in train cancellations.  
The trackwork scheduling approach used in this study is consistent with the SERA directive [32], which is widely adopted in European Union member states. Therefore, the findings of this study have broad relevance and demonstrate the need for increased attention to be given to trackwork scheduling.
## 3 Results and Discussion  
### Bulk Structural Properties  
As mentioned in Section 2.2, for the structural relaxations of CZTSe a two-step process has been employed. The starting point is the conventional kesterite unit cell of CZTSe, and for several unit cell volumes around the experimentally known ground state volume a full structural relaxation of the internal coordinates has been performed, thereby keeping the respective unit cell volumes fixed. In that way we obtain the total energy curves, i.e., the dependence of the calculated total energy on the various volumes, shown exemplary as black dots (green triangles) for the SCAN (hybrid HSE06) exchange and correlation functionals in Figure 2a.  
A further analysis of the total energy curves by means of [PERSON]'s equation of state (Equation (1)) leads to the ground state volume \(V_{0}\), and a final relaxation of the internal coordinates for \(V_{0}\) yields the lattice parameters for CZTSe, amounting to \(a=5.694\) A (\(a=5.693\) A) and \(c=11.351\) A (\(c=11.422\) A) for calculations based on the SCAN (hybrid HSE06) exchange and correlation functionals [22], in favourable agreement with experimental values [2], reported as \(a=5.693\) A and \(c=11.347\) A, respectively. The agreement with respect to the experimental unit cell volume is shown in Figure 2b, showing very good agreement for the SCAN functional calculation, and an overestimation of up to 2% for the hybrid HSE06 exchange and correlation functional [22].  
However, the main focus here is on the performance for the newly devised approach to speed up structural relaxations employing hybrid functionals, and the agreement with experimental results. As mentioned in Section 2.2, for each of the volumes of the SCAN full structural relaxations (black dots in Figure 2a) a single-shot HSE06 calculation has been performed based on the relaxed structural properties of the SCAN functional calculations. The corresponding total energy curve is shown by the red triangles in Figure 2a, and is shifted towards the full hybrid HSE06 results, respectively. A further analysis by [PERSON]'s equation of state (Equation (1)) reveals lattice constants of \(a=5.726\) A and \(c=11.413\) A, in favourable agreement with the results obtained for full structural relaxations employing the hybrid HSE06 exchange and correlation functional. In other words, employing the described two-step process of relaxing the structural properties using the computationally much cheaper SCAN exchange and correlation functional, followed by single-shot hybrid HSE06 calculations for each of the different unit cell volumes necessary to sample the total energy curve, yields structural properties in favourable agreement with full structural relaxations based on the hybrid HSE06 functional. This approach can now be applied to problems where a full hybrid functional structural relaxation has not been possible up to now, either due to the size of the unit cell or simply the number of necessary structural relaxations.
# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 1 Introduction
Initial approaches on the automation of dicentric chromosomes focused on segmentation techniques [19; 20], whereas more recent studies have achieved high precision and accuracy levels using convolutional neural networks [14; 21; 22; 23]. The aim of this work is to identify dicentric chromosomes in the images of metaphase cells by developing an artificial intelligence software based on deep learning and computer vision for distinguishing between damaged (dicentric chromosomes) and undamaged chromosomes (monocentric). Specifically, an ensemble of detectors is proposed in this work. An ensemble aims to merge the results of the models that compose it, and this strategy has been widely used in earlier studies [24; 25; 26; 27]. The decision made by consensus is likely to be more accurate than decisions made by individual models, since the consensus function used by the ensemble can correct some classification errors, provided the results from the individual models are accurate. The remainder of this paper is organized as follows. Section 2 describes the methodology proposed in this work. Then, the experiments that have been carried out are described in Section 3. Finally, Section 4 details the conclusions of this work.
## 4 VSG Applications of Power Electronics-Based Units  
Unlike the theoretical modeling of VSG control, which assumes DC energy to be infinite and constant, practical applications must consider the available generation/storage ability, which is constrained by the status of the system. Once these are addressed, the VSG control strategy can be implemented in applications such as wind turbines, PV panels, energy storage, bidirectional electric vehicle chargers, and flexible loads.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 3 Simulations
### Simulation Results
#### 3.2.2 Robustness  
The noise problem of scanning acoustic microscopy is more prominent than traditional ultrasonic testing. In order to test the robustness of SR and CD-SR, zero-mean Gaussian white noise was added to the AMI signal to construct AMI signals with different signal-to-noise ratios. Figure 8a shows \(x_{1}(t)\) and \(x_{2}(t)\) without noise. After adding noise, as shown in Figure 8b,c, the signal-to-noise ratio is 0 and \(-15\) dB, respectively.  
The comparison results are shown in Figure 9. Compared with SR, CD-SR has smaller amplitude error and position error in reconstructing echoes, and is more robust for cases with poorer signal-to-noise ratio. In the case of a signal-to-noise ratio of \(-15\) dB, the reconstructed echoes are shown in Figure 10. The waveforms obtained by CD-SR are still consistent with the actual waveforms (see Figure 8a), but SR can no longer successfully separate the echoes.  
Figure 6: Comparison of errors (Red: constraint dictionary-based sparse representation (CD-SR), Black: sparse representation (SR)).  
Figure 8: AMI signals with different signal-to-noise ratios (SNR): (**a**) SNR \(=+\infty\); (**b**) SNR \(=0\) dB; (**c**) SNR \(=-15\) dB.  
Figure 9: Comparison of errors (Red: CD-SR, Black: SR).
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 2 Preliminaries
### Quantum Annealing and Ising Models  
Quantum annealing is a metaheuristic for solving combinatorial optimization problems, where it tries to find a value \(+1\) or \(-1\) for each of the Ising variables \(\mathbf{s}=(s_{1},s_{2},\ldots,s_{N})\) such that the following Hamiltonian of the Ising models gives the lowest energy state:  
\[H_{P}(\mathbf{s})=\sum_{i=1}^{N}h_{i}s_{i}+\sum_{i<j}l_{i,j}s_{i}s_{j}, \tag{1}\]  
where \(h_{i}\) is the magnetic field coefficient at site \(s_{i}\), and \(l_{i,j}\) is the interaction coefficient between \(s_{i}\) and \(s_{j}\). Ising variables correspond to discrete variables in the metaheuristic. The lowest energy state, called the _ground state_ of \(H_{P}\), provides the optimal solution. Figure 1 depicts a two-dimensional array Ising model. A circle represents a spin and is connected to four neighbor spins. The arrows \(\uparrow\) and \(\downarrow\) represent the \(up\) spin (\(+1\)) and _down_ spin \((-1)\), respectively.  
Figure 1: 2D Array Ising Model.  
A Hamiltonian may include an objective function, where the lower energy in the terms of the function contributes to a smaller objective value in minimization problems. For maximization problems, we reverse the sign of the Ising model parameters for the objective function and then minimize it. Constraints in optimization problems should also be composed of sets of terms in the Hamiltonian. The lowest energy state of the terms leads to the satisfaction of the corresponding constraints. Therefore, the annealing process needs to obtain feasible solutions that satisfy all constraints with a good quality of the objective value by exploring the lowest energy state of the total Hamiltonian. Note that we can easily replace the type of decision variables from \(\{+1,-1\}\) to \(\{0,1\}\), and vice versa.  
The mechanism of the quantum annealing process is explained well in [2; 4; 13]. The following paragraph gives only a brief introduction to the optimization process based on quantum annealing.  
An Ising model Hamiltonian, whose ground state encodes the solution of the target optimization problem, can be solved using adiabatic quantum optimization. First, we prepare the following time-dependent Hamiltonian:  
\[H(t)=(1-\frac{t}{T})H_{0}+\frac{t}{T}H_{P}, \tag{2}\]  
where \(H_{P}\) is the Hamiltonian corresponding to the target optimization problem as shown in Equation (1); \(H_{0}\) is another Hamiltonian whose ground state is easily set up in the computing platform. The Hamiltonian varies from \(H(0)=H_{0}\) to \(H(T)=H_{P}\) according to the time progress from \(t=0\) to \(t=T\). The adiabatic theorem states that if \(H(0)\) is the ground state, then \(H(T)\) is also the state under a sufficiently slow change of parameters in the Hamiltonian during the time progress. That is, the solution of the combinatorial optimization is obtained at \(H(T)\).
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 4 Analysis of Constellation Collision Maneuver  
Starlink satellites can take evasive maneuver measures on the basis of collision early warning based on the cataloged object orbit data provided by the US Space Surveillance Network, but because the thrust provided by ion thruster is small, the fuel or working medium that the satellite can carry is limited, and the operation time of each avoidance maneuver is longer than several minutes. A too-high maneuvering frequency or long-term altitude changes will lead to a constellation configuration change. Therefore, it is necessary to selectively avoid possible collisions. When formulating the avoidance strategy in the design stage of the satellite, the accepted collision probability level (ACPL) is usually  
Figure 5: Flux of objects on satellites with different orbital parameters with objects \(\geq\)6 cm.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Phase** & \multicolumn{3}{c}{**Single Satellite**} & \multicolumn{3}{c}{**All Satellites**} \\ \hline  & \(P_{\text{S3}^{*}}\) & \(P_{\text{S3},2^{*}}\) & \(P_{\text{P}0^{*}}\) & \(P_{\text{P}7,6^{*}}\) & \(P_{\text{S3}^{*}}\) & \(P_{\text{S3},2^{*}}\) & \(P_{\text{P}0^{*}}\) & \(P_{\text{P}7,6^{*}}\) \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & 1.89 & 1.72 & 1.06 & 0.88 \\
2 & 0.06 & 0.07 & 0.10 & 0.12 & 63.36 & 66.25 & 50.88 & 44.49 \\
3 & 0.01 & 0.01 & 0.01 & 0.01 & 6.83 & 6.69 & 4.21 & 3.32 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Probability of collision between satellites in different orbital layers and objects \(\geq\)6 cm.  
used as the evaluation criterion to measure whether to take evasive measures or not. For satellites operating in LEO orbits, the ACPL values used in the design are between \(10^{-4}\) and \(10^{-5}\)[17].  
The DRAMA software [18] developed by the ESA is used to analyze the avoidance maneuver times and collision risk of 1628 Starlink satellites deployed in phase i. The cross-section of the satellite is 25.2 m\({}^{2}\), the equivalent radius is 2.667 m, and the collision risk of objects of 6 cm and above is considered.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 4 Discussion
### ORA Airflow Pressure  
Little is known of the temporal and spatial characteristics of the ORA airflow. Using a fast response pressure sensor with a micro-aperture and an opening of 0.25 mm, [PERSON] [25] investigated the vertical and horizontal spatial pressure profile of the ORA. Regarding the spatial extent of the airflow, 50% of the maximum airflow was found to be within 2.9 mm with a CoV of 3%. Furthermore, the repeatability of the absolute airflow pressure within the central 3 mm zone was high (CoV 5%). In a separate study the duration of the airflow was reported to be approximately 20 ms, with a symmetrical profile for increase and decrease edges [4]. The findings in the present study are in concordance with [PERSON]'s observations. However, the present investigation provides an in-depth understanding of the 3D dimension of the ORA airflow pressure.
## 5 Research on Optimization Design of ILVT Structure
### Scheme Optimization Design
#### 5.2.2 Width Optimization  
Similar to the sizes considered for the optimization of the length of the track bed slab, the optimization of the width of the track bed slab was attempted by considering six different sizes increment as follows: 20 mm, 40 mm, 60 mm, 80 mm, 100 mm and 120 mm. By increasing the track bed slab width by 100 mm (Figure 33), the track slab length becomes 6580 mm, and width becomes 3000 mm. The stress of T1, T2 and T3 at the loading points of No. 8 and No. 11 of six sizes were calculated (Tables 13 and 14).  
It can be seen that widening the width of the track bed will affect the end and internal force performance of the track bed at the same time. As the width of the track bed increases, the stress gradually decreases. When the length increases to 40 mm, the change tends to be gentle. At this time, the stress at T1 and T2 drops to 77% and 85% of the original size, and the stress at T3 changes less. At the load point of No. 11, the stress at T1 position gradually decreases with the increase in the width of the track bed slab, and the stress at other positions changes little.
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 4 Experiment and Analysis  
The experiment was conducted using the Flink stream distributed computing framework, with Java as the programming language. The input variables for the experiment were pH value, NH4N, and turbidity. As the dataset had a low dimension, single-machine three-thread parallel computing was utilized to simulate distributed computing. Cluster analysis was performed using the proposed stream-DBSCAN model in Figure 4, and the reservoir water quality was analyzed and predicted based on the cluster results.